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20 Manufacturing Case Studies [2024]

In this collection of 20 manufacturing case studies, we explore a variety of industries embracing technological innovation, sustainability, and efficiency to tackle modern challenges. These examples showcase how global leaders have successfully implemented groundbreaking strategies to enhance their processes, reduce environmental impact, and remain competitive in the ever-evolving market landscape.

20 Manufacturing Case Studies

Case study 1: ford motor company – implementation of 3d printing.

Company Overview:  Ford Motor Company, a giant in the automotive industry, has been exploring cutting-edge technologies to better its manufacturing procedure and development.  

Challenges:

1. Need for faster prototyping to accelerate product development.

2. Reduction of waste and costs in the manufacturing procedure.  

Solutions Implemented:  Ford integrated 3D printing technology into its manufacturing and prototyping processes. This technology allows for quickly creating parts and tools at a significantly lower cost than traditional methods.  

Results:  3D printing enabled Ford to drastically reduce the turnaround time for prototyping from months to days, accelerating the overall time to market for new vehicle designs. This approach also minimized waste and reduced the costs associated with manufacturing prototypes and certain parts, enhancing sustainability and efficiency.

Case Study 2: Patagonia – Leading Sustainability in Textile Manufacturing

Company Overview:  Patagonia, a prominent clothing label, is celebrated for its commitment to ethical manufacturing and sustainable practices.  

1. High environmental impact of traditional textile production processes.

2. Client demand for sustainable and ethically created items.  

Solutions Implemented:  Patagonia adopted organic cotton and recycled materials for its products, emphasizing sustainability throughout its supply chain. The company also implemented the Worn Wear program, encouraging consumers to repair and reuse their gear instead of buying new.  

Results:  These initiatives have significantly reduced the company’s carbon footprint and water usage, aligning with its mission to cause no unnecessary harm. Patagonia’s commitment to sustainability has improved its brand, drawing an increasing number of environmentally aware consumers.

Case Study 3: Siemens AG – Digital Factory Initiatives

Company Overview:  Siemens AG, a global electronics and electrical engineering leader, operates across the industry, power, and healthcare sectors. The company is at the forefront of industrial automation and digitalization.  

1. Increasing production efficiency and flexibility in a rapidly changing technological landscape is necessary.

2. Reducing the carbon footprint of manufacturing processes.  

Solutions Implemented:  Siemens launched its Digital Factory initiatives, which include using digital twins, automation with AI, and integration of IoT devices across its manufacturing operations. These technologies enable virtual testing and optimization before physical processes occur.  

Results:  Implementing these digital solutions has significantly enhanced the efficiency and precision of Siemens’ manufacturing methods. The digital twin technology, in particular, has led to a 30% decrease in time-to-market for new products, improved system performance, and reduced energy consumption across various facilities.

Related: Career in Manufacturing vs Service Industry

Case Study 4: Toyota Motor Corporation – Sustainable Plant Initiatives

Company Overview:  Toyota Motor Corporation is known for its innovative approaches to manufacturing and is a pioneer in producing energy-efficient vehicles, including the Prius hybrid.  

1. Maintaining leadership in automotive innovation while lessening the environmental influence of manufacturing processes.

2. Implementing measures to conserve water, reduce waste, and cut energy use.  

Solutions Implemented:  Toyota has invested heavily in building sustainable plants. One of its notable initiatives is the use of bioenergy and hydrogen fuel cells for energy. The company has also established comprehensive recycling programs and extensively utilizes solar panels across its plants.

Results:  These sustainability efforts have allowed Toyota to reduce its waste and energy consumption drastically. Using renewable energy sources in its plants has significantly cut carbon emissions. Toyota’s commitment to sustainability has improved its environmental footprint and positioned it as a leader in sustainable manufacturing practices.

Case Study 5: Boeing – Lean Manufacturing and Automation

Company Overview:  Boeing, one of the largest aerospace companies globally, manufactures commercial jetliners as well as defense, space, and security systems. Boeing is known for its high standards in manufacturing efficiency and innovation.  

1. There is a need for high efficiency and precision in aircraft production, which are complex assemblies with tens of thousands of parts.

2. Minimizing production time and cost while maintaining the highest levels of quality and safety.  

Solutions Implemented:  Boeing has embraced lean manufacturing techniques and advanced automation in its production lines. This includes using automated guided vehicles (AGVs) to move parts within the factory, automated drilling and riveting systems, and advanced robotics for assembly processes.  

Results:  The adoption of these technologies and practices has led to significant improvements in Boeing’s production efficiency. Automation has reduced the time required for assembly processes by about 25%, and lean manufacturing techniques have minimized waste and optimized the use of resources across Boeing’s production facilities. Additionally, these advancements have contributed to maintaining high safety and quality standards, which are critical in aerospace manufacturing.

Case Study 6: Intel Corporation – Advanced Chip Manufacturing

Company Overview:  Intel, a semiconductor industry leader, manufactures microprocessors central to many computing devices. The company is known for its technological innovations and large-scale manufacturing capabilities.  

1. The need to continually push the boundaries of chip performance and energy efficiency.

2. Managing the complexities and cost of ultra-precision manufacturing processes.  

Solutions Implemented:  Intel has pioneered the development of next-generation microprocessors using advanced manufacturing techniques, including photolithography and 3D transistor technology. The company invested heavily in state-of-the-art fabrication facilities with clean rooms and automated assembly lines to control the manufacturing environment precisely.  

Results:  These investments and innovations have allowed Intel to produce reduced, more powerful, and energy-efficient chips. This progress has kept Intel at the forefront of the semiconductor industry, maintaining its competitive edge and responding effectively to the rapidly evolving demands of global markets.

Related: High Paying Jobs in Manufacturing Industry

Case Study 7: BASF SE – Resource-Efficient Chemical Production

Company Overview:  BASF, one of the world’s leading chemical companies, is involved in various products, including chemicals, plastics, implementation products, farming solutions, and oil and gas.  

1. Reducing energy consumption and environmental impact in chemical production traditionally involves high energy and resource usage.

2. Ensuring sustainable growth and compliance with increasingly stringent global environmental regulations.  

Solutions Implemented:  BASF has implemented a “Verbund” system in its operations, integrating energy and material flows across different production units to maximize efficiency and minimize waste. The company also uses advanced catalysts and other innovative technologies to enhance the efficiency of chemical reactions, reducing the need for energy and raw materials.  

Results:  The Verbund system has significantly reduced energy use and greenhouse gas emissions. BASF has reported that this system saves the company approximately 19 million MWh of energy annually, equivalent to the energy consumption of a small city. These efforts have improved BASF’s environmental footprint and strengthened its position as a leader in sustainable chemical production.

Case Study 8: Coca-Cola – Water Replenishment and Recycling Initiatives

Company Overview:  Coca-Cola, one of the largest beverage companies globally, produces and distributes a variety of soft drinks and other beverages. Given the scale of its operations, sustainable water use is a critical focus for the company.  

1. Significant water usage in beverage production, coupled with growing environmental concerns.

2. The need to balance high production volumes with sustainability commitments.  

Solutions Implemented:  Coca-Cola launched comprehensive water stewardship initiatives, including advanced water treatment and recycling technologies to ensure water used in manufacturing processes is returned to the environment safely. The company also invested in community-based partnerships to replenish water in stressed areas.

Results:  Through these initiatives, Coca-Cola has achieved its goal of replenishing 100% of the water it uses in its finished products. This milestone underscores its commitment to sustainable water use. These efforts have helped mitigate the environmental impact, improved community relations, and supported regulatory compliance.

Case Study 9: Caterpillar – Automation and Connectivity in Machinery Manufacturing

Company Overview:  Caterpillar is a leading construction and mining equipment manufacturer known for its heavy machinery and engines. The company is focused on enhancing product quality and operational efficiency.  

1. High variability in product demand requires flexible manufacturing processes.

2. The must improve production efficiency and reduce operational costs.

  Solutions Implemented:  Caterpillar has embraced Industry 4.0 technologies, incorporating automation, machine learning, and IoT connectivity in its manufacturing operations. This includes using autonomous robots for material handling and assembly, sensors, and data analytics to predict maintenance needs and optimize production schedules.  

Results:  The integration of these technologies has significantly increased Caterpillar’s manufacturing agility and efficiency. Automation has reduced labor costs and improved safety by taking over dangerous tasks previously handled by humans. Moreover, connectivity solutions have enabled real-time monitoring and adjustments, leading to better product quality and faster response times to market changes.

Related: Marketing for the Manufacturing Sector

Case Study 10: Airbus – Implementing Eco-Efficient Manufacturing

Company Overview:  Airbus is a global leader in aerospace and defense, known for its commercial aircraft, helicopters, military transports, and space systems. Sustainability and innovation are key pillars of its business strategy.  

1. The aerospace industry has a significant environmental footprint, particularly in carbon emissions and resource consumption.

2. Increasing regulatory and consumer pressure to reduce environmental impact.  

Solutions Implemented:  Airbus has implemented several eco-efficient manufacturing techniques, such as using lighter and more sustainable materials, including composites that reduce the overall weight of aircraft. The company has also integrated more efficient manufacturing processes that reduce waste and energy consumption.

Results:  These initiatives have enabled Airbus to decrease fuel consumption and CO2 emissions, contributing to more sustainable flight operations. The use of advanced materials and technologies has also reduced the lifecycle environmental impact of its products, helping Airbus meet its sustainability goals and regulatory requirements.

Case Study 11: Samsung Electronics – Smart Factory Solutions

Company Overview:  Samsung Electronics, a major global player in consumer electronics and semiconductor manufacturing, strives for high efficiency and innovation in its production processes.  

1. High competition in the electronics market requires rapid adaptation to changing consumer demands.

2. Need for high efficiency and precision in manufacturing small, complex electronics components.  

Solutions Implemented:  Samsung has developed and implemented “Smart Factory” solutions across its manufacturing facilities. These include automation, AI, and IoT technologies that streamline production processes, enhance quality control, and reduce production times. For instance, automated assembly lines and AI-driven defect detection systems have improved production yield and efficiency.  

Results:  The Smart Factory initiatives have significantly improved production speed and quality while reducing manufacturing costs. By minimizing human error and optimizing production workflows, Samsung has maintained its position as a leader in the highly competitive and rapidly evolving tech industry.

Case Study 12: Tata Steel – Advanced Manufacturing and Sustainability Initiatives

Company Overview:  Tata Steel, one of the world’s leading steel producers, is based in India and operates globally. The company is committed to innovation and sustainability in its operations.  

1. High energy usage and CO2 emissions associated with steel production.

2. Increasing global demand for sustainable building materials.  

Solutions Implemented:  Tata Steel has invested in cutting-edge technologies to enhance the efficiency of its production processes and reduce its environmental impact. These include using electric arc furnaces powered by renewable energy and advanced smelting techniques that significantly lower CO2 emissions. The company also focuses on recycling scrap steel, decreasing the requirement for raw materials, and minimizing waste.  

Results:  These sustainable practices have significantly reduced Tata Steel’s carbon footprint while maintaining production efficiency. Adopting electric arc furnaces and recycling initiatives has positioned Tata Steel as a leader in sustainable steel manufacturing, appealing to environmentally conscious consumers and businesses.

Related: How to Start a Career in the Manufacturing Industry?

Case Study 13: Pfizer – Digital Integration in Pharmaceutical Manufacturing

Company Overview:  Pfizer is a global pharmaceutical giant known for its research and development in medicine. The company focuses on innovation to improve healthcare outcomes worldwide.  

1. The need for stringent quality control and efficiency in the production of pharmaceuticals.

2. Rapid scaling of production capacities, especially highlighted by the COVID-19 vaccine rollout.  

Solutions Implemented:  Pfizer has embraced digital integration within its manufacturing processes, employing technologies such as data analytics, AI, and IoT to monitor production quality and streamline operations. For instance, during the production of the COVID-19 vaccine, Pfizer utilized advanced data systems to optimize the manufacturing and distribution process, ensuring rapid delivery and high-quality standards.  

Results:  The integration of these digital technologies enabled Pfizer to enhance its manufacturing agility and quality control, dramatically speeding up the production and distribution of the COVID-19 vaccine. This approach helped meet global demand swiftly and ensured that the vaccines distributed met the highest quality standards.

Case Study 14: H&M Group – Sustainable Textile Production

Company Overview:  H&M Group, a leading global fashion retailer, is committed to sustainable fashion, aiming to become fully circular and climate-positive.  

1. The fashion industry is among the largest polluters globally, primarily due to high water usage, chemical dyes, and textile waste.

2. Consumer need for sustainable and ethically produced fashion is growing.  

Solutions Implemented:  H&M has implemented several sustainability initiatives, such as increasing the use of recycled and sustainably sourced materials, introducing water-efficient dyeing processes, and setting up garment collecting programs in their stores to promote recycling.  

Results:  These measures have significantly reduced H&M’s environmental impact. The company has made strides towards using 100% recycled or sustainably sourced materials by 2030. Additionally, their garment collection initiative has helped recycle thousands of tonnes of fabric, preventing waste and promoting circular fashion.

Case Study 15: Tesla, Inc. – Automation and Innovation in Electric Vehicle Manufacturing

Company Overview:  Tesla, Inc. is a leader in electric vehicle (EV) and clean energy solutions, renowned for its innovative approach to automobile manufacturing and energy solutions.  

1. Scaling production to meet the demands for electric vehicles.

2. Reducing manufacturing costs to make EVs more accessible to a broader market.  

Solutions Implemented:  Tesla has pioneered high automation in its production lines, particularly in its Gigafactories, which integrates cutting-edge robotics and artificial intelligence to streamline manufacturing processes. Tesla has also extended its battery technology to drop costs and enhance automobile efficiency.  

Results:  Tesla’s high degree of automation has drastically reduced the time and cost associated with vehicle assembly, allowing the company to scale up production rapidly. Innovations such as introducing more efficient battery cells have not only enhanced vehicle performance but have also lowered the cost of EVs, facilitating broader market adoption.

Related: Digital Transformation in the Manufacturing Sector

Case Study 16: Procter & Gamble (P&G) – Zero Manufacturing Waste

Company Overview:  Procter & Gamble, a multinational consumer goods corporation, manufactures various products, including personal health/consumer health and hygiene products.  

1. Significant environmental impact due to waste generated from large-scale production processes.

2. Increasing regulatory and consumer pressure for sustainable practices.  

Solutions Implemented:  P&G initiated a zero manufacturing waste program to have no manufacturing waste go to landfills. The company optimized its resource use and improved its recycling, reuse, and conversion practices, including converting waste to energy.

Results:  The initiative eliminated manufacturing waste across all its global plants, significantly reducing P&G’s environmental footprint and operational costs. This accomplishment has further bolstered P&G’s standing as a leader in sustainable manufacturing.

Case Study 17: General Electric (GE) – Advanced Additive Manufacturing

Company Overview:  General Electric, a multinational conglomerate, functions in the power, renewable energy, aviation, and healthcare sectors. GE has been at the forefront of industrial innovation.  

1. Need to enhance product performance and reduce production costs in highly competitive sectors.

2. Accelerate the development cycle of complex products.  

Solutions Implemented:  GE has aggressively invested in additive manufacturing (3D printing), particularly for producing parts for its aviation and healthcare equipment. This technology allows for lighter, more efficient designs and drastically reduces material waste.  

Results:  Additive manufacturing has enabled GE to produce parts that are impossible to make with traditional methods, reducing the weight of some components by up to 80% and overall production times by 50%. This has led to cost savings and improved product performance, particularly in jet engines and medical imaging devices.

Case Study 18: IKEA – Sustainable Furniture Manufacturing

Company Overview:  IKEA, a global furniture and home accessories leader, is known for its affordable and innovative products. The company is committed to positive environmental practices.  

1. The environmental impact of logging and furniture manufacturing.

2. Consumer demand for sustainably sourced and produced furniture.  

Solutions Implemented:  IKEA has focused on using sustainable materials such as bamboo, recycled wood, and plastics. It has also implemented more efficient manufacturing processes to reduce waste and energy consumption, including flat-pack designs that optimize transport efficiency.

  Results:  These practices have significantly reduced IKEA’s carbon footprint and made its operations more sustainable. Using recycled materials has reduced waste and appealed to environmentally conscious consumers, enhancing IKEA’s market reach and brand loyalty.

Related: AI in Chip Manufacturing Use Cases

Case Study 19: Nestlé – Water Efficiency in Food Production

Company Overview:  Nestlé, one of the world’s largest food and beverage companies, has a broad portfolio that includes dairy products, coffee, water, and pet care.

  Challenges:

1. High water usage in food and beverage production.

2. Growing global pressure to adopt sustainable water management techniques.  

Solutions Implemented:  Nestlé has focused on water stewardship, implementing advanced water recycling and reduction techniques across its factories worldwide. The company has invested in technology that treats and reuses water from manufacturing processes.  

Results:  These measures have drastically reduced Nestlé’s water consumption per ton of product, helping the company achieve significant water savings and reducing its overall environmental impact. Nestlé’s commitment to water efficiency has improved its sustainability credentials and compliance with international environmental standards.

Case Study 20: John Deere – Precision Agriculture Manufacturing

Company Overview:  John Deere, a leading agricultural machinery manufacturer, also focuses on technology solutions to improve farm productivity and sustainability.  

1. Increasing demand for agricultural efficiency and sustainability.

2. Farmers need tools that reduce costs and increase crop yield.  

Solutions Implemented:  John Deere has incorporated advanced technologies into its equipment, such as GPS tracking, IoT connectivity, and data analytics, to facilitate precision farming. These technologies allow for better resource management, optimizing everything from seeding to harvesting.

Results:  Adopting these technologies has enabled farmers to significantly increase efficiency and reduce waste, leading to better yields and lower environmental impact. John Deere’s creations have cemented its leadership in the farming sector, proposing cutting-edge resolutions catering to current farmers’ requirements.

These 20 case studies demonstrate the transformative power of innovation in manufacturing across diverse sectors. By adopting advanced technologies and sustainable practices, these companies have optimized their operations and set new standards for environmental responsibility and operational excellence. Their success stories offer inspiration for future industry advancements.

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55 Process Improvement Case Studies & Project Results [2023]

Hi there! As a data analyst and AI professional, I wanted to share a comprehensive look at 55 process improvement case studies and their results. Optimizing business processes is essential for reducing costs, boosting quality, and improving customer satisfaction.

In this guide, we‘ll explore real-world examples of process improvement across industries. My goal is to provide you with detailed case studies so you can use them as benchmarks for your own projects. Let‘s dive in!

What Typical Results Should You Expect?

Before looking at individual cases, it‘s helpful to understand the most common benefits of process improvement:

• Increased Efficiency: Streamlining processes lets companies do more with less. For example, a manufacturer used process mining to cut invoice approval time and automated 75% of activities.
• Higher Quality: With smoother flows, there‘s less room for errors. A chemicals firm reduced defects by 70% after process optimization.
• Enhanced Customer Satisfaction: Faster throughput and fewer mistakes boost customer loyalty. An insurer‘s process work shortened response times and increased on-time case resolutions by 42%.
• Cost Savings: Eliminating waste and paperwork reduces expenses. A telecom achieved $2.3 million in annual savings through an RPA initiative.

Overview of 55 Process Improvement Case Studies

Manufacturing – Improving Production Processes

Manufacturers can leverage solutions like Lean Six Sigma to optimize production and supply chain processes.

For example, Lockheed Martin aimed to reduce its environmental footprint and costs. By focusing on eliminating non-value-added work, it decreased facility size by 33% and chemical storage to just 2% of original size. These lean initiatives saved enormously on real estate and hazardous waste expenditures.

Another manufacturing firm, 3M, recycled and reused waste materials rather than disposing them. This lean approach saved around $30 million annually in waste costs.

Across industries, remember that small efficiencies compound. 3M‘s CEO once stated: “Most of our products emerge from somebody’s idea about how to do something simpler, better, or cheaper.”

Healthcare – Boosting Patient Experience

In healthcare, accurate and timely processing of records is critical. Process improvement helps hospitals handle patient data more efficiently.

At University Hospitals Birmingham NHS Trust, automated self-check-in kiosks were implemented. This robotic process automation initiative reduced check-in time from 15 minutes to under 5 minutes. Patients could check-in quickly and accurately, improving their experience.

Meanwhile, BMC HealthNet Plan applied lean principles to claims processing. This increased claims processing accuracy from 93% to 99.7%. With fewer erroneous claims, patients received faster and more accurate service.

Banking – Accelerating Lending Decisions

Banks can struggle with long wait times for lending decisions. Process mining helps identify and eliminate inefficiencies in lending processes.

VTB Bank in Russia used process mining to cut application decision time from 14 days to 5 days. The percentage of timely handled loan applications also increased from 68% to 98%.

Another financial institution, BridgeLoan, reduced total loan processing time by 40% after process improvements. Applicants received faster decisions, improving customer satisfaction.

Across banking, optimizing processes around customer applications can significantly impact customer lifetime value.

Professional Services – Improving Delivery

Client work in professional services firms involves many interdependent processes. Consultancies like EY have applied RPA to coordinate projects better.

For EY, bots automated the process of reminding staff to book travel for upcoming client meetings. This initiative reduced manual effort by 50% while cutting airfare costs by 20% through early booking.

Meanwhile, enterprise giant Microsoft applied Six Sigma principles to sales and service processes. Fine-tuning these workflows reduced defects and enabled data-driven insights around optimal customer segmentation.

15 More Impactful Case Studies

Here are some additional examples that showcase process improvement results across sectors:

• Retail: Starbucks used lean principles to reduce customer waiting times in stores and speed up ordering.
• Energy: Synergy generated $2.3M in savings annually through RPA in its billing processes.
• Pharma: A global pharma optimized artwork management processes, improving coordination between countries.
• Construction: Afrisam implemented process mining for risk management, achieving real-time visibility.
• Food & Beverage: Kahiki Foods‘ lean program dropped production waste by 70% in months.
• Transport: Deutsche Bahn optimized rail freight through BPM, cutting logistical costs.
• Telecom: By mining processes, Nokia identified lead time improvements and training needs.
• Technology: Dell applied RPA in finance processes, saving $2M annually.
• Automotive: Mercedes Benz‘ kaizen approach reduced manufacturing time per vehicle by 10-20%.
• Aerospace: Lean initiatives helped Lockheed Martin consolidate facilities and slash chemical waste.
• Oil & Gas: Chevron‘s Six Sigma program targeted high-cost equipment failures, saving millions.
• Utilities : Alliander increased purchasing process efficiency with process mining.
• Insurance: Aegon‘s Lean Six Sigma initiative stopped £20M spend on external contractors.
• Media: The BBC sped up program preview creation through RPA bots.
• NGO: World Vision improved performance evaluation with optimized processes.

The data shows that process gains can have truly enterprise-wide benefits regardless of sector.

6 More In-Depth Case Studies

Let‘s look at a few case studies more closely to better understand how companies achieved process improvements:

1. AkzoNobel – Chemicals Company

• Initiative: Process mining to identify procurement inefficiencies
• Processes: P2P, AP, O2C
• Results: Cut manual work by 18%, improved supplier efficiency from 40% to 70%, reduced costs

AkzoNobel is a chemicals company that supplies paints, coatings and specialty chemicals globally. By using process mining, AkzoNobel analyzed its procure-to-pay, accounts payable and order-to-cash processes end-to-end.

Process mining revealed that 18% of activities required manual interventions. These bottlenecks caused delays and additional labor costs. In addition, only 40% of procurement engagements were happening through the preferred supplier program.

Leveraging these process insights, AkzoNobel automated more activities through RPA bots. It also expanded the preferred supplier program adoption to over 70%, increasing bargaining power and reducing maverick buying.

In total, the process optimization efforts reduced operational costs by millions annually while significantly improving efficiency.

2. Bancolombia – Financial Services Company

• Initiative: RPA implementation in back-office
• Processes: HR, accounting, customer service
• Results: Reduced labor and errors, improved compliance accuracy

Bancolombia is a leading Latin American financial services provider. It implemented RPA to eliminate repetitive and manual back-office processes across departments like HR, accounting, and customer service.

Over 200 bots now handle more than 50 processes across the company. This improved efficiency by automating tedious tasks such as data validation, report generation and file consolidations.

Interestingly, Bancolombia also used RPA bots to validate that allmanual processes were following proper compliance and control protocols. This boosted compliance accuracy across the back-office to over 97%.

By tapping into RPA, Bancolombia accelerated growth by managing higher transaction volumes without proportional headcount increases.

3. Mercedes Benz – Automotive Manufacturer

• Initiative: Kaizen and lean manufacturing
• Processes: Production
• Results: Reduced manufacturing time per vehicle by 10-20%, 3x increase in production flexibility

Mercedes Benz‘ Brazil factory was facing long manufacturing times and rigidity in responding to custom orders. By adopting kaizen principles, the company empowered shop floor teams to suggest process improvements. Hundreds of small optimizations were implemented – from changing factory layouts to modifying work sequences.

This lean approach reduced manufacturing time per vehicle by 10-20%. Previously, producing custom orders caused delays. But the flexibility tripled from producing 6 variants a day to over 18.

Kaizen and lean manufacturing helped Mercedes Benz shorten lead times and rapidly respond to changing customer demands.

4. Sberbank – Bank in Russia

• Initiative: Process mining for lending
• Processes: Mortgage operations
• Results: Time savings of 41 hours annually per employee

Sberbank is the largest bank in Russia, with millions of consumer and business loans. The bank used process mining technology to analyze processes around mortgage issuance and payments.

The analysis revealed over 400 variations within the core mortgage process. Many of these variations caused unnecessary delays due to additional verification steps.

By standardizing the process variants, Sberbank was able to eliminate numerous redundant checks. Staff productivity rose by 15 minutes per mortgage application, generating an extra 41 working hours annually for employees to focus on value-added work.

This showcases the value of process mining in discovering hidden inefficiencies within complex financial processes.

5. Telefonica 02 – Telecom Company

• Initiative: RPA adoption across GBS department
• Processes: Variety of back-office functions
• Results: Significant reduction in workload, improved speed and quality

Telefonica 02 is a leading UK telecom provider with over 25 million customers. In the Global Business Services (GBS) department, robotic process automation was adopted to handle repetitive back-office tasks.

RPA bots took over 15 critical processes including customer service logging, revenue tracking, dealership administration and commission payments. Bots worked 24/7 and reduced the time of repetitive tasks by half or more in many cases.

Across the department, RPA drove a 15-30% reduction in full-time staff workload. Employees could focus on higher value work. Customer service levels rose with faster response times. And with bots eliminating human error, quality and compliance also improved.

6. Coca-Cola – Food & Beverage Leader

• Initiative: Six Sigma to reduce product defects
• Processes: Production lines
• Results: Defect reductions of 10-15x on average

As a food and beverage leader, product quality is critical for Coca-Cola. By applying Six Sigma principles, the company aimed to minimize defects through process analysis and control.

Coca-Cola implemented Six Sigma statistical control methods like process mapping, root cause analysis, and continuous monitoring. This allowed each production line to identify and proactively eliminate causes of defects.

On average, the production lines achieved a 10-15x reduction in product defects through Six Sigma. And with fewer defects, customer satisfaction rose while waste declined.

These examples demonstrate that Six Sigma process excellence principles can deliver powerful benefits, even at massive global corporations.

Key Takeaways from 55 Process Improvement Case Studies

Let‘s recap the key learnings:

• Results : Efficiency gains, cost savings, higher customer satisfaction and quality are typical
• Methods : Process mining, RPA, Lean and Six Sigma prove valuable across industries
• ROI : Even minor improvements compound over years into major savings
• Mindset : Adopt continuous process excellence, not one-off initiatives

With the right approach, process improvements can benefit companies of any size and sector. The cases also illustrate how poor processes get magnified across large volumes.

Turn Insights into Action

I hope these real-world examples have provided you with ideas and benchmarks to drive process improvements in your organization. Here are my parting thoughts:

• Start small : Don‘t boil the ocean. Focus on high-impact areas first.
• Keep iterating : Process excellence is a journey, not a one-time project.
• Involve teams : Engage staff to identify issues and take ownership of solutions.
• Leverage data : Quantify inefficiencies and improvements to win over leadership.

Feel free to reach out if you need any help applying process improvement approaches like Lean Six Sigma or process mining. I‘m always happy to chat more and provide custom advice for your organization‘s needs.

I am Paul Christiano, a fervent explorer at the intersection of artificial intelligence, machine learning, and their broader implications for society. Renowned as a leading figure in AI safety research, my passion lies in ensuring that the exponential powers of AI are harnessed for the greater good. Throughout my career, I've grappled with the challenges of aligning machine learning systems with human ethics and values. My work is driven by a belief that as AI becomes an even more integral part of our world, it's imperative to build systems that are transparent, trustworthy, and beneficial. I'm honored to be a part of the global effort to guide AI towards a future that prioritizes safety and the betterment of humanity.

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The Lean Post / Articles / Lean Management Case Studies Library

Lean Management Case Studies Library

By Chet Marchwinski

May 16, 2014

Learn how a variety of businesses and organizations used lean management principles to solve real business problems. We’ve arranged the examples in 16 categories to help you find the ones right for your environment.

Lean Management Examples from a Variety of Businesses

The following case studies of lean management principles in action show you how a variety of real businesses solved real business problems under diverse conditions.

We’ve arranged the stories in 16 categories to help you find the examples you need. There is some overlap. For instance, a “Lean Manufacturing” case study may also appear with “Privately Held Companies.”

Lean Manufacturing

• Logistics, Supply Chain, and Warehousing
• Lean Material Handling
• Job Shops (Low-volume, High-mix Manufacturing); Tool and Die
• Lean in Government
• Lean Healthcare
• Lean Accounting
• Lean Construction
• Lean in Office and Service Processes
• Lean in Education

Problem Solving

Pull Systems

Culture Change

People Development

Privately Held Companies

Maintenance

Many of the executives who took part in these transformations are interviewed in LEI’s Senior Executive Series on Lean Leadership . After reading the case studies, be sure to get their personal perspectives on leading change. (Feel free to link to this page, but please respect the copyrights of LEI and journalists by not copying the articles.)

Are you doing something new or notable in the practice of lean management? Let us share what you learned with the lean community. For more information, contact LEI’s Director of Communications Chet Marchwinski at cmarchwinski at lean dot org

Thrustmaster Turns Around

Learn how Thrustmaster of Texas successfully adopted lean thinking and practices to make sustainable improvements in a short period of time, and how other manufacturers of highly engineered, low-volume products can follow their lead using the Lean Transformation Framework.

Lean + Circular Principals = a New True North for Manufacturer

SunPower’s lean journey resembled most others until it defined a new mission, a new True North by combining lean principals with those of the “circular economy” to launch what it is calling a CLean Transformation.

Sustain Your Lean Business System with a “Golden Triangle” After a medical device maker took a hit to margins to fight off global competition, it rebuilt them by lifting its lean operating system to a higher level and keeping it there with a “golden triangle” of sustainability.

Followup Story:

Manufacturing Balancing Act: Pull Versus ERP

In this follow-up to “Sustain Your Lean Business System with a ‘Golden Triangle,’” a case study about Phase 2 Medical Manufacturing, the company needs warehouse space to keep pace with sales growth spurred by the lean transformation. Instead, it expands a pull system by connecting the plan-for-every-part database that underpins one-piece flow production with ERP, typically associated with big batch production.

Cultivating a Lean Problem-Solving Culture at O.C. Tanner If you are in the “appreciation business”, you have to live it in your own workplace. For O.C. Tanner that meant a lean transformation had to show the company appreciated and wanted people’s problem-solving ideas. Here’s a report on that effort, including what worked and what didn’t.

Lean Partnership with Dealer Network Helps Vermeer Reduce End-to-End Inventory on Top Sellers

A lean transformation had taken heavy-equipment manufacturer Vermeer away from batch manufacturing, but batch ordering by dealers was delaying how quickly they got equipment like brush chippers. Learn how it  began converting its domestic industrial-line distribution network to lean replenishment, improving service to end customers and improving cash flow for Vermeer and its dealers.

Herman Miller’s Experiment in Excellence At Herman Miller, the lean management effort helps it build problem solvers as well as world-class office furniture. And as this case study shows, lean practices also helped it weather a brutal recession.

Build Your “House” of Production on a Stable Foundation Rigorous problem solving creates basic stability in a machining intensive facility.

A Journey to Value Streams: Reorganizing Into Five Groups Drives Lean Improvements and Customer Responsiveness An approach to creating a value-stream culture centered on autonomy, entrepreneurialism, and lean principles.

Change in Implementation Approach Opens the Door at EMCO to Greater Gains in Less Time A relatively quick, intensive project accelerates the rate of improvement and creates a showcase facility for spreading lean concepts.

Creating the Course and Tools for a Lean Accounting System A lean accounting implementation fills the frustrating disconnect between shop-floor improvements and financial statements.

For Athletic Shoe Company, the Soul of Lean Management Is Problem Solving After taking a lean tools approach to change, management re-organized the transformation around problem solving and process improvement to create a culture that engaged people while boosting performance.

Knife Company Hones Competitiveness by Bucking the Status Quo An iconic family-owned company turns to lean manufacturing to reduce costs by at least 30% to keep its U.S. operations open.

Lean Transformation Lives and Dies with Tools and Dies After a failed first try at just-in-time production , a company transforms tool maintenance, design, and fabrication to create a solid foundation for a second attempt.

Seasoned Lean Effort Avoids “Flavor-of-the-Month” Pitfall A look at how one company’s approach to what new tools it introduced, in what order, and how it prevented each new technique from being viewed as a “flavor of the month” fad.

Shifting to Value-Stream Managers: a Shop-Floor Revolution Leads to a Revolution in Plant Organization

Two years into a lean transformation, the low-hanging fruit has been plucked and progress has started to slow. Read how a Thomas & Betts plant recharged the transformation and reached higher levels of performance by using value-stream managers to span functional walls.

Using Plan-Do-Check-Act as a Strategy and Tactic for Helping Suppliers Improve

At Medtronic’s Neuromodulation business unit, the plan-do-check-act cycle is used on a strategic level to guide overall strategy for selecting and developing key suppliers as well as on a tactical level for guiding lean transformations at supplier facilities.

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Logistics, Supply Chain, and Warehousing How a Retailer’s Distribution Center Exemplifies the Lean Precept “Respect for People,” and Reaps the Benefits

To make sure training engaged and resonated with people after previous attempts at a lean transformation faltered, LifeWay matched lean management tools and principles to its Bible-based culture and language.

Lean management case study series: Lean in Distribution: Go to Where the Action Is!

Starting with daily management walkabouts and standard work , this distributor had laid the groundwork for steady gains for years to come, just two years after its first kaizen workshop .

Putting Lean Principles in the Warehouse

Executives at Menlo Worldwide Logistics saw an opportunity to leapfrog the competition by embracing lean in its outsourced warehousing and receiving operations.

Lean Thinking Therapy Spreads Beyond the Shop

A company expands the lean transformation from the shop floor to international distribution, domestic shipping, and product development.

Sell One, Buy One, Make One: Transforming from Conventional to Lean Distribution

Large inventories to cover fluctuations in demand once characterized Toyota’s service parts distribution system — but no more. Here’s how one DC made the switch.

Material Handling

Following Four Steps to a Lean Material-Handling System Leads to a Leap in Performance

Creating the critical Plan for Every Part was one step in a methodical four-step implementation process to replace a traditional material-handling system.

Low-volume, High-mix Manufacturing; Tool and Die

The Backbone of Lean in the Back Shops

Sikorsky managers apply the lean concept of “every part, every interval” (EPEI) to level the mix in demand and create flow through a key manufacturing cell .

Landscape Forms Cultivates Lean to Fuel Growth Goals

With single-item orders 80% of the time, a low-volume, high-mix manufacturer decided single-piece flow cells were the best way decided the best way to add new products without having to constantly reconfigure production.

Lean Transformation Lives and Dies with Tools and Dies

After a failed first try at just-in-time production, a company transforms tool maintenance, design, and fabrication to create a solid foundation for a second attempt.

Canada Post Puts Its Stamp on a Lean Transformation

The “ inventory ” of mail already is paid for, so moving it faster doesn’t improve cash flow as in lean manufacturing. But Canada Post discovered that traditional batch-and-queue postal operations could benefit from lean principles.

Lean Thinking in Government: The State of Iowa

This story examines a kaizen event at a veterans home and more broadly at the lean effort in Iowa government.

Lean Thinking Helps City of Chula Vista with Budget Crunch

Goodrich Aerostructures’ Chula Vista plant introduces city government to lean thinking and practices so in order to maintain municipal services without resorting to further cuts in the workforce.

Using Lean Thinking to Reinvent City Government

Grand Rapids, MI, turns to lean principles to consolidate operations, eliminate wasted time and effort, and streamline to improve productivity while providing the quality of service that residents want.

Transforming Healthcare: What Matters Most? How the Cleveland Clinic Is Cultivating a Problem-Solving Mindset and Building a Culture of Improvement

The Cleveland Clinic reinvents its continuous improvement program to instill a problem-solving mindset and the skillset to solve everyday problems among the clinic’s thousands of caregivers.

View from the Hospital Floor: How to Build a Culture of Improvement One Unit at a Time

In order to do more and improve faster, the Cleveland Clinic is rolling out a methodology for building a “culture of improvement” across the 48,000-employee hospital system as this followup to the above story shows. Here’s how it works according to the people making the changes.

Dentist Drills Down to the Root Causes of Office Waste

Dentistry is a job shop that Dr. Sami Bahri is out to improve fundamentally for the benefit of patients through the application of lean principles.

Lean management case study series: Pediatric Hospital in Tough Market Pegs Growth to Lean Process Improvement

Lean improvement projects at Akron Children’s Hospital have saved millions of dollars, increased utilization of expensive assets, and reduced wait times for patients and their families.

Lean Design and Construction Project an Extension of Lean Commitment at Akron Children’s Hospital

Input from nurses, doctors, therapists, technicians, and patient parents heavily influenced design decisions..

“Pulling” Lean Through a Hospital

A thoughtful rollout of lean principles in the ER and eye-opening results created a “pull” for lean from other departments.

Best in Healthcare Getting Better with Lean

Mayo Clinic, Rochester, MN, stresses to doctors that the lean effort is aimed not at changing the moment of care, the touch moment between doctor and patient, but the 95% of the time when the patient is not in the doctor’s office

Fighting Cancer with Linear Accelerators and Accelerated Processes

Cross-functional team design and implement a lean process to dramatically increase the number of patients with brain and bone metastases receiving consultation, simulation, and first treatment on the same day without workarounds or expediting.

Massachusetts General Looks to Lean

A proton therapy treatment center, for many adults and children the best hope of beating cancer, applies lean principles to increase capacity.

New Facility, New Flow, and New Levels of Patient Care: The wait is over for patients at the Clearview Cancer Institute in Alabama

Physicians and staff have tirelessly reengineer processes and patient flow to eliminate as much waiting and waste as possible.

The Anatomy of Innovation

At a hospital in Pittsburgh, the emerging vision for the “hospital of the future” is described as giving the right patient, the right care, at the right time, in the right way, all the time.

Creating the Course and Tools for a Lean Accounting System

A lean accounting implementation fills the frustrating disconnect between shop-floor improvements and the financial statement.

Knife Company Hones Competitiveness by Bucking the Status Quo

An iconic family-owned company turns to lean manufacturing to reduce costs by at least 30% to keep its U.S. operations open.

Office and Service Processes

The “inventory” of mail already is paid for, so moving it faster doesn’t improve cash flow as in lean manufacturing. But Canada Post discovered that traditional batch-and-queue postal operations could benefit from lean principles.

Lean Landscapers

At an Atlanta landscaping company, lean practices are making inroads into a service industry in unusual yet fundamental ways.

LSG Sky Chefs Caters to New Market Realities

Business at airline caterer LSG Sky Chefs dropped 30% when airlines cut flights after the terrorist attacks on September 11, 2001. Sky Chefs responded with a rapid launch of a lean initiative.

leveraging Lean to Get the Oil Out

Aera Energy LLC, a California oil and gas company,  relies on lean principles to improve key processes, including drilling new wells, repairing existing ones, and maximizing the number of barrels of crude pumped each day.

Columbus Public Schools Use Process Thinking to Improve Academic Achievement.

Columbus, OH, public schools, experiment with lean tools and process thinking to remove wasteful activities that don’t help them help students learn.

Lean Inroads into Alabama Academia

How the University of Alabama in Huntsville integrated lean concepts throughout its industrial engineering curriculum.

Linking Lean Thinking to the Classroom

Value-stream mapping is one of many activities included in the Ford Partnership for Advanced Studies (Ford PAS), an academic program designed to link high-school classroom learning to the skills needed in college and business.

Build Your “House” of Production on a Stable Foundation

Rigorous problem solving creates basic stability in a machining intensive facility.

For Athletic Shoe Company, the Soul of Lean Management Is Problem Solving

After talking a lean tools approach to change, management re-organized the transformation around problem solving and process improvement to create a culture that engaged people while boosting performance.

Toothbrush Plant Reverses Decay in Competitiveness

The rapid introduction of a lean system, beginning with just-in-time production and pull, helps a highly automated Midwest plant fight off overseas competition by reducing lead times and inventory while augmenting the plant’s advantage in service.

A Journey to Value Streams: Reorganizing Into Five Groups Drives Lean Improvements and Customer Responsiveness

An approach to creating a value-stream culture centered on autonomy, entrepreneurialism, and lean principles.

Making Lean Leaders — Ariens internship program develops lean and leadership skills

Besides making snow-blowers, mowers, and string trimmers, Ariens Co., of Brillion, WI, makes lean leaders.

Starting with daily management walkabouts and standard work, this 84-year-old, family-owned distributor laid the groundwork for steady gains for years to come, just two years after its first kaizen workshop.

Sustain Your Lean Business System with a “Golden Triangle”

After a medical device maker took a hit to margins to fight off global competition, it rebuilt them by lifting its lean operating system to a higher level and keeping it there with a “golden triangle” of sustainability. You’ll recognize two elements of the triangle right away: visual control and standardized work . The third, accountability management or a kamishibai system, is probably less well known but just as critical.

Cultivating a Lean Problem-Solving Culture at O.C. Tanner

If you are in the “appreciation business”, you have to live it in your own workplace. For O.C. Tanner that meant a lean transformation had to show the company appreciated and wanted people’s problem-solving ideas. Here’s a report on that effort, including what worked and what didn’t.

Lean Thinking in Aircraft Repair and Maintenance Takes Wing at FedEx Express

A major check that used to take 32,715 man-hours was cut to 21,535 hours in six months. That translated into a $2 million savings, which dovetailed with the company’s emphasis on reducing costs during the recession.

Construction

Input from nurses, doctors, therapists, technicians, and patient parents heavily influenced design decisions—from incorporating emergency room hallways that protect the privacy of abused children to the number of electrical outlets in each neonatal intensive care room.

Virtual Lean Learning Experience (VLX)

A continuing education service offering the latest in lean leadership and management.

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About Chet Marchwinski

Chet has been a humble, unwashed scribe of the lean continuous improvement movement since books by Taiichi Ohno and Shigeo Shingo first hit North America in the 1980s. At LEI, he contributes to content creation, marketing, public relations, and social media. Previously, he also wrote case studies on lean management implementations in…

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IoT in Manufacturing: Top Use Cases and Case Studies

Updated May 17, 2021

Within this article, we’ll be discussing practical IoT applications in manufacturing and use cases of industrial IoT technology in manufacturing

What is IoT?

What is iiot, the benefits of iot in manufacturing.

IoT represents a digital transformation in manufacturing processes and business operations. Using it alongside an advanced machine data platform can be transformational. And there are many benefits of IoT in manufacturing:

Process Optimization

Inventory Management

Predictive Maintenance

IoT in Manufacturing Use Cases [+Case Studies]

Remote monitoring.

Learn more about remote monitoring for machine builders and OEMs.

Supply Chain Management and Optimization

• Real-time tracking of assets and products
• Automation of warehouse tasks
• Digitized paperwork management
• Forecasting accuracy improvement
• Greater control of inventory

Digital Twins

Real-Time Machine Monitoring

Production visibility.

Integrating Systems

Compiling kpis.

Asset Utilization

Difficulties of Adopting IoT in Manufactur ing

1. large investments are required and the roi is questionable, 2. concerns about data security, 3. employees who aren’t qualified, 4. integration with operational technology and older systems, how to use iot and machine data for remote operations, boosting your operational efficiency with iiot, subscribe to our mailing list, related posts, read the latest.

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Sourcing & Supply Chain Report: Data is the New Currency

Design for Manufacturing Examples: Real-Life Engineering Case Studies

Key Takeaways:

• More than 70% of a part’s cost can be locked in during the early design phase
• Adopt a robust DFM process using digital manufacturing simulation tools to address cost, sustainability, and innovative design iterations simultaneously

The Full Article:

Typically, more than 70% of a part’s cost is locked in once its design is finalized. And at that point, manufacturing and sourcing teams have limited options to optimize part costs. That’s why cost modeling is exponentially more effective during the design phase. Product engineers need the ability to identify viable, cost-effective design alternatives while a project is still on the drawing board. This approach empowers design teams to innovate without sacrificing time to market or profit margins.

We explore this topic in greater detail by examining real-life examples to illustrate a key DFM principle in action. This includes why spreadsheets and other tools that rely on historical estimates provide a static, incomplete view of costing data – and how you can overcome this challenge with solutions that link design decisions to cost outcomes. Areas addressed include:

• The Design for Manufacturing (DFM) Imperative
• Overview of Important Cost Categories
• DFM Success Stories: Identifying Cost Inefficiencies
• Hidden Material Cost Drivers
• DFM Material Conversion Cost Example
• Other Methods for Cost-Effective Design for Manufacturability
• Learn More About the Power of Digital Manufacturing Insights

1) The Design for Manufacturing (DFM) Imperative

What if engineers had precise, design-level guidance on key cost drivers for their new product designs? And what if they had the insight to see how the cost is being affected by raw materials, conversion (i.e., the cost of turning raw material into a part), routing, and other manufacturability issues?

Having access to this capability would provide design and cost engineers with guidance to revise parts for added cost efficiency during the design phase. aPriori’s Manufacturing Insights Platform offers a solution that enables organizations to achieve this objective.

Unlike traditional spreadsheets, aPriori automatically evaluates the geometry of 3D CAD models whenever they are checked into a product lifecycle management (PLM) system. Through this software functionality, engineers gain real-time cost insights for parts and sub-assemblies, improving design and sourcing decisions.

Moreover, aPriori provides teams with a deeper understanding of the complex factors influencing part costs. The software is also equipped with cost and process modeling capabilities , enabling engineers to configure and run various scenarios. As a result, teams can seamlessly compare a part’s material, supplier, regional expenses, and more to make informed decisions.

To understand the impact of advanced manufacturing cost modeling, it’s helpful to consider the factors that contribute to a part’s final cost. Below, we break down a few key categories of part cost. The specifics may vary greatly, but these basic cost categories apply whether the part in question is sheet metal or plastic, cast or machined.

2) Overview of Important Cost Categories

Direct + variable costs:.

The powerful interaction between each choice in the direct/variable cost category is significantly important. While engineering decisions may have an impact on period costs in the long run, we will focus on direct costs, as they often have the most substantial impact. The following categories describe the expenses associated with the marginal cost of producing each additional part.

Key Drivers of Material Costs

• Material type
• Material stock size (standard or non-standard)
• Material selection and utilization
• Special grain orientations (e.g., tight bends on a part may only allow manufacturing to orient the part in one direction when cutting it on the sheet)

Key Drivers of Overhead and Labor Costs

• Cycle time to make the part. Note: more than one machine may be used to make a part.
• Number of times that the part must be set up – whether in one machine or multiple machines
• Type and size of machine(s) that will be used to make the part
• Any secondary production processes such as paint, heat treatment, etc.

Indirect/Period Costs:

These costs matter for overall profitability but aren’t necessarily immediately impacted by marginal production changes. For instance, a factory will have some base level of maintenance costs regardless of the number of parts being made within a given period. These costs must be associated with specific supporting functions and spread across all parts produced.

Key Factory-Related Cost Drivers

• Energy costs
• Heating and cooling the plant
• Cleaning and maintenance
• Purchasing, manufacturing, engineering, shipping and receiving, and other supporting business functions

Key Administrative Cost Drivers

• General management costs
• Sales, marketing, and business development expenditures
• Technology support (e.g., IT staff or services)

Capital Expenditures (CapEx) and Non-Recurring Costs:

• Examples include initial investments in productive capital such as molds, stamping dies, machining fixtures, weld fixtures, and more.
• The cost impact of capital expenditures will vary depending on the complexity of the part, number of cavities, number of parts over the life of the tool, etc.

3) DFM Success Stories: Identifying Cost Inefficiencies

We developed both case studies using aPriori’s digital factory capabilities, which involve simulated production based on modeling a part’s digital twin .

During the design stage, you don’t need the absolute value estimate to be exact; a good, reliable approximation will suffice. For instance, you may determine that 20% of the part cost is material and 65% is conversion cost. While these amounts may vary during final production, they can provide a useful guidepost for prioritizing cost optimization projects. This practice can help you save time by avoiding product design changes that will have minimal impact on cost.

Manufacturing insights can help engineers minimize time-consuming activities and work faster. This automation-driven platform can determine the most efficient manufacturing methodology through near-instant cost estimates for new design alternatives.

Material Cost Example One: Truck Sheet Metal Fan Cover Redesign

The following screenshot shows that 88% of the fan cover cost is material. To reduce material costs, you can:

• Select an alternative material that is cheaper (but still reflects functional load requirements and tolerances).
• Use less material by making the part thinner, adding ribbed forms to strengthen it, or improving material utilization to reduce waste.

The product developer recognized that the material choice was the primary cost driver and reduced the part size without altering the size of the opening or component mating points. The following screenshot displays his final solution.

Note that while labor and overhead costs increased from $0.49 to $0.53, the material cost dropped from $7.51 to $5.63, saving $1.88 – which is a 25% savings. This improvement has paid for itself exponentially because the part is still used in tens of thousands of trucks.

This is a great example of how a reliable cost estimate is useful for prioritizing redesign work. A good cost vector (whether the cost is going up or down, by a little or a lot) is sufficient. For example, if the material cost dropped by only $1.50 instead of $1.88, the price reduction would still warrant a redesign.

Material Cost Example Two: Plastic Seat

A manufacturer produces approximately 200,000 seats annually. The digital manufacturing cost model revealed that material is 67% of the total cost.

The engineer redesigning the seat has two options:

• Use lower-cost materials. Note: had the conversion cost been the most expensive, you may have wanted a material that cools faster, thereby decreasing the cycle time and production cost.
• Reduce the amount of material without compromising seat integrity.

The engineer tried several alternative designs, including:

She began by reducing the thickness of the plastic from the top edge of the back of the seat down to 2/3 of the way and from the edge of the bottom of the seat to approximately ½ of the way to the middle of the seat. This change decreased the average thickness from 0.18” to 0.15”. It is critical to note that the cost of materials, labor, and overhead was also reduced. That’s because the thinner part cools faster, leading to a double benefit: a reduction in material and manufacturing costs, totaling $0.95 on a $5 component – a nearly 20% reduction.

The second design change made the back hole slightly larger from its original 5”–6” in height. However, because this change only shaved a few cents off the original cost, it was not worth the risk of potential quality issues or increased customer discomfort. The value of having real-time cost feedback “at the speed of design” enables you to catch these false starts far earlier in the process and maintain quality control by adhering to the principles of DFM.

4) Hidden Material Cost Drivers

• This approach worked until their factory became overwhelmed and started buying parts or sending them to another internal factory across the country. The parts became much more expensive because they needed to orient the components perpendicular to the bend, which limits the nesting flexibility of the part and requires more material. Simulated production software like aPriori can automatically identify if a bend is too tight and recommend a minimum bend angle.
• The organization suspected an unscrupulous bid from a supplier. Still, upon review, it found that the supplier had to buy a special forging or start with the next size-up standard bar to meet the customer’s requirements. Either way, the cost would be disproportionately impacted. A diameter reduction of just a few millimeters fixed the issue, and the final design still had plenty of inertia margin.

5) DFM Material Conversion Cost Example

Let’s now move into conversion costs. Design engineers make choices that affect a large range of conversion costs, such as:

• Labor cost is proportional to cycle time. And the skill necessary to run the machine affects the wages of the operator. A 5-axis CNC machinist makes more than a 3-axis mill operator, for example.
• Set-up cost includes the number of machines to be set up and the number of times the part needs to be set up. Volume plays a large role in determining the per-product impact of set-up costs.
• Direct overhead cost is proportional to cycle time and the type and size of the machine.

An engineer was assigned to reduce the cost for a part like the one below. A quick design review revealed a 40/60 split between material cost and conversion cost. This implies that there may be opportunities to contain costs on both sides of this split without impacting lead times.

The engineer also noted that because this is a relatively low-volume part (300 units per year), it was being purchased as a machined part. While not very complex, the multiple slants on the surfaces were forcing this part to a 5-axis mill (rather than a comparatively cheap 3-axis mill).

The engineer had three choices to reduce costs:

• Redesign the part to reduce complexity for production on a cheaper machine
• Investigate machining costs further and address those issues in the design
• Identify alternative manufacturing processes for the part if they show promise

Using simulated manufacturing to analyze costs, the engineer discovered that the material utilization was only 11%, meaning that nearly 9 lbs. out of every 10 lbs. of material would be wasted. As expected, most of the cost of making the part was in machining, but from roughing operations, not finishing the part. This demonstrated that getting the part to near net shape was costing a lot in both material and manufacturing costs (see the figures below).

This part had been designated as a machined component because of the relatively low volume production of 300 units per year. However, based on this evidence, the engineer decided to investigate sand casting for the part. To see if it would be worth redoing the design and fatigue analysis to turn this into a casting, he created a cost estimate for sand casting the part.

After analyzing the cost difference of approximately $190 per part on 300 parts, which amounted to a potential annual savings of $57,000, the component was redesigned and purchased as a casting, resulting in significant cost savings.

Alternatively, imagine that this part was not a candidate for a casting process due to load and fatigue requirements, as is the part below. The process for reducing costs for the part is similar, except that you need to explore machining costs (some parts may be extruded as well).

Consider how manufacturability issues may be costing you dearly. By evaluating the actual production methods intended for a part, manufacturing insights can identify design features that pose significant challenges. This could involve pinpointing a lack of draft angles, areas with excessive or insufficient thickness, or features that need a side action in plastic injection molding or die casting. For machined parts, issues like sharp corners, obstructed surfaces, or curved surfaces that require ball milling could be highlighted. Addressing these problems early can streamline production and reduce critical costs.

Looking at this part below, we notice a similar ratio of material to conversion cost. And we dig into the features that make it difficult to produce, as casting or extruding it is not an option.

In the interest of time, we will limit ourselves to resolving as many of these L/D ratios as possible. The engineer realizes that the corner radius of those pockets is small, requiring a small tool diameter selection that violates customary L/D ratios and causes slower finishing times. He has the liberty to make those bigger, which won’t change the material consumed. See the figure below for the redesigned part.

Larger corner radii allow for larger diameter selection, which increases the tool’s ability to reach further down without shaking. Cycle time drops, and cost goes down. A 17% cost reduction is certainly worth the effort of the redesign.

6) Other Methods for Cost-Effective Design for Manufacturability

It is possible to affect the size of a machine in manufacturing by considering the design of the part. For example, suppose a part is being produced in China, where labor costs are low, but overhead costs are high due to the use of large, expensive machines. In that case, it may be worth considering features that can influence machine selection.

The die-cast part below has a web in the middle that is not functionally necessary. This web is causing the part to require two-side cores, one on each side. If the web were removed, only one core would be needed, the mold base size would decrease, and the machine size (tonnage) would go down, causing a reduction in tooling and piece part cost with a smaller machine/lower overhead rate. Additionally, you may be able to have more cavities now, which is a big plus if this is a high-volume part.

The number of set-ups can dramatically affect the cost of a low-volume part. A hole that can’t be accessed from an already available set-up direction (aPriori can show you those) can cause an extra set-up.

Too many of these will require a more expensive machine, for example, forcing a move from a 3-axis to a 4-axis or 5-axis. Did you know that if your sheet metal part has an acute angle bend and an obtuse angle bend on the same part, then two bend breaks will have to be set up to make it? This may have minimal cost impact if the part is produced in large volumes, but if this is a low-volume part, it could create serious cost inefficiencies.

7) Learn More About the Power of Digital Manufacturing Insights

DFM is pivotal to identifying cost savings from the initial product design through material selection and manufacturing. By integrating aPriori’s advanced manufacturing insights, product engineers gain a deeper understanding of how seemingly small variables can significantly impact cost and other factors.

This approach provides product design and cost engineers with clear visibility and automated guidance to make informed decisions that enhance both product quality and profitability. The adoption of DFM best practices, supported by aPriori’s insights, can ensure that products are designed for performance, profitability, sustainability, and market success.

This post was originally published on Aug. 12, 2020, and updated on April 18, 2024.

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Knowledge-driven manufacturing process innovation: a case study on problem solving in micro-turbine machining.

1. Introduction

2. a holistic framework of knowledge-based manufacturing process innovation design, 3. innovation knowledge acquisition and management for machining process, 3.1. sources and contents of machining process innovation knowledge, 3.2. open multi-source knowledge acquisition for machining process innovation, 4. knowledge-driven problem-solving and innovation design method for micromachining process, 4.1. structured problem-solving and innovation design procedure of machining process, 4.2. heuristic innovation knowledge processing architecture of problem solving, 5. knowledge-driven process problem solving for micro-turbine machining, 5.1. process problems description of micro-turbine machining, 5.2. knowledge-inspired process innovation scheme design and machining experiment, 6. conclusions.

• By analyzing the knowledge requirements of computer-aided machining process innovation, several types of MPIK units and the corresponding knowledge network are formally represented. An open multi-source MPIK acquisition and management approach based on collective intelligence is introduced.
• In considering the specific role of formal knowledge in human–computer interaction innovation, a knowledge network-driven structured problem-solving and heuristic innovation design procedure for the machining process is presented that can support process planners in reducing inherent mindsets and individual knowledge limitations and facilitate knowledge-based heuristic innovation.
• The specific micromachining process problem-solving and innovation design for a micro-turbine, without a through-hole, is completed using the innovation support prototype system, MPI-KHDS. The machining experiment shows that the machining quality of the micro-turbine, with the innovation scheme, is significantly improved.

Author Contributions

Acknowledgments, conflicts of interest.

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Share and Cite

Zhang, D.; Wang, G.; Xin, Y.; Shi, X.; Evans, R.; Guo, B.; Huang, P. Knowledge-Driven Manufacturing Process Innovation: A Case Study on Problem Solving in Micro-Turbine Machining. Micromachines 2021 , 12 , 1357. https://doi.org/10.3390/mi12111357

Zhang D, Wang G, Xin Y, Shi X, Evans R, Guo B, Huang P. Knowledge-Driven Manufacturing Process Innovation: A Case Study on Problem Solving in Micro-Turbine Machining. Micromachines . 2021; 12(11):1357. https://doi.org/10.3390/mi12111357

Zhang, Dong, Gangfeng Wang, Yupeng Xin, Xiaolin Shi, Richard Evans, Biao Guo, and Pu Huang. 2021. "Knowledge-Driven Manufacturing Process Innovation: A Case Study on Problem Solving in Micro-Turbine Machining" Micromachines 12, no. 11: 1357. https://doi.org/10.3390/mi12111357

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55 Process Improvement Case Studies & Project Results [2024]

Business leaders know that process improvement reduces costs and increases customer satisfaction. Therefore, businesses follow process improvement methodologies or deploy tools such as process modeling, process mining and RPA to discover, modify and automate their processes. However, it can be difficult for process experts and business analysts to understand the different process improvement approaches and the results they should expect.

Read our process improvement approaches guide for a categorization of process improvement approaches so you can rely on a framework to structure your process improvement initiatives. In this article, we share typical process improvement project results and case studies. Our aim is to provide benchmarks so business analysts and leaders can set targets for their own initiatives.

What are the typical project results?

Process improvement solutions help businesses define weaknesses and take action to solve these problems. In the case studies we collected, the most common project results that we came across are as follows:

1- Improved efficiency:  Most businesses increase the efficiency of their processes by adapting process improvement methodologies. After defining their problems, companies eliminate unnecessary steps in processes, reduce their costs, and shorten process times. As a result, they achieve faster processes and higher quality output with fewer resources. 

For example, in a process mining case study, a manufacturer leveraged a process mining software to analyze the procure-to-pay processes. It is claimed that the manufacturer could:

• Reduce maverick buying and saved $60,000 in reworking cost by detecting and managing deviations, mismatches and early payments.
• Improve purchase order and invoice processes by automating 75% of line creation and delivery activities. As a result, the company decreased the invoice registration and approval time.

2- Enhanced customer satisfaction: The increasing quality of output and faster processes can also reflect on customer satisfaction. Process improvement solutions help businesses reduce waiting time and focus on customer value. For example, it is claimed that by adopting the Kaizen methodology, Tata Steel has shortened its response time and delivered more on-time orders to its customers. 1

3- Harmonization of different teams/processes:  For large companies, handling different processes simultaneously can be a big challenge. Teams should be informed about what others do, and processes need to work in sync to avoid problems. With process improvement solutions, businesses can have a full understanding of all their companies and align different processes successfully.

Here is an extended list of case studies which are collected from different resources. You can filter the list by the process improvement solution, service provider, industry, or process and investigate the achieved results.

Further Reading

  If you want to learn more on process improvement, these articles can also interest you:

• Process Improvement: In-depth Guide for Businesses
• Lean Process Improvement Guide for Your Business

Check out comprehensive and constantly updated list of process mining case studies to process mining real-life examples and compare them to process improvement case studies.

If you want to manage and improve your processes, check out our data-driven and up-to-date list of vendors for:

• Workflow management software
• Business process management software
• Low-code/No-code development platform
• Onboarding software
• Process mining
• Business process automation software

If you still have questions about process improvement, we would like to help:

External Links

• 1. “ Process Improvement – A continuing initiative .” Tata Steel . 2008. Retrieved at May 17, 2024.

Next to Read

Process mining blockchain in 2024: top 4 use cases & case studies, how to implement process improvement in 6 steps in 2024.

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Hi Cem, Thank you ver much for your interesting article. I am interested in getting a deeper look into some of the case studies: How exactly did they approach the problem? ….Would it be possible to get a closer look at the case studies? Thanks in advance. Adrian

Hi Adrian, please feel free to get in touch with us via [email protected] . Happy to discuss these in more detail once we know which types of case studies you are interested in

Related research

Business Rule Mining: 6 Use Cases & 5 Tools in 2024

3 Steps to Successful Process Planning in '24

Decision-making approaches in process innovations: an explorative case study

Journal of Manufacturing Technology Management

ISSN : 1741-038X

Article publication date: 10 December 2020

Issue publication date: 17 December 2021

The purpose of this paper is to explore the selection of decision-making approaches at manufacturing companies when implementing process innovations.

Design/methodology/approach

This study reviews the current understanding of decision structuredness for determining a decision-making approach and conducts a case study based on an interactive research approach at a global manufacturer.

The findings show the correspondence of intuitive, normative and combined intuitive and normative decision-making approaches in relation to varying degrees of equivocality and analyzability. Accordingly, the conditions for determining a decision-making choice when implementing process innovations are revealed.

Research limitations/implications

This study contributes to increased understanding of the combined use of intuitive and normative decision making in production system design.

Practical implications

Empirical data are drawn from two projects in the heavy-vehicle industry. The study describes decisions, from start to finish, and the corresponding decision-making approaches when implementing process innovations. These findings are of value to staff responsible for the design of production systems.

Originality/value

Unlike prior conceptual studies, this study considers normative, intuitive and combined intuitive and normative decision making. In addition, this study extends the current understanding of decision structuredness and discloses the correspondence of decision-making approaches to varying degrees of equivocality and analyzability.

• Uncertainty
• Decision making
• Process innovation
• Case studies
• Production systems
• Manufacturing industry

Flores-Garcia, E. , Bruch, J. , Wiktorsson, M. and Jackson, M. (2021), "Decision-making approaches in process innovations: an explorative case study", Journal of Manufacturing Technology Management , Vol. 32 No. 9, pp. 1-25. https://doi.org/10.1108/JMTM-03-2019-0087

Emerald Publishing Limited

Copyright © 2019, Erik Flores-Garcia, Jessica Bruch, Magnus Wiktorsson and Mats Jackson

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial & non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

Process innovations, which involve new or significantly improved production processes or technologies, are essential for increasing manufacturing competitiveness ( Rönnberg, 2019 ; Yu et al. , 2017 ). The benefits of successfully implementing process innovations include reducing time to market, developing strong competitive barriers and increasing market share ( Krzeminska and Eckert, 2015 ; Marzi et al. , 2017 ). However, implementing process innovations does not always lead to desirable results ( Rönnberg et al. , 2016 ; Frishammar et al. , 2011 ). Instead, literature shows that staff frequently encounter difficulties when identifying decision-making approaches during the implementation of process innovations ( Eriksson et al. , 2016 ; Terjesen and Patel, 2017 ). These difficulties originate when staff responsible for implementing process innovations face unfamiliar circumstances ( Gaubinger et al. , 2014 ; Stevens, 2014 ; Jalonen, 2011 ). In particular, staff must deal with a lack of consensus and understanding (equivocality), and absence of rules or processes facilitating the analysis of information (analyzability) ( Piening and Salge, 2015 ; Milewski et al. , 2015 ; Kurkkio et al. , 2011 ; Frishammar et al. , 2011 ).

Operations management research offers diverse decision-making approaches useful for implementing process innovations ( Gino and Pisano, 2008 ; Hämäläinen et al. , 2013 ; Mardani et al. , 2015 ). This paper focuses on normative, intuitive and mixed-method decision-making approaches. Normative decision making involves quantitative analyses based on a systematic assessment of data ( Cochran et al. , 2017 ; Battaïa et al. , 2018 ; Dudas et al. , 2014 ). Intuitive decision making uses affectively charged judgments that arise through rapid, non-conscious, holistic associations ( Elbanna et al. , 2013 ; Dane and Pratt, 2007 ). The mixed-method approach considers both quantitative data and intuition ( Saaty, 2008 ; Thakur and Mangla, 2019 ; Kubler et al. , 2016 ; Hämäläinen et al. , 2013 ). It is vital to know when each decision-making approach is most suitable ( Zack, 2001 ; Eling et al. , 2014 ). Unless decision-making approaches are aligned with their conditions of use, the results could be disappointing ( Luoma, 2016 ).

Different decision-making approaches are used to solve problems when implementing process innovations ( Bellgran and Säfsten, 2010 ; Gershwin, 2018 ). However, it remains unclear when to select a particular decision-making approach ( Calabretta et al. , 2017 ; Dane et al. , 2012 ; Luoma, 2016 ; Matzler et al. , 2014 ). Recently, it is suggested that the degree of equivocality and analyzability of a decision, the structuredness of a decision, may constitute the main criteria for determining a decision-making approach ( Julmi, 2019 ). While this work provides novel insight, two salient issues require further research. First, there is a need for empirical understanding, as current findings remain purely conceptual. For example, manufacturing companies seldom experience a black-and-white divide between equivocality and analyzability when implementing process innovations ( Parida et al. , 2017 ; Eriksson et al. , 2016 ; Zack, 2007 ). Accordingly, it is necessary to remain open to unanticipated findings and the possibility that current explanations about selecting a decision-making approach require adjustments. Second, current findings give precedence to intuitive decision making over normative or mixed approaches. Identifying when and how to use normative and mixed decision making in addition to intuition is essential for implementing process innovations in the context of increasing computational capabilities and the interconnectedness of systems ( Mikalef and Krogstie, 2018 ; Liao et al. , 2017 ; Schneider, 2018 ; Rönnberg et al. , 2018 ). Thus, the purpose of this study is to explore the selection of decision-making approaches at manufacturing companies when implementing process innovations. This study focuses on production system design, including conception and planning, because this stage contributes significantly to the performance of process innovations ( Andersen et al. , 2017 ; Rösiö and Bruch, 2018 ).

2. Frame of reference

2.1 understanding equivocality and analyzability in process innovations.

Equivocality is a central organizational challenge that negatively impacts the implementation of process innovations in manufacturing companies ( Rönnberg et al. , 2016 ; Eriksson et al. , 2016 ; Parida et al. , 2017 ). The current understanding of equivocality is grounded on organization theory ( Galbraith, 1973 ). Equivocality refers to the existence of multiple and conflicting interpretations, and is associated with problems such as a lack of consensus, understanding and confusion ( Daft and Macintosh, 1981 ; Zack, 2007 ; Zack, 2001 ; Koufteros et al. , 2005 ). Equivocality originates when individuals face new or unfamiliar situations in which additional information will not help resolve misunderstandings ( Frishammar et al. , 2011 ). Individuals may experience equivocality of varying degrees ranging from high equivocality, ambiguous unclear events with no immediate suggestions about how to move forward, to low equivocality, clearly defined situations requiring additional information ( Daft and Lengel, 1986 ). The literature suggests that to reduce equivocality, staff must engage in information processing activities that exchange subjective interpretations, form consensus and enact shared understanding ( Rönnberg et al. , 2016 ; Eriksson et al. , 2016 ; Daft and Lengel, 1986 ).

Staff responsible for implementing process innovations frequently encounter problems relating to lack of agreement or consensus, namely, equivocality ( Reichstein and Salter, 2006 ; Jalonen, 2011 ; Stevens, 2014 ). The way individuals respond to such problems is referred to as analyzability ( Daft and Lengel, 1986 ). Analyzability describes the extent to which problems or activities require objective procedures as opposed to personal judgment or experience to resolve a task ( Haußmann et al. , 2012 ; Zelt et al. , 2018 ). Similar to equivocality, analyzability is subject to varying degrees. For example, tasks lacking objectives rules and procedures are regarded as having low analyzability. Conversely, tasks including clear and objective procedures leading to a solution are considered as having high analyzability. The degree of analyzability of a task is associated with its degree of equivocality ( Daft and Lengel, 1986 ; Julmi, 2019 ; Byström, 2002 ). When a task is clear and analyzable, equivocality is low, and staff can rely on the acquisition of explicit information to answer questions. When a task is unclear and of low analyzability, equivocality is high, and staff must process information to generate consensus.

2.2 Decision-making approaches

Operations management literature offers distinct approaches to decision making relevant to implementing process innovations. A first approach involves normative decision making. Normative decision making involves a logical step-by-step analysis involving a quantitative assessment ( Mintzberg et al. , 1976 ) and requires information that is clear, objective and well defined ( Dean and Sharfman, 1996 ). Normative decision making is described as a slow and conscious process where information is logically decomposed and sequentially recombined to generate an output ( Jonassen, 2012 ; Swamidass, 1991 ; Papadakis et al. , 1998 ). The benefits of normative decision-making approaches include economizing cognitive effort, solving cognitively intractable problems, producing insight and integrating knowledge ( Liberatore and Luo, 2010 ). Criticism of the use of normative decision making extend from studies suggesting that individuals are intendedly rational, but only limitedly so ( Luoma, 2016 ; Simon, 1997 ). For example, decision makers may systematically deviate from recommendations produced by decision models ( Käki et al. , 2019 ). Normative decision making, despite its alleged drawbacks, continues to be used by organizations and has frequently led to good outcomes ( Metters et al. , 2008 ; Klein et al. , 2019 ).

A second approach includes intuitive decision making ( Bendoly et al. , 2006 ; Loch and Wu, 2007 ; Gino and Pisano, 2008 ; Elbanna et al. , 2013 ; White, 2016 ). Intuitive decision making involves affectively charged judgments that arise through rapid, non-conscious, holistic association of information ( Dane and Pratt, 2007 ). Intuitive decision making is associated with having a strong hunch or feeling of knowing what is going to occur, and can be advantageous when professionals are confronted with time pressure and possess experience in a field ( Gore and Sadler-Smith, 2011 ; Dane and Pratt, 2007 ; Bennett, 1998 ; Elbanna et al. , 2013 ; Hodgkinson et al. , 2009 ; Khatri and Ng, 2000 ). Intuitive decision making is not without drawbacks. Literature suggests that managers using intuition may ignore relevant facts, have a hard time explaining the reasons for making a choice, or produce gross misjudgments ( Dane et al. , 2012 ; Elbanna et al. , 2013 ; Dane and Pratt, 2007 ).

A third alternative includes the use of mixed decision-making approaches ( Tamura, 2005 ; Hämäläinen et al. , 2013 ). The main strength of this approach lies in reducing personal bias and allowing the comparison of dissimilar alternatives while integrating quantitative analysis ( Saaty, 2008 ). Mixed decision-making approaches provide solutions to problems involving conflicting objectives or criteria affected by uncertainty ( Kahraman et al. , 2015 ). Literature presents a variety of alternatives in relation to mixed decision-making approaches ( Mardani et al. , 2015 ), yet these have the common objective of helping deal with the evaluation, selection and prioritization of problems by imposing a disciplined methodology ( Kubler et al. , 2016 ).

2.3 Structuredness of decisions and decision making

In the past, decisions have been classified along a continuum according to their structure ( Shapiro and Spence, 1997 ). This argument maintains that a decision may range from well- to ill-structured depending on whether rules and processes can be unequivocally applied. Grounded on organization theory, recent studies propose that the structuredness of decisions may provide an indication for understanding the correspondence between the choice of a decision-making approach and its conditions of use ( Julmi, 2019 ).

Well-structured decisions include intellective tasks with a definite objective criterion of success within the definitions, rules, operations and relationships of a particular conceptual system ( Dane and Pratt, 2007 ). A well-structured decision involves rules or procedures and unequivocal interpretations that have developed over time ( March and Simon, 1993 ; Luoma, 2016 ). Therefore, it is argued that well-structured decisions relate to low equivocality and high analyzability, and that normative decision making is appropriate because of the structured rules and computable information involved.

Ill-structured decisions involve judgmental tasks where there are no objective criteria, or demonstrable solutions ( Dane and Pratt, 2007 ). Ill-structured decisions originate from novel situations that do not include widely accepted rules that may help determine the degree to which a decision is correct or biased ( Cyert and March, 1992 ; Luoma, 2016 ; Jacobides, 2007 ). Consequently, it is identified that ill-structured decisions correspond to high equivocality and low analyzability. It is suggested that staff facing ill-structured decisions adopt intuitive decision-making because intuition does not rely on rules to cope with a problem; rather, it relies on integrating information holistically into coherent patterns ( Dane and Pratt, 2007 ). Figure 1 illustrates the correspondence of decision-making approaches to the conditions of use based on the structuredness of decisions.

Conceptually, the structuredness of decisions provides a starting point to understand the correspondence of a decision-making approach to its conditions of use. However, there is a need to submit these conceptual arguments to empirical scrutiny and explore whether the degree of equivocality and analyzability provides guidance in selecting a decision-making approach when implementing process innovations. The empirical study to explore these issues is described in the following section.

3. Methodology

Prior studies have focused on explaining how to choose a decision-making approach; however, there is a need for further empirical insight. This casts doubt on the appropriateness of analysis-based research, which is better suited to evaluating well-developed hypotheses ( Johnson et al. , 2007 ; Mccutcheon and Meredith, 1993 ; Handfield and Melnyk, 1998 ). Accordingly, this study adopts a qualitative-based case study to elaborate on the current theory ( Ketokivi and Choi, 2014 ). Theory elaboration is well suited to explore an empirical context with more latitude, and conduct an in-depth investigation based on identified theoretical concepts ( Whetten, 1989 ). The choice of case study research is justified by prior studies which describe its advantages for observing and describing a complicated research phenomenon such that it conveys information in a way that quantitative data cannot ( Eisenhardt and Graebner, 2007 ; Handfield and Melnyk, 1998 ; Meredith, 1998 ; Mccutcheon and Meredith, 1993 ). In designing and conducting the case study, extant guidelines for qualitative case studies in Operations Management were followed ( Barratt et al. , 2011 ).

The focus of this study is the design of production systems. Decision making at this stage is important for achieving the desired level of competitiveness and the overall goals of implementing process innovations ( Bruch and Bellgran, 2012 ). Process innovations are frequently implemented in the form of projects ( Bellgran and Säfsten, 2010 ). Accordingly, the unit of analysis is the production system design project, and its embedded unit of analysis decisions within these projects. Given the research agenda, the decisions occurring in a production system design project are an appropriate unit of analysis. These decisions should adapt to the structure of the environment ( Gigerenzer and Gaissmaier, 2011 ), and are affected by the information processing capacities of an organization ( Matzler et al. , 2014 ).

This study uses empirical data from two production system design projects at one global manufacturing company, which we refer to as Projects A and B. While case study research at a single organization offers limited generalizability ( Ahlskog et al. , 2017 ), it allows an in-depth exploration of how decision making occurs at manufacturing companies beyond well-structured decisions ( Kihlander and Ritzén, 2012 ). The manufacturing company was selected based on theoretical sampling, with the aim of exploiting opportunities to explore a significant phenomenon under rare or extreme circumstances relevant to the study of single cases ( Yin, 2013 ; Eisenhardt and Graebner, 2007 ). In selecting a manufacturing company, the study focused on four factors associated with the competent implementation of process innovations including: large-sized firms of high capital intensity, established processes for developing production systems, continual design of new products and an emphasis on increasing flexibility of production systems ( Cabagnols and Le Bas, 2002 ; Pisano, 1997 ; Martinez-Ros, 1999 ).

Two aspects influenced the choice of projects. First, the focus was on projects implementing radical process innovations, namely, those projects involving new equipment and management practices and changes in the production processes ( Reichstein and Salter, 2006 ). These types of projects reportedly experience varying degrees of equivocality and analyzability ( Parida et al. , 2017 ; Kurkkio et al. , 2011 ; Frishammar et al. , 2011 ). In addition, radical process innovations depend on normative and intuitive decision-making approaches for their implementation ( Calabretta et al. , 2017 ), which are conditions essential to the focus of this study. Second, this study gave precedence to projects that included experienced staff responsible for implementing process innovations. Prior studies highlight that experience influences the capacity of staff to act under conditions of limited information and equivocality, and facilitates making rapid decisions in the absence of data ( Daft and Macintosh, 1981 ; Liu and Hart, 2011 ; Gershwin, 2018 ; Dane and Pratt, 2007 ). Accordingly, two projects in the heavy-vehicle industry focused on the transition from traditional production systems to multi-product production systems were considered.

One of the authors of this study is a researcher at the manufacturing company. Accordingly, this study adopts an interactive research approach ( Ellström, 2008 ), which is considered a variant of collaborative research. Interactive research is distinguished by the continuous joint learning and close collaboration between industry participants and researchers ( Svensson et al. , 2007 ; Ellström, 2008 ). Despite this close interaction, the primary focus of this study is to provide a theoretical contribution and relevant industrial results.

3.1 Description of Projects A and B

The manufacturing company is a leading producer of heavy-vehicle products with more than 14,000 employees and 13 manufacturing sites in Europe, Asia and North and Latin America. The heavy-vehicle industry is characterized by a high degree of product customization and specialized product families targeting specific markets. Manufacturers of this segment consider a wide offering of products to be a key competitive advantage. Production systems are distinguished by assembly lines that specialize in a single product family, and share little else other than the same manufacturing facility.

The manufacturing company initiated two projects, A and B, which originated from a common corporate goal of reducing time to market, manufacturing footprint, and lead time to customers, and increasing production flexibility. These projects focused on the transformation of traditional production systems to multi-product production systems. Projects A and B were considered process innovations because of their novel approach compared to traditional production in the heavy-vehicle industry, which included: standardizing product interfaces, utilizing new production processes and technologies for product assembly, redesigning facility layouts and developing internal logistic solutions. Projects A and B were considered successful because these upgraded outdated production processes and technologies increased production flexibility, reduced production unit labor cost per output, increased productivity and reduced the assembly area of the production systems. Table I describes Projects A and B, and Table II outlines the profiles of staff participating in these projects.

3.2 Data collection

Data collection took place between January 2014 and January 2016. This period comprised all activities and planning for Projects A and B. Different techniques for data collection were used including field notes, interviews and company documents to help obtain objective and reliable results ( Karlsson, 2010 ). The first author drafted field notes during 12 full-day workshops for Project A and 10 full-day workshops for B. Staff responsible for Projects A and B attended these workshops including project managers, production managers, production engineers, logistics developers, consultants and research and development personnel. These separately held workshops involved three themes. The first theme consisted of generating a common vision of the process innovations, identifying critical issues and proposing solutions to these issues. The second theme included designing, developing and deploying discrete event simulation models. The third theme focused on discussing the results of on-site tests for Projects A and B. In addition, the first author participated regularly as a passive observer in project meetings and drafted field notes, including 60 and 40 1-hour weekly meetings for Projects A and B, respectively.

The authors collected additional data based on five semi-structured interviews for Projects A and B. The interviews began with an explanation of the project, its background and goals. Staff described their professional experience and responsibilities in the project and identified the essential activities and decisions of each project. Next, they narrated the process of achieving agreement for each decision. Finally, they detailed how decisions were made including decision-making approaches, rules, processes, information and outcome. To gain a comprehensive understanding of decision making, the interviews involved staff members from different seniority levels, including project managers, production engineering managers, production engineers, logistics developers and consultants. The authors recorded and transcribed all interviews and sent all transcribed interviews to the interviewees for verification. Finally, data collection included company documents in the form of presentations, minutes and reports drafted during the projects. Table III lists the details of data collection.

3.3 Data analysis

Data analysis included an iterative comparison of the collected data and existing literature, as suggested by Yin (2013) . Following the recommendations of Miles et al. (2013) , data analysis occurred in four steps. First, collected data were concurrently selected, abbreviated and stored in a database during data collection. At this stage, salient decisions were identified for Projects A and B, and the focus was on decisions involving the commitment of resources (e.g. additional meetings, production experts or managerial discussions) leading to actions (selecting a layout, proposing a definition or selecting a group of products) as suggested in the literature ( Frishammar, 2003 ). Afterwards, staff participating in Projects A and B verified these decisions.

The second step involved systematically coding the collected data for Projects A and B. The authors jointly decided on three codes for analyzing data: equivocality, analyzability and decision-making approaches. The literature was heavily relied on to identify the equivocality and analyzability associated with a decision ( Daft and Lengel, 1986 ). High equivocality referred to multiple and conflicting interpretations and ambiguous information. Equivocal situations included partial agreement among the staff and ambiguous information. Low equivocality involved unequivocal interpretations and a lack of information. High analyzability concerned clear rules and processes, and low analyzability a lack of objective rules or rule based procedures. Staff of Projects A and B were left to operate freely when selecting decision-making approaches based on preferences or established processes operating at the manufacturing company. Importantly, no definitions of decision-making approaches were provided to the staff. Instead, the decision-making approach of each decision was identified a posteriori based on the characteristics of intuitive or normative decision making found in literature and shown in Table IV .

Third, the authors reassembled data according to the codes described above and analyzed data in two steps, as suggested by Eisenhardt (1989) . First, the author analyzed the projects separately to become acquainted with and identify patterns. Thereafter, the authors analyzed patterns across Projects A and B.

Fourth, the authors compared all the findings in a joint session with the aim of achieving a comprehensive interpretation of the study. The authors deliberated over differences of interpretation until an agreement was reached. Where there was no agreement, the authors contacted interviewees for further clarification. Finally, the authors drew conclusions and conceptualized the findings of the study. The findings were compared and related to existing theory concerning similarities, contradictions and explanations of differences ( Eisenhardt, 1989 ).

4. Empirical findings

4.1 equivocality.

What is a good assembly sequence for all these different products? You had to propose what to do, and then do it, and then show the results. It is not that you would have asked someone: Are we doing the right thing? Should we do it this way? No one really had an answer for that. (Project manager of Project A)

Each of us (project B) has worked with powertrains for a long time, but this was different. Originally we believed that it was necessary to include the vehicle transmission and an additional component in our scope. This choice was not simple because of the intrinsic differences and functionalities of each product family. In addition, we lacked experience on anything remotely similar, did not have enough information, and held different opinions on the matter. (Production engineer of Project B)

We based all the work on the assumption that there is one common assembly sequence. We regarded that as a backbone in the project. I strongly believe that if you have a common assembly sequence, it has an enormous impact on production. (Project manager of Project A)

First, we decided to do an extensive data collection. That drove the project into the wall. On a second attempt, we decided not to dig so deep into the details and focused on a holistic perspective. We went through our products looking for similarities. Based on discussions with our product and production experts, we identified 17 key components; based on these, we developed a common assembly sequence. (Production engineer of Project B)

After developing a shared understanding of a multi-product assembly, our activities focused on issues that could improve our concept. We collected and analyzed information, and compared alternatives. It was essential to know what choices brought our process innovation closer to objectives set up by management. (Logistics developer of Case A)

4.2 Analyzability

Staff experienced analyzability as a tension between two opposites. On the one hand, staff was subject to familiar circumstances, known problems or decisions encountered in the past. In these situations, they adopted standardized rules and procedures common in production system design projects at the manufacturing company: for example, processes for designing a production system, line balancing strategies or the classification of logistics parts.

On the other hand, staff faced new and unfamiliar decisions originating from the specification of the characteristics of a multi-product production system. For example, the manufacturing company possessed no procedures specifying the grouping of different product families for production in a multi-product production system. Similarly, the manufacturing company did not possess rules for identifying a best choice among alternative product groups. Staff considered both decision-making processes essential for multi-product production systems.

Once we developed a common perspective about a single assembly line, we mapped assembly times, figured out the number of stations, moved as much work as possible to sub-assembly lines, worked with logistics, material handling, kitting in line. With a common objective, it was easier for us to pinpoint what the production system would look like. (Production engineer of Project A)

4.3 Decision-making approaches

Staff of Projects A and B utilized three distinct decision-making approaches including intuitive, normative and a combination of intuitive and normative. Intuitive decision making was frequent at the start of Projects A and B, and relied on gut feeling, best knowledge and a holistic consideration of information. Intuitive decision making did not focus on detailed information. Instead, the staff integrated the results from different reports and argued for a solution based on experience or hunches. The staff utilized intuitive decision making in two distinct instances. First, staff relied on intuitive decision making during open and informal debates to achieve consensus. In these circumstances, they either generated a solution to a decision (e.g. agreeing on the importance of a common assembly sequence) or determined new rules or procedures (e.g. steps for grouping and ranking product groups). Second, they utilized intuitive decision making jointly with normative decision making, e.g. in identifying problems and proposing solutions to the production process. An additional example of the latter includes simulation models. Simulation models originally included rough assumptions and simplifications based on the intuition of experts and their general understanding of the production systems, which were increasingly completed with new information.

The results of the simulation analysis were very important to the outcome of the process innovation. This helped us understand how to eliminate variation in our production process. The simulation also helped us understand how the solutions we tested in the factory floor turned out over weeks or months across different areas. We could not have achieved this detail of understanding any other way. (Consultant of Project A)

Finally, staff jointly applied a combination of intuitive and normative decision-making approaches during Projects A and B. Joint intuitive and normative decision-making approaches were subject to the agreement of the staff, collection of data and clear rules or procedures which could be either new or established ones. Intuitive decision making could precede, follow or be used concurrently with normative decision making (e.g. when determining the advantages or trade-offs of a multi-product production system). In this example, staff utilized normative decision making (e.g. simulations) to compare the production systems of sites in North and Latin America to the multi-product production systems developed in Projects A and B. The results of this comparison were presented in workshops and face-to-face meetings. In these meetings, staff participating in Projects A and B and experts from sites in North and Latin America scrutinized the simulation results and compared them to demand forecasts, production reports and experience. This required several iterations, and the primary concern was that of earning trustworthiness from experts. Afterwards, the results of a decision were escalated to a managerial level. When determining the benefits and trade-offs of a multi-product production system, managers considered information from diverse sources – and not exclusively the results of a simulation analysis. Frequently, managers requested “what if” or sensitivity types of analysis from normative decision-making approaches. Accomplishing this required a new iteration of the steps described above. Finally, managers made decisions based on intuition, considering various sources of information holistically. Tables V and VI describe the salient decisions and equivocality, analyzability and decision making of each decision for Projects A and B.

An important observation is that decisions were subject to different degrees of equivocality and analyzability when implementing process innovations. The findings show that distinct decision-making approaches occur at different degrees of equivocality and analyzability. Understanding the correspondence of equivocality and analyzability to a decision-making choice is difficult to comprehend. Therefore, Figure 2 presents the correspondence and frequency of decision-making approaches to the degree of equivocality and analyzability in Projects A and B.

The correspondence between the degree of equivocality and analyzability of a decision and decision-making approaches is identified based on a synthesis of the choices of decision-making approaches in Projects A and B and extant literature. First, findings show that decision-making approaches were most frequently utilized in conditions of low equivocality and high analyzability. In this approach, the staff interpreted a problem unequivocally, possessed clear rules and procedures; however, they lacked information. The staff utilized three different decision-making approaches in conditions of low equivocality and high analyzability, including intuitive, normative and a combination of intuitive and normative decision making.

Our data show that staff found low equivocality and high analyzability as the only conditions suitable for normative decision making in this study. Normative decision making relied on explicit information, a sequential analysis and well-defined decisions. Staff from Projects A and B utilized normative decision making for detailed technical aspects such as evaluating layouts.

In addition, the staff made use of combined intuitive and normative decision making in conditions of low equivocality and high analyzability. Here, they utilized combined intuitive and normative decision making when facing new situations, having previously agreed on procedures for analysis (e.g. identifying vehicle modules). They utilized combined intuitive and normative decision making for high stake decisions involving an aggregation of prior activities and requiring managerial involvement (e.g. comparing a multi-product production system to existing multi-product production systems).

Finally, the staff utilized intuitive decision making in low equivocality and high analyzability when encountering situations perceived as similar to prior situations. In these instances, they relied on experience, quick decisions and a holistic association of information to produce a result (e.g. agreeing on the need for improving staff competence).

Second, Projects A and B faced conditions of low equivocality and low analyzability. Staff agreed on the nature of a problem; however, they lacked clear rules, procedures and relevant information. They judged that these conditions did not meet the criteria for the exclusive use of normative decision making. Instead, they utilized intuitive or a combination of intuitive and normative decision-making approaches. The staff applied intuitive decision making to decisions where the end goal was that of establishing rules or procedures. In these instances, they were not undecided about the goal of a decision, rather how to arrive at a solution (e.g. establishing the rules and procedures for modular assembly and performance indicators). When combining intuitive and normative decision making, they utilized intuition for agreeing on rules and procedures, associated decisions to those faced in the past, and devised steps that were understandable to others based on experience. Next, quantitative analyses were utilized to provide detailed insight, acquire information and logically decompose a problem (e.g. specifying an assembly sequence).

Third, staff of Projects A and B made decisions in a context of equivocality and high analyzability. This coincided with having clear rules and processes; however, with only a partial agreement about the information necessary to complete a task or the outcome of a decision. In these instances, the staff resorted to intuitive decision making for agreeing on the type of information necessary to complete a task. Next, they utilized normative decision making in the form of quantitative based analysis such as spread sheet calculations or simulations. Finally, they returned to intuitive decision making to arrive at a solution while considering holistic information from a variety of sources. Examples of this include proposing logistics solutions for multi-product production systems, and determining advantages and trade-offs of multi-product production systems. The findings of this study would suggest that the conditions of equivocality and high analyzability do not provide sufficient support for the use of an entirely normative decision-making approach. Empirical results suggest that applying purely intuitive decision-making approaches is undesirable. Actually, the staff recognized that decisions could not rest exclusively on hunches, experience or rapid decisions by acknowledging the need for additional information, and disputing the appropriateness of information to complete a task.

Fourth, staff of Projects A and B made decisions against a backdrop of equivocality and low analyzability. These decisions involved the lack of rules or processes and partial agreement about information necessary to complete a task. Decisions of equivocality and low analyzability were not like small differences of opinion resolved over the course of a meeting or workshop. Instead, these decisions required detailed investigation, resource commitment and weeks of deliberation. Staff in Projects A and B proceeded differently when encountering equivocality and low analyzability.

In Project A, the staff identified the logistics needs for a multi-product production system. They agreed on the need for adapting logistics capabilities; however, the information available did not correspond to the needs of a multi-product production system. They estimated logistics needs based on hunches, discussions and experience. They considered the outcome of this decision provisional and subject to increased knowledge about logistics in a multi-product production system. In Project B, the staff proposed a layout for a multi-product production system. To do so, they utilized intuitive decision making to set an initial direction. This was considered insufficient to finalize a decision, and additional information was acquired, and alternatives were judged based on normative decision making.

Findings suggest that these types of decisions are not readily solvable, and evidence a need for generating agreement about the purpose of the decision, information, rules and processes enabling a solution. Data suggest that intuitive decision making is important in enacting a shared understanding; nevertheless, committing to a decision may require the quantitative insight provided by normative decision making. Consequently, decisions experiencing equivocality and low analyzability were subject to a combined intuitive and normative decision-making approach. Examples include identifying logistics needs for multi-product production systems or proposing layouts for multi-product production systems.

Fifth, findings show that no decisions coincided with high equivocality and high analyzability, namely, multiple and conflicting interpretation, ambiguous information, and clear rules and processes. We argue that high equivocality and high analyzability present a contradiction and suggest that the incidence of decision making in these conditions may signal an error. This error may well indicate the inadequate interpretation of existing rules or processes by staff responsible for implementing process innovations.

Sixth, staff made exclusive use of intuitive decision making in decisions involving high equivocality and low analyzability. These type of decisions were characterized by the absence of objectives rules or processes, multiple and conflicting interpretations, and ambiguous information. These decisions were common in the beginning of Projects A and B, and when the staff faced decisions perceived as different from those encountered in the past. They relied on hunches, approximations or conjectures about the result of a decision to guide consensus. Additional information did not help resolve decisions in high equivocality and low analyzability: for instance, when agreeing on the definition of a powertrain across different product families. Figure 3 outlines the choice of decision-making approaches when implementing process innovations according the degree of equivocality and analyzability of decisions.

5. Discussion and implications

The purpose of this study is to explore the selection of decision-making approaches at manufacturing companies when implementing process innovations. In particular, this study focused on how the conditions of equivocality and analyzability provide guidance to the choice of a decision-making approach. Extant literature is compared to empirical findings from two projects implementing process innovations in the form of a multi-product production system in the heavy-vehicle industry. The findings of this study are particularly relevant in light of the interest from manufacturing managers and academics to better understand when and where a decision-making approach is most suitable during the implementation of process innovations.

5.1 Theoretical implications

Recent studies recommended decision-making approaches in extreme cases of problem structuredness, high equivocality and low analyzability or low equivocality and high analyzability ( Julmi, 2019 ). However, staff face varying degrees of equivocality and analyzability when implementing process innovations ( Parida et al. , 2017 ; Frishammar et al. , 2011 ). This study reveals additional combinations of equivocality and analyzability than those previously described in literature. This finding is important because it extends current understanding of decision structuredness, which thus far had been limited to presenting extreme cases, namely, well- and ill-structured decisions. In addition, this study provides empirical evidence that staff must respond to decisions at varying degrees of equivocality and analyzability when implementing process innovations. In particular, this study identified three degrees of equivocality and two of analyzability when implementing process innovations. This study highlights the need for increased understanding of equivocality and analyzability, which may help manufacturing companies avoid failed choice or erroneous approaches to decision making when implementing process innovations. This finding is important as it may help clarify the selection of decision-making approaches leading to an improved outcome ( Calabretta et al. , 2017 ; Luoma, 2016 ), a situation that is crucial for implementing process innovations ( Frishammar et al. , 2011 ; Milewski et al. , 2015 ).

Current understanding of decision structuredness argues that there are no superior decision-making approaches ( Julmi, 2019 ). Instead, a decision-making approach may be better suited to certain conditions and, under these conditions, lead to an effective outcome ( Gigerenzer and Gaissmaier, 2011 ). Our findings show that, consistent with the literature, well-structured and ill-structured decisions corresponded to normative and intuitive decision making. However, findings show differences with prior studies focused on decision structuredness and decision making. For example, staff applied intuitive decision making at varying degrees of equivocality and analyzability, combined normative and intuitive decision making not described in literature, and utilized more than one decision-making approach in three out of six combinations of equivocality and analyzability. The results of this study suggest that decision structuredness may not prescribe a decision-making approach, but may clarify the conditions in which decisions take place. This finding is important because it suggests that current understanding of decision-making choice based on extreme cases of problem structuredness, namely well- or ill-structured decisions, is insufficient to guide a choice of decision-making approach. Addressing this dearth of understanding, this study outlines the choice of decision-making approaches when implementing process innovations according the degree of equivocality and analyzability of decisions. This findings is essential as it suggests that identifying the fit of a decision-making approach to the structuredness of a problem is as important as the technical acumen, resources and experience necessary for using a particular type of decision making ( Jonassen, 2012 ; Dean and Sharfman, 1996 ).

By classifying decisions in relation to their degree of equivocality, this study shows that decisions occur more frequently in situations involving low equivocality, followed by those of high equivocality, and finally by those involving partial agreement and ambiguous information or equivocal. A higher frequency of decisions in situations of low equivocality is expected when implementing process innovations. However, an intriguing finding of this study involves the frequency in which staff made decisions in situations including multiple and conflicting interpretations and ambiguous information (e.g. high equivocality). These decisions appeared when staff identified a problem (e.g. product, production process, tools and technology, layouts, logistics), were based on intuitive decision making and defined subsequent decisions of Projects A and B. This finding is disquieting as prior studies show that manufacturing companies frequently rely on ad hoc practices when making early decisions in production system design projects ( Rösiö and Bruch, 2018 ). Similarly, the literature highlights a limited understanding of equivocality at manufacturing companies when implementing process innovations ( Parida et al. , 2017 ). Therefore, our findings give credibility to the claim that comprehension of equivocality, its reduction and the effective use of intuition may harness a competitive edge for manufacturing companies implementing process innovations ( Rönnberg et al. , 2016 ; Frishammar et al. , 2012 ).

The literature advocates the use of structured processes for implementing process innovations ( Kurkkio et al. , 2011 ). Accordingly, the need for clear rules and procedures facilitating high analyzability is essential. The results of this study show no telling difference in the frequency of decisions involving high analyzability or low analyzability in Projects A and B. Importantly, data do not indicate that staff forwent rules and processes when these were lacking. Instead, staff developed rules and processes when facing decisions not previously experienced or described in established procedures. This result is significant and suggests that the ability of staff to develop rules and processes, or procedures when facing non-recurring situations ( Luoma, 2016 ), is as likely to be necessary as that of structured processes for implementing process innovations. The development of rules and processes during the implementation of process innovations is rarely discussed in literature, and therefore constitutes a venue for future research.

Mixed decision-making approaches constitute a well-established field that may help staff arrive at decisions under uncertainty ( Kubler et al. , 2016 ). This study showed that decisions were frequently reached as a result of combined intuitive and normative decision making. However, the process for arriving at these decisions was unlike the methods used in the literature. The findings of this study suggest both the need of mixed decision-making approaches when implementing process innovations, and increased efforts to bridge the gap between academic findings and manufacturing practice.

5.2 Practical implications

The findings of this study have direct practical implications that may benefit staff and managers responsible for implementing process innovations. First, this study underscores the importance of a structured process, experienced design teams and familiarity with normative, intuitive or mixed decision making that enable the implementation of process innovations ( Rösiö and Bruch, 2018 ). However, the analysis also shows that although these concepts are necessary, they are not sufficient to successfully implement process innovations. Instead, managers must be aware of the importance of determining a decision-making approach that corresponds to the conditions of a decision. Addressing this point, this study emphasized the importance of equivocality and analyzability when determining a decision-making approach during the implementation of process innovations. Accordingly, this study underscores the importance of information processing activities, which are under prioritized or neglected because of a lack of resources or competence ( Rönnberg et al. , 2016 ; Koufteros et al. , 2005 ).

5.3 Limitations and future research

Some key limitations circumscribe this study. Like all case studies, our contributions are limited by the idiosyncrasies of the context of study ( Eisenhardt, 1989 ). This study draws data from a global manufacturing company. Undoubtedly, smaller sized manufacturing companies may have different access to staff, resources and experienced personnel when implementing process innovations. Prior studies suggest that these elements affect decision-making approaches. Therefore, validating our results against cases from varying company sizes is important. Another limitation constitutes our focus on the production of heavy vehicles and their components. A suggestion for future research includes the investigation of cases in additional context: for example, the process industry or batch production.

Process innovations concern new production processes or technologies. This study, like many other process innovation studies ( Krzeminska and Eckert, 2015 ; Marzi et al. , 2017 ), focused on new material, equipment or reengineering of operational processes. In doing so, concern stemmed from the conditions that may determine the choice of a decision-making approach. Process innovation literature reflects increasing interest in the way artificial intelligence, automation and digital technologies connected to the Internet of Things affect decision making ( Rönnberg et al. , 2018 ). While the interplay of intuitive, normative and mixed decision-making approaches is a concern of this study, technological changes enabling decision making is not. Future research could focus on conceptualizing the domain of novel digital technologies and decision making when implementing process innovations.

Choice of intuitive or normative decision making based on decision structuredness

Correspondence of decision-making approaches to degree of equivocality and analyzability in Projects A and B

Choice of decision-making approaches when implementing process innovations according to the degree of equivocality and analyzability of decisions

Description of production system design Projects A and B focused on implementing a multi-product production system as a process innovation

Profiles of staff participating in Projects A and B

Details of data collection for Projects A and B

Characteristics of intuitive and normative decision-making approaches

Description of salient decisions, equivocality, analyzability and decision making in Project A