case study big data analytics in manufacturing

Driving Efficiency: Data Analytics Use Cases in Manufacturing Excellence

• June 16, 2023 July 21, 2023

This article delves into the world of data analytics use cases in manufacturing excellence, exploring how organizations leverage data-driven strategies to streamline operations, improve product quality, enhance efficiency, and stay ahead in a competitive market.

Data analytics in manufacturing excellence goes beyond traditional methods of production optimization. It leverages advanced analytics techniques, machine learning algorithms, and real-time data to extract meaningful insights from a wealth of manufacturing data.

• 90% of manufacturing executives say that data analytics is important to their business.
• 70% of manufacturing companies that use data analytics have seen a return on investment (ROI) of at least 20%.
• Data analytics can help manufacturers improve production efficiency, reduce costs, and improve product quality.
• The global market for data analytics in manufacturing is expected to reach $12.9 billion by 2023.

Examples of how data analytics is being used in manufacturing

Siemens: Siemens is using data analytics to improve the efficiency of its production lines. The company has installed sensors on its equipment to collect data on production processes. This data is then analyzed to identify areas where efficiency can be improved. As a result of these efforts, Siemens has been able to reduce the time it takes to produce a product by 20%.

General Electric: General Electric is using data analytics to improve the quality of its products. The company has developed a system that uses data analytics to identify potential defects in products before they are shipped to customers. This system has helped General Electric to reduce the number of defects in its products by 50%.

Nike: Nike is using data analytics to improve the performance of its athletes. The company has developed a system that uses data analytics to track the performance of athletes during training. This data is then used to provide athletes with personalized training plans that help them to improve their performance.

Data analytics enables manufacturers to improve overall equipment effectiveness (OEE), optimize energy usage, perform root cause analysis, and achieve continuous improvement through lean manufacturing principles. By leveraging data-driven insights, manufacturers can enhance productivity, reduce costs, improve product quality, and drive sustainable growth.

The top 20 use cases of data analytics in manufacturing:

• Predictive maintenance: Leveraging data analytics to predict equipment failures and optimize maintenance schedules, reducing downtime and improving operational efficiency.
• Quality control and defect detection: Analyzing data from sensors, inspection systems, and production processes to identify patterns and anomalies, enabling early detection of quality issues and reducing waste.
• Supply chain optimization: Integrating data from various sources to gain end-to-end visibility into the supply chain, optimizing inventory levels, improving demand forecasting, and enhancing overall supply chain efficiency.
• Overall equipment effectiveness (OEE) improvement: Analyzing production data to identify opportunities for process optimization, reduce downtime, minimize waste, and improve production efficiency.
• Root cause analysis: Utilizing data analytics to perform in-depth analysis of production data, identifying underlying causes of issues or defects and implementing corrective actions.
• Energy management and sustainability: Analyzing energy consumption data to identify opportunities for energy savings, optimize resource utilization, and reduce environmental impact.
• Demand forecasting and planning: Leveraging historical and market data to predict demand trends, optimize production planning, and improve inventory management.
• Product lifecycle management: Utilizing data analytics to gain insights throughout the product lifecycle, from design and development to production and maintenance, improving product quality and time-to-market.
• Supplier performance analysis: Analyzing supplier data to assess performance, identify bottlenecks, and optimize supplier relationships, ensuring a reliable and efficient supply chain.
• Process optimization: Using data analytics to analyze process data and identify areas for improvement, optimizing production processes, and reducing variability.
• Real-time production monitoring: Implementing real-time data analytics to monitor production metrics, identify deviations, and enable timely interventions to maintain production efficiency.
• Warranty and service analytics: Analyzing warranty and service data to identify recurring issues, improve product reliability, and optimize service and maintenance strategies.
• Continuous improvement and lean manufacturing: Utilizing data analytics to identify opportunities for continuous improvement, reduce waste, and implement lean manufacturing principles.
• Asset utilization and optimization: Analyzing data from production equipment to optimize asset utilization, improve equipment reliability, and minimize downtime.
• Productivity and labor optimization: Leveraging data analytics to analyze labor data, identify productivity bottlenecks, and optimize workforce allocation and scheduling.
• Supply chain risk management: Applying data analytics to identify and mitigate supply chain risks, enabling proactive risk management strategies and ensuring business continuity.
• Regulatory compliance and reporting: Utilizing data analytics to ensure compliance with regulations, automate reporting processes, and improve accuracy and efficiency in compliance management.
• Customer sentiment analysis: Analyzing customer feedback and sentiment data to gain insights into product satisfaction, identify improvement areas, and enhance customer experience.
• Smart factory optimization: Leveraging IoT data and real-time analytics to optimize operations, monitor equipment performance, and enable data-driven decision-making in a smart factory environment.
• Continuous data-driven improvement: Implementing a culture of continuous improvement through data analytics, using insights to drive ongoing enhancements across all aspects of manufacturing operations.

The use cases of data analytics in manufacturing excellence are vast and diverse, ranging from predictive maintenance and quality control to supply chain optimization and process efficiency.

By harnessing advanced analytics techniques, machine learning algorithms, and real-time data, manufacturers can make informed decisions, proactively address issues, and drive innovation. Learn how a CEO Leads the Company to Success with Data-Driven Insights. The journey towards manufacturing excellence continues, and data analytics serves as a guiding light, empowering organizations to unlock their full potential, drive operational efficiency, and achieve remarkable outcomes.

Explore what Data Analytics use cases can be applied to Manufacturing, Finance, Marketing, Telecom, and Banking.

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Big data analytics for intelligent manufacturing systems: A review

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Top 10 Manufacturing Analytics Use Cases in 2024

IDC estimates that the global big data analytics market revenue would’ve reached ~$274B by 2022. This would have made manufacturing as one of the top 3 industries with largest analytics growth.

Manufacturing analytics is one of the critical steps for manufacturing digital transformation required for Industry 4.0 , which aims to:

• Automate traditional manufacturing processes
• Reduce costs
• Improve efficiency

What is manufacturing analytics?

Manufacturing analytics is the practice of capturing, cleansing, and analyzing machine data in order to predict their future use, prevent failures, forecast maintenance requirements, and identify areas for improvement. Manufacturing data includes all structured and unstructured information collected manually or by using software from machines and humans during every stage of production until a product is launched to the market.

What are the use cases of manufacturing analytics?

Manufacturing processes produce a large volume of data from:

• Machines: robotics, sensors, actuators, IoT devices, etc.
• Operators: ERP, sales, logistics, etc.

This data can be collected and analytics can be applied to it for:

Supply chain

1. demand forecasting.

Demand forecasting relies heavily on historical data about supply levels, material costs, purchase trends, and customer behavior. Manufacturers can leverage analytics to:

• define the types of products to be manufactured in a certain period
• define out of stock products
• calculate the number of products to be manufactured
• forecast sales opportunities

2. Inventory management

Forecasting demands enables manufacturers to manage their inventory, purchase materials, and optimize storage capacities in a data-driven manner. Analytics also provides insights about:

• sales to inventory ratio which represents the average inventory over the net sales
• days in inventory which is the number of days a manufacturer holds their product before selling it)
• gross margin return on inventory (GMROI) which indicates how much gross margin a manufacturer gets back for each dollar invested in inventory. 

Explore inventory management in more details.

3. Order management

Manufacturers can leverage predictive analytics to optimize the order management workflow by identifying products in demand, calculating the time required to build and ship every product, and defining the inventory needed to meet the demand for the finished product.

Explore order management in more details.

4. Maintenance optimization

Data collected from different manufacturing machines, tools, and devices, as well as data about operations and which machines they require, can be analyzed in order to:

• Predict when a machine will require maintenance based on time it’s been used and operations used in.
• Detect anomalies in operations which are caused by or will lead to machine failure.
• Prevent down time by planning machine breaks, fixes, or replacements.

Explore predictive maintenance in more details.

5. Risk management

Implementing analytics enables manufacturers to manage risks in a data-driven manner, such that they can:

• Determine recurring errors and prevent repetitive losses
• Predict insurance needs
• Monitor real-time machinery and operator work
• Identify real-time fails and system anomalies
• Plan risk management strategies

6. Price optimization

Leveraging analytics can help manufacturers understand the real price of a product based on the prices of materials, cost of operations, machines, and tools used or purchased for manufacturing. Additionally, manufacturers can leverage data about competitors, market trends, consumer behavior, and purchase history to optimize prices accordingly. Analytics can also help set dynamic prices which are based on demand, supply, competition price, and subsidiary product prices.

7. Automation and robotics

Analytics can provide an overall view of a manufacturing process, operation costs, as well as the number of operators and hours spent on a product. Large manufacturing firms can leverage these analyses to uncover automation or robotization opportunities which can reduce the time and cost of launching certain products

8. Transportation allocation

Manufacturers can leverage analytics on:

• Historical data : For predicting transportation time and vehicle requirements to deliver products to businesses or consumers.
• Real time data : For analyzing the impact of unplanned transportation events such as labor strikes or road works.

Product development

9. product progress measurement.

Based on historical data about the same or similar products, materials, machines, and tools used, as well as allocated employees for production, analytics can provide an estimation about the production process, when the product will be launched, which errors or pitfalls may be faced, and create a roadmap for the following procedures.

10. End user experience estimation

Product development teams can leverage analytics on product features, consumer behavior, and comments on online platforms, as well as competitor products, to estimate why end users buy certain products, when to launch similar products, and which features require optimization.

What other technologies are used in manufacturing?

Some of the technologies leveraged today by manufacturers include:

Robotic process automation (RPA)

RPA is a type of software capable of replicating human interactions with computers in order to automate repetitive processes. Manufacturers can leverage RPA for supply chain management and stock optimization.

To explore use cases, feel free to read our article about the benefits and top 8 use cases of RPA in manufacturing .

AI has numerous applications in manufacturing including:

• Digital twins and digital twin of an organization
• Augmented reality
• Demand forecasting
• Generative design
• Quality assurance
• Process optimization

To explore AI use cases in manufacturing, read our in-depth article top 12 use cases and applications of AI in manufacturing .

If you believe your business will benefit from manufacturing technologies, feel free to check our data-driven lists of vendors for:

• Manufacturing Analytics Software
• Manufacturing API
• IoT Analytics Platform
• RPA Software
• Web Crawler

And you can contact us to guide you through the process

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month. Cem's work has been cited by leading global publications including Business Insider , Forbes, Washington Post , global firms like Deloitte , HPE, NGOs like World Economic Forum and supranational organizations like European Commission . You can see more reputable companies and media that referenced AIMultiple. Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization. He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider . Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

To stay up-to-date on B2B tech & accelerate your enterprise:

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Top 6 digital twins use cases for manufacturing in 2024, 14 case studies of manufacturing analytics in 2024, how do businesses democratize analytics with ai in 2024.

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Big Data in Manufacturing: Unlocking Valuable Use Cases

The adoption of big data analytics in manufacturing.

The term “big data” refers to increasingly complex, massive data stores that can’t be effectively processed using traditional methods. In manufacturing, big data can refer to information collected from a variety of sources including machine sensor data, quality assurance information, data from suppliers, production output, maintenance, financial information, and basically any other measurable process that goes into modern manufacturing.

There’s a reason manufacturers are collecting such vast swathes of data about anything and everything. Big data can be processed and refined into business insights that can fuel massive financial growth, customer retention, savings on maintenance, warehousing, and unexpected downtime, and more.

Using the power of big data and manufacturing analytics , manufacturers are more easily able to add efficiency and productivity to their businesses while knowing that the moves they make are calculated and based on accurate data. This adds not only a boost to the probability of success, but also confidence in the ideas that are implemented.

Why is the Use of Data Growing in the Manufacturing Industry?

The answer to this question is two-fold. To make increasingly complex decisions and gain deeper insights, manufacturers are relying more and more on data from a variety of sources. As more data is collected from the shop floor and transformed into usable reports, data-driven decisions can be made that were simply not possible before.

Another reason the use of data is growing in the manufacturing industry is because it’s easier to access. The barrier to entry for implementing IIoT devices and smart factory equipment is at a historical low. Manufacturers can easily and affordably measure many aspects of their business, both in terms of data capture and data warehousing and storage. For example, with MachineMetrics, manufacturers can deploy plug-and-play solutions to provide immediate visibility into shop floor performance .

Additionally, modern markets push manufacturers toward the use of big data in order to stay flexible, efficient, and relevant to their target consumers, while remaining competitive in the marketplace. Data is unlocking the next step in manufacturers’ continuous improvement journey.

Big Data Use Cases in Manufacturing

Big data has a place in nearly every aspect of running a manufacturing business. Some of the most prominent use cases for big data in manufacturing include:

Machine Utilization

When machines are underutilized, manufacturers lose time, money, and opportunity. By analyzing data about when factory machines are utilized, manufacturers can clearly see which machines serve as bottlenecks, which are being underutilized, and which are being pushed to the brink of their capacities.

Machine Utilization Report from MachineMetrics.

Product Design

Big data can be used to gather information and inspiration about potential new products, augment understanding of how a product is actually used by customers to develop changes and improvements that better align with use expectations, as well as to determine product viability with greater ease and effectiveness.

Product Quality

Big data has been used with great success alongside machine learning to understand sentiment in customer reviews and support tickets to determine which points of failure are most frequent and frustrating for consumers. Big data can also be used for on-the-line quality control and quality assurance using technology to capture and report machine condition data .

Demand Forecasting

Big data offers manufacturers a peek into the future of what customers will want and when. By forecasting demand, manufacturers realize savings on warehouse costs, wasted supplies, and production time that could be otherwise spent elsewhere.

Customer Experience

Customers will feel more heard when their concerns are addressed. Big data offers the insight to not only address the concerns that arise but to spot and prevent future ones. Also, big data usually leads to higher quality products at lower costs and with speedier times for delivery.

Supply Chain Optimization

By analyzing supply chain data, manufacturers can cut costs both by finding cheaper suppliers and by bundling related products from single suppliers, boost quality, see and find solutions to logistics issues—such as snow storms and natural disasters—to continue business without missing a beat.

Benefits of Big Data In Manufacturing

Manufacturers who make effective use of big data see notable benefits to business from multiple angles. Since data can be applied in a broad manner, both use cases and benefits can be limitless, increasing in complexity and incremental value based on the "data-maturity" of the manufacturer. Some of the top benefits of big data in manufacturing that are usually the first to spur data collection and analysis include:

Competitive Advantage

Access to accurate, real-time production data allows for unprecedented business flexibility and agility that can even mesh with customer expectations. With insight into the shop floor, better decisions can be made with ease, giving manufacturers a strong advantage over less data-savvy competitors.

Predicting trends and being able to more quickly iterate on product design leads data-driven manufacturers to innovate better. In the same vein, time and money saved on supplies and production thanks to the use cases above means that manufacturers have more resources and flexibility to commit to innovation while still seeing success.

Lower Costs

Not buying excess supplies, optimizing warehouse space, finding the most cost-effective quality suppliers available, and circumventing logistical struggles all lead to cost savings. Further, machine maintenance that keeps equipment running smoothly reduces downtime and catastrophic ( and costly ) equipment failure .

Improved Customer Service

The ability to analyze customer data at every stage of their journey from marketing to sales to reviews on social media means that customers are able to receive top-notch, data-driven service that addresses their real wants, needs, and concerns.

Examples of Big Data in Manufacturing

One of our customers, BC Machining, utilizes data to understand when their machining tools are going to break. Via our High Frequency Data Adapter , BC Machining is able to extract high frequency data straight from the control of the machine. After building an algorithm, we are able to predict and prevent machine tool failures by monitoring certain thresholds.

This advanced big data use case has been able to eliminate nearly 100% of scrap parts. Furthermore, it has saved operator time, as BC Machining no longer needs to sort the scrap part, allowing both them and the machines to be focused on producing good parts and generating revenue for the company. The result has been $72,000 in annual savings per machine.

Other manufacturers use big data to keep factory floor workers on track through the use of visible stats that update in real-time. With this, workers are able to understand where they stand in relation to production goals, as well as react quickly to any problems on the shop floor, such as a downtime event.

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Data Analytics for Manufacturing: the Tesla’s Case Study (Part 1)

Posted by Thai Pham on Sun, Apr 30, 2017

If Tesla Motors is to achieve the aggressive goal of making 1 million cars by 2020 set out by its CEO Elon Musk, it will partly be because of the application of data analytics in its manufacturing operations.

Data analytics for manufacturing

• Blindness to data

Tableau’s advantages

Looking at data from different angles, process monitoring.

This April marked a historic moment for the automotive industry when Tesla briefly became the US’ most valuable carmaker. Its market capitalisation reached US$51 billion on April 10, surpassing US$50 billion of General Motors (GM) and US$45 billion of Ford.

Why do investors value Elon Musk’s electric car manufacturer more than GM and Ford even though Tesla’s 2016 sales volume was only 76,000 vehicles, a fraction of Ford’s (6.6 million) and GM’s (10 million), and it is still in the red, posting a net loss of US$675 million last year?

Musk’s bold vision for the future of transportation, in which all vehicles will eventually be all-electric and fully autonomous, certainly plays an important part. To make that vision a reality, CEO Elon Musk has set ambitious goals for Tesla for production capacity.

In 2013, Tesla delivered just around 20,000 Model S full-size sedans, the company’s first series production vehicle. 3 years later, the annual production rate reached 100,000. And Elon Musk is aiming to deliver 500,000 vehicles by 2018 and 1,000,000 by 2020.

Last December, the city of Fremont, California, approved Tesla’s plans to double the size of its factory there. There will be an additional 420,000 square meters of manufacturing space. And the number of workers is expected to increase from 6,000 to 9,000.

This massive expansion is crucial as Tesla’s product line continues to grow rapidly. Prior to 2015, the Fremont factory only built the Model S, which was later joined by the Model X SUV. Production of the more affordable Model 3 is scheduled to commence later this year. At present, Tesla has received over 370,000 pre-orders for Model 3.

Such rapid production growth and aggressive goals can undoubtedly create a lot of chaos. And Tesla is no stranger to missed deadlines. According to the Wall Street Journal, the company has failed to meet its projections more than 20 times in the past 5 years. As part of the ongoing effort to tame chaos and improve manufacturing efficiency, Tesla has turned to advanced data analytics software .

Back to the top

Tesla production data is fed into several different key systems. The first major one is in the MES (Manufacturing Execution System). This software essentially is the air traffic controller of the entire production process. It can tell a certain item in the assembly line where it should be heading, keep track of the production orders and quality issues, and collect basic measurements. The MES uses Oracle/SQL Server databases.

More in-depth and customised test data is stored in a separate MySQL Test database which was developed by Tesla’s in-house dev team. Additionally, the web-enabled QuickBase is also used to track process changes and ongoing quality issues due to its flexibility and ease of use. And finally, Excel spreadsheets are used for one-off manual reports. 

Not only does data go into many different places, but it also has to serve highly diverse audiences, who have very different interests. Whereas people working in Production care about yield and rework calculation, people in Equipment Sustaining may care about preventive maintenance, and people in Process Sustaining are interested in root cause investigation. Sometimes, people do not know where to get the data they need.

Read more: Business Intelligence in the Manufacturing Industry

‘Blindness to data’

Apart from a wide variety of databases and data reporting needs, there were plenty of different tools employed by Tesla staff. Most relied on Excel or an in-house app built on LabView, and a select few knew how to use MySQL Workbench or R.

This situation caused a lot of frustration when people had a hard time looking for the right data. Bottlenecks were created since only a handful of people had direct access to most data as well as the expertise to use advanced data analytics tools. And because the majority of staff had to use Excel for data handling, the process was slow and inefficient. Consequently, people were often unaware of what was going on in other parts of the production line.

The top data reporting needs are:

• Production counts and yield
• Quality defect tracking/analysis
• Statistical Process Control (SPC) and Process monitoring
• Root-cause investigation
• Open-ended data exploration
• Statistical analysis - DOE, GRR, etc.

It is worth noting that most of these reporting needs are heavy on data visualisation and data exploration rather than on statistics and scripting. This played an important part in Tesla’s decision to adopt Tableau data analytics software .

Read more: 3 Best Business Intelligence & Analytics Vendors 2017

One distinct advantage of Tableau is “data source agnostic”, i.e. users no longer have to care where the data comes from. They can pull any data from any source at any time. They can view data located in MES, Test DB, QuickBase or Excel files side-by-side on the same dashboard.

Tableau also allows ordinary users, even those who are not programmers, to easily create powerful and tailored visualisations. Before that, they would have had to spend at least 6 months learning R to build such data visualisations.

Another huge improvement brought about by Tableau is publishing and sharing. In the past, those capable of using analytics tools often had to export their works and send them to others via email. This approach was time-consuming and lacked interactions. Conversely, Tableau users can easily publish their works and let others interact with them on their web browsers.

In the next sections, we will explore some applications of Tableau in building production reporting tools at the Tesla factory.

To have a complete picture, people need to be able to look at and drill down data from multiple angles. Then trends and patterns are much easier to detect.

The following visualisation workbook shows 3 different views of a single set of data: the number of quality issues.

The top left visualisation shows the defect counts over time. The top right visualisation counts the number of defects by work centres. And at the bottom right corner is the visualisation of defects by categories.

Users from Process Sustaining use this workbook in their day-to-day jobs. They can drill down the data in any way they want. For instance, they can easily see the number of defects in each work centre by dates and by categories just by clicking on that particular work centre.

Then they can filter down even further by clicking on a defect category and see the number of that specific defect type over time.

The bottom left corner is where users can see the detailed information on each quality defect, including notes from individuals who conducted the inspection. So users can perform a context drill-down from high-level data to individual serial numbers on the same screen.

The following dashboard monitors the number of false part rejects, which happen when parts are scanned by barcode scanners and falsely rejected. This could be due to several reasons. The parts may not be held at the right angle towards the scanners or the scanners are misaligned. 

The following dashboard shows data from 19 work centres (MAC01 to MAC19); each has 10 nests (A to J) where the barcodes are scanned. The false reject rates are represented by colours. A green nest means the false reject rate is within the limit.

Users can click on one particular work centre and see the trends of false rejects of individual nests over time. More important, they can pull equipment logs of the same period of time from a separate database and see if someone has already logged the scanners down and alerted the maintenance crew and if the false reject rate of a scanner has dropped after it was repaired.

In this particular case, there were 2 maintenance alerts put in for the scanner of nest B / work centre MAC14. And the rate has gone down after the 2 nd repair.

The point of such process monitoring is to be able to see not only what is going on but also the deeper context of the actions taken.

This is the 1 st part of our article. Please  read the second part  or  subscribe to our blog for the latest content about Business Intelligence and Analytics. 

You can also request a Tableau demo to see how this solution can help your business.

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The Impact of Digitalization on Production Management Practices: A Multiple Case Study

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• First Online: 26 April 2024
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• Ruggero Colombari 6 ,
• Jasmina Berbegal Mirabent 7 &
• Paolo Neirotti 8  

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 206))

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• International Conference on Industrial Engineering and Industrial Management (ICIEIM) – Congreso de Ingeniería de Organización

With the diffusion of Industry 4.0, manufacturing firms can decentralize their operational decisions and enable real-time data-driven decision-making. Using a socio-technical approach and the manufacturing shop-floor as a unit of analysis, this article studies the changes induced by digitalization on operational decision-making, organizational structures, and individual competencies. A cross-country multiple case study conducted in the automotive sector suggests three main areas on which firms have to focus: decentralized data-driven decision-making, front-line managers’ upskilling, and production workers’ involvement. The successful implementation of digitalization and the actual decentralization of decision-making depend on individual factors related to the competencies of front-line managers, who acquire a central role in this skill-biased technological and organizational change.

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Ruggero Colombari

Dept. d’Organització d’Empreses. Escola Politècnica Superior d’Enginyeria, Universitat Politècnica de Catalunya, Av Victor Balaguer, s/n, 08800, Vilanova i la Geltrú, Spain

Jasmina Berbegal Mirabent

Dipartimento di Ingegneria Gestionale e della Produzione (DIGEP), Politecnico di Torino , Corso Duca degli Abruzzi, 24, 10129, Torino, Italia

Paolo Neirotti

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Colombari, R., Berbegal Mirabent, J., Neirotti, P. (2024). The Impact of Digitalization on Production Management Practices: A Multiple Case Study. In: Bautista-Valhondo, J., Mateo-Doll, M., Lusa, A., Pastor-Moreno, R. (eds) Proceedings of the 17th International Conference on Industrial Engineering and Industrial Management (ICIEIM) – XXVII Congreso de Ingeniería de Organización (CIO2023). CIO 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 206. Springer, Cham. https://doi.org/10.1007/978-3-031-57996-7_44

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Data Analytics in Manufacturing

Empowering Industry Transformation through Data-Driven Insights

ScatterPie Analytics empowers manufacturers with advanced analytics, AI, and ML solutions to optimize efficiency and scalability in today’s dynamic manufacturing landscape.

Why Choose Scatterpie?

Official Tableau Partner

Extensive experience of working with fortune manufacturing giants, projects completed, experienced team of data engineers & data analytics, data challenges.

Operational Inefficiencies

Data analytics identifies the inefficiencies in manufacturing processes, streamlining operations for enhanced efficiency and productivity.

Quality Control

Real-time data analytics ensures consistent product quality by detecting issues early and adhering to quality standards.

Demand Forecasting

Accurate demand forecasts optimize production, inventory management, and resource allocation.

Supply Chain Complexity

Data analytics optimizes supply chain performance, improving collaboration with suppliers and streamlining logistics.

Equipment Maintenance & Downtime

Predictive maintenance reduces downtime, improves equipment utilization, and extends lifespan.

Compliance and Risk Management

Data analytics monitors compliance, identifies risks, and develops risk mitigation strategies.

Manufacturing Data Analytics Solutions

Drive efficiency, productivity, and growth with analytics in manufacturing.

At Scatterpie Analytics, we go beyond generating mere data analysis reports. We provide manufacturers with actionable insights that empower decision-making at all levels of the organization. Our end-to-end analytics services offer the following advantages:

Real-Time Visibility:

Manufacturers gain real-time visibility into critical production metrics, inventory levels, and supply chain performance, enabling them to make informed decisions and respond swiftly to market dynamics.

Performance Dashboards:

Through intuitive dashboards and reports, executives gain access to key performance indicators (KPIs), enabling them to monitor progress, identify trends, and drive strategic initiatives with confidence.

Predictive Analytics:

Our advanced predictive analytics models empower manufacturers to forecast demand, optimize production schedules, and mitigate potential bottlenecks, ensuring timely delivery and superior customer satisfaction.

Prescriptive Analytics

By leveraging prescriptive analytics, manufacturers can optimize pricing strategies, identify cost-saving opportunities, and improve resource allocation, maximizing profitability and competitive advantage.

Data-Driven Continuous Improvement

With our analytics insights, manufacturers can foster a culture of continuous improvement, identifying process inefficiencies, optimizing workflows, and driving innovation throughout the organization.

AI-Driven Quality and Safety Solutions:

We help manufacturers harness the power of AI-driven analytics to revolutionize quality control and safety in manufacturing. Companies can leverage advanced algorithms to detect anomalies, enhance defect detection, and ensure a safe working environment, driving unparalleled quality and safety standards.

Our Manufacturing Clientele

Our Partner Technology

Empower Your Manufacturing with Data Analytics

Featured insights.

• Pharmaceuticals Industry
• Cement Indusrty
• Tyre Industry

Enhance Efficiency: Supply Chain Analytics in Manufacturing

July 27, 2023

How Predictive Analytics in Manufacturing is Transforming Operations

Reasons to Add Analytics in Manufacturing

April 20, 2023

A Deep Dive into Comparing and Benchmarking KPI

April 14, 2023

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How Big Data Analytics in Manufacturing Strengthens the Industry?

Manufacturing remains a critical component of the world’s economic engine, but the role it plays in advanced and developing economies has transferred dramatically. According to a report by  Research and Markets , the manufacturing industry market value was $904.65 million in 2019 and is expected to reach $4.55 billion in 2025. With big data analytics in manufacturing, manufacturers can uncover the latest information and recognize patterns that allow them to enhance processes, boost supply chain efficiency and determine variables that impact production.

Top leaders in manufacturing companies understand the significance of the process. A KRC research study found that 67% of manufacturing executives thought to invest in data analytics, even in the aspect of pressure, to reduce costs in this unpredictable market.

To comprehend big data analytics in manufacturing and its consequences, let us dive into how its intervention improves and modernizes the operations.

Acquiring Asset Performance and Productivity Increase

Since manufacturing profits depend wearily on maximizing the value of assets, performance increases can impact huge productivity enhancements even if it is only enhanced on the margins. Similarly, a decrease in asset breakdowns can reduce inefficiencies and prevent losses. For these purposes, manufacturers concentrate on maintenance and constantly optimize asset performance.

This data potentially is of great value to manufacturers, but many are surprised by the sheer volume of incoming data. Data analytics can help them captivate, clean, and interpret machine data to reveal insights that can help them improve performance.

In addition to allowing historical data analysis, Big Data can propel predictive analytics, which manufacturers can use to drive predictive maintenance. This enables manufacturers to  prevent expensive asset breakdowns  and dodge unexpected downtime.

Case Study: AI & ML Solution to Predict the Engine Leakage Failure

Creating feasible product customization.

Traditionally, manufacturing focuses on production at range and allows product customization to enterprises serving the niche market. In the past, it did not make sense to customize because of the time and effort engaged to request a smaller group of customers.

Big Data analytics is evolving by making it possible to assume the demand for customized products precisely. By identifying the changes in customer behavior, Big Data Analytics can allow manufacturers to produce customized products almost as effectively as goods offered at a greater scale. Innovative capabilities include tools that enable product engineers to  collect, analyze and visualize customer feedback  in near-real-time.

By providing manufacturers with the tools they want to deep dive into processes, Big Data Analytics enables them to distinguish points within the production process where they can successfully include custom processes using in-house capabilities or delay production to facilitate partners to perform customization before completing the manufacturing process.

Increasing Supply Chains & Production Processes

In this evolving global and interconnected environment, manufacturing processes and supply chains are deep and complicated. Efforts to modernize the processes and optimize supply chains must be maintained by the ability to analyze every process component and supply chain in coarse detail. Big Data Analytics provides manufacturers this capability.

With the right analytics, manufacturers can zero in on every section of the production process and monitor supply chains in exact detail, considering every individual activity and task. This capability to narrow the focus enables manufacturers to identify bottlenecks and reveal underperforming components and processes. Big Data Analytics also unveils dependencies, empowering manufacturers to strengthen production processes and generate alternative plans to discuss potential pitfalls.

Top Manufacturing Big Data Analytics Tools

Check out some top-notch tools that manufacturers are successfully using today to optimize asset performance, enhance production processes and alleviate product customization. Here is a brief overview of quintessential Big Data Analytics tools:

Apache Hadoop: Apache Hadoop is a software framework utilized for collected file systems and managing big data. It processes datasets of big data using the MapReduce programming model. Hadoop is an open-source framework that is coded in Java and provides cross-platform support.

Cloudera: CDH (Cloudera Distribution for Hadoop) points at enterprise-class deployments of that technology. It is completely open-source and has a free platform distribution that incorporates Apache Hadoop, Apache Spark, Apache Impala, and many more. It enables you to gather, process, manage, distribute, discover, model, and share unlimited data.

KNIME: KNIME stands for Konstanz Information Miner which is an open-source tool utilized for Enterprise reporting, integration, analysis, CRM, data analytics, and business intelligence. It supports Linux, OS X, and Windows operating systems.

Xplenty: Xplenty is a platform to combine, process, and provide data for analytics on the cloud. It fetches all your data sources together. Its inherent graphic interface will help you with executing ETL, ELT solutions. Xplenty is a comprehensive toolkit for building data pipelines with low-code and no-code capabilities.

Datawrapper: Datawrapper is an open-source platform for data visualization that supports its users to create easy, precise, and integrated charts immediately.

Tableau: Tableau is a software solution for business intelligence and analytics which offers a wide array of integrated products in visualizing and interpreting their data. Tableau is proficient in managing all data sizes and is simple for technical and non-technical customer base and provides real-time customized dashboards.

• Open studio for Big data:  It appears with a free and open-source license. Its components and connectors are Hadoop and NoSQL.
• Big data platform:  It has a user-based subscription license. Its components and connectors are MapReduce and Spark.
• Real-time Big data platform:  It has a user-based subscription license. Its components and connectors include Spark streaming, Machine learning, and IoT.

RapidMiner: Rapidminer is a cross-platform tool that allows an integrated environment for data science, predictive analytics, and machine learning. It comes under various licenses that offer small, medium, and large established editions as well as a free edition that provides for 1 logical processor and up to 10,000 data rows.

Conclusion on Big Data:

With the proper data integration and management platform, manufacturers can finally leverage the data’s strategic value, enhance operations, gain profits and strengthen relationships with customers, suppliers and partners. Establishing Big Data to work has never been more critical, and the time to make the data integration and management tools to unlock data’s value is now present.

Want to know how big data analytics in manufacturing helps organization to gain profits and strengthen relationships with customers? Let’s connect and discuss .

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Data Analytics Case Study Guide 2024

by Sam McKay, CFA | Data Analytics

Data analytics case studies reveal how businesses harness data for informed decisions and growth.

For aspiring data professionals, mastering the case study process will enhance your skills and increase your career prospects.

So, how do you approach a case study?

Use these steps to process a data analytics case study:

Understand the Problem: Grasp the core problem or question addressed in the case study.

Collect Relevant Data: Gather data from diverse sources, ensuring accuracy and completeness.

Apply Analytical Techniques: Use appropriate methods aligned with the problem statement.

Visualize Insights: Utilize visual aids to showcase patterns and key findings.

Derive Actionable Insights: Focus on deriving meaningful actions from the analysis.

This article will give you detailed steps to navigate a case study effectively and understand how it works in real-world situations.

By the end of the article, you will be better equipped to approach a data analytics case study, strengthening your analytical prowess and practical application skills.

Let’s dive in!

Table of Contents

What is a Data Analytics Case Study?

A data analytics case study is a real or hypothetical scenario where analytics techniques are applied to solve a specific problem or explore a particular question.

It’s a practical approach that uses data analytics methods, assisting in deciphering data for meaningful insights. This structured method helps individuals or organizations make sense of data effectively.

Additionally, it’s a way to learn by doing, where there’s no single right or wrong answer in how you analyze the data.

So, what are the components of a case study?

Key Components of a Data Analytics Case Study

A data analytics case study comprises essential elements that structure the analytical journey:

Problem Context: A case study begins with a defined problem or question. It provides the context for the data analysis , setting the stage for exploration and investigation.

Data Collection and Sources: It involves gathering relevant data from various sources , ensuring data accuracy, completeness, and relevance to the problem at hand.

Analysis Techniques: Case studies employ different analytical methods, such as statistical analysis, machine learning algorithms, or visualization tools, to derive meaningful conclusions from the collected data.

Insights and Recommendations: The ultimate goal is to extract actionable insights from the analyzed data, offering recommendations or solutions that address the initial problem or question.

Now that you have a better understanding of what a data analytics case study is, let’s talk about why we need and use them.

Why Case Studies are Integral to Data Analytics

Case studies serve as invaluable tools in the realm of data analytics, offering multifaceted benefits that bolster an analyst’s proficiency and impact:

Real-Life Insights and Skill Enhancement: Examining case studies provides practical, real-life examples that expand knowledge and refine skills. These examples offer insights into diverse scenarios, aiding in a data analyst’s growth and expertise development.

Validation and Refinement of Analyses: Case studies demonstrate the effectiveness of data-driven decisions across industries, providing validation for analytical approaches. They showcase how organizations benefit from data analytics. Also, this helps in refining one’s own methodologies

Showcasing Data Impact on Business Outcomes: These studies show how data analytics directly affects business results, like increasing revenue, reducing costs, or delivering other measurable advantages. Understanding these impacts helps articulate the value of data analytics to stakeholders and decision-makers.

Learning from Successes and Failures: By exploring a case study, analysts glean insights from others’ successes and failures, acquiring new strategies and best practices. This learning experience facilitates professional growth and the adoption of innovative approaches within their own data analytics work.

Including case studies in a data analyst’s toolkit helps gain more knowledge, improve skills, and understand how data analytics affects different industries.

Using these real-life examples boosts confidence and success, guiding analysts to make better and more impactful decisions in their organizations.

But not all case studies are the same.

Let’s talk about the different types.

Types of Data Analytics Case Studies

Data analytics encompasses various approaches tailored to different analytical goals:

Exploratory Case Study: These involve delving into new datasets to uncover hidden patterns and relationships, often without a predefined hypothesis. They aim to gain insights and generate hypotheses for further investigation.

Predictive Case Study: These utilize historical data to forecast future trends, behaviors, or outcomes. By applying predictive models, they help anticipate potential scenarios or developments.

Diagnostic Case Study: This type focuses on understanding the root causes or reasons behind specific events or trends observed in the data. It digs deep into the data to provide explanations for occurrences.

Prescriptive Case Study: This case study goes beyond analytics; it provides actionable recommendations or strategies derived from the analyzed data. They guide decision-making processes by suggesting optimal courses of action based on insights gained.

Each type has a specific role in using data to find important insights, helping in decision-making, and solving problems in various situations.

Regardless of the type of case study you encounter, here are some steps to help you process them.

Roadmap to Handling a Data Analysis Case Study

Embarking on a data analytics case study requires a systematic approach, step-by-step, to derive valuable insights effectively.

Here are the steps to help you through the process:

Step 1: Understanding the Case Study Context: Immerse yourself in the intricacies of the case study. Delve into the industry context, understanding its nuances, challenges, and opportunities.

Identify the central problem or question the study aims to address. Clarify the objectives and expected outcomes, ensuring a clear understanding before diving into data analytics.

Step 2: Data Collection and Validation: Gather data from diverse sources relevant to the case study. Prioritize accuracy, completeness, and reliability during data collection. Conduct thorough validation processes to rectify inconsistencies, ensuring high-quality and trustworthy data for subsequent analysis.

Step 3: Problem Definition and Scope: Define the problem statement precisely. Articulate the objectives and limitations that shape the scope of your analysis. Identify influential variables and constraints, providing a focused framework to guide your exploration.

Step 4: Exploratory Data Analysis (EDA): Leverage exploratory techniques to gain initial insights. Visualize data distributions, patterns, and correlations, fostering a deeper understanding of the dataset. These explorations serve as a foundation for more nuanced analysis.

Step 5: Data Preprocessing and Transformation: Cleanse and preprocess the data to eliminate noise, handle missing values, and ensure consistency. Transform data formats or scales as required, preparing the dataset for further analysis.

Step 6: Data Modeling and Method Selection: Select analytical models aligning with the case study’s problem, employing statistical techniques, machine learning algorithms, or tailored predictive models.

In this phase, it’s important to develop data modeling skills. This helps create visuals of complex systems using organized data, which helps solve business problems more effectively.

Understand key data modeling concepts, utilize essential tools like SQL for database interaction, and practice building models from real-world scenarios.

Furthermore, strengthen data cleaning skills for accurate datasets, and stay updated with industry trends to ensure relevance.

Step 7: Model Evaluation and Refinement: Evaluate the performance of applied models rigorously. Iterate and refine models to enhance accuracy and reliability, ensuring alignment with the objectives and expected outcomes.

Step 8: Deriving Insights and Recommendations: Extract actionable insights from the analyzed data. Develop well-structured recommendations or solutions based on the insights uncovered, addressing the core problem or question effectively.

Step 9: Communicating Results Effectively: Present findings, insights, and recommendations clearly and concisely. Utilize visualizations and storytelling techniques to convey complex information compellingly, ensuring comprehension by stakeholders.

Step 10: Reflection and Iteration: Reflect on the entire analysis process and outcomes. Identify potential improvements and lessons learned. Embrace an iterative approach, refining methodologies for continuous enhancement and future analyses.

This step-by-step roadmap provides a structured framework for thorough and effective handling of a data analytics case study.

Now, after handling data analytics comes a crucial step; presenting the case study.

Presenting Your Data Analytics Case Study

Presenting a data analytics case study is a vital part of the process. When presenting your case study, clarity and organization are paramount.

To achieve this, follow these key steps:

Structuring Your Case Study: Start by outlining relevant and accurate main points. Ensure these points align with the problem addressed and the methodologies used in your analysis.

Crafting a Narrative with Data: Start with a brief overview of the issue, then explain your method and steps, covering data collection, cleaning, stats, and advanced modeling.

Visual Representation for Clarity: Utilize various visual aids—tables, graphs, and charts—to illustrate patterns, trends, and insights. Ensure these visuals are easy to comprehend and seamlessly support your narrative.

Highlighting Key Information: Use bullet points to emphasize essential information, maintaining clarity and allowing the audience to grasp key takeaways effortlessly. Bold key terms or phrases to draw attention and reinforce important points.

Addressing Audience Queries: Anticipate and be ready to answer audience questions regarding methods, assumptions, and results. Demonstrating a profound understanding of your analysis instills confidence in your work.

Integrity and Confidence in Delivery: Maintain a neutral tone and avoid exaggerated claims about findings. Present your case study with integrity, clarity, and confidence to ensure the audience appreciates and comprehends the significance of your work.

By organizing your presentation well, telling a clear story through your analysis, and using visuals wisely, you can effectively share your data analytics case study.

This method helps people understand better, stay engaged, and draw valuable conclusions from your work.

We hope by now, you are feeling very confident processing a case study. But with any process, there are challenges you may encounter.

Key Challenges in Data Analytics Case Studies

A data analytics case study can present various hurdles that necessitate strategic approaches for successful navigation:

Challenge 1: Data Quality and Consistency

Challenge: Inconsistent or poor-quality data can impede analysis, leading to erroneous insights and flawed conclusions.

Solution: Implement rigorous data validation processes, ensuring accuracy, completeness, and reliability. Employ data cleansing techniques to rectify inconsistencies and enhance overall data quality.

Challenge 2: Complexity and Scale of Data

Challenge: Managing vast volumes of data with diverse formats and complexities poses analytical challenges.

Solution: Utilize scalable data processing frameworks and tools capable of handling diverse data types. Implement efficient data storage and retrieval systems to manage large-scale datasets effectively.

Challenge 3: Interpretation and Contextual Understanding

Challenge: Interpreting data without contextual understanding or domain expertise can lead to misinterpretations.

Solution: Collaborate with domain experts to contextualize data and derive relevant insights. Invest in understanding the nuances of the industry or domain under analysis to ensure accurate interpretations.

Challenge 4: Privacy and Ethical Concerns

Challenge: Balancing data access for analysis while respecting privacy and ethical boundaries poses a challenge.

Solution: Implement robust data governance frameworks that prioritize data privacy and ethical considerations. Ensure compliance with regulatory standards and ethical guidelines throughout the analysis process.

Challenge 5: Resource Limitations and Time Constraints

Challenge: Limited resources and time constraints hinder comprehensive analysis and exhaustive data exploration.

Solution: Prioritize key objectives and allocate resources efficiently. Employ agile methodologies to iteratively analyze and derive insights, focusing on the most impactful aspects within the given timeframe.

Recognizing these challenges is key; it helps data analysts adopt proactive strategies to mitigate obstacles. This enhances the effectiveness and reliability of insights derived from a data analytics case study.

Now, let’s talk about the best software tools you should use when working with case studies.

Top 5 Software Tools for Case Studies

In the realm of case studies within data analytics, leveraging the right software tools is essential.

Here are some top-notch options:

Tableau : Renowned for its data visualization prowess, Tableau transforms raw data into interactive, visually compelling representations, ideal for presenting insights within a case study.

Python and R Libraries: These flexible programming languages provide many tools for handling data, doing statistics, and working with machine learning, meeting various needs in case studies.

Microsoft Excel : A staple tool for data analytics, Excel provides a user-friendly interface for basic analytics, making it useful for initial data exploration in a case study.

SQL Databases : Structured Query Language (SQL) databases assist in managing and querying large datasets, essential for organizing case study data effectively.

Statistical Software (e.g., SPSS , SAS ): Specialized statistical software enables in-depth statistical analysis, aiding in deriving precise insights from case study data.

Choosing the best mix of these tools, tailored to each case study’s needs, greatly boosts analytical abilities and results in data analytics.

Final Thoughts

Case studies in data analytics are helpful guides. They give real-world insights, improve skills, and show how data-driven decisions work.

Using case studies helps analysts learn, be creative, and make essential decisions confidently in their data work.

Check out our latest clip below to further your learning!

Frequently Asked Questions

What are the key steps to analyzing a data analytics case study.

When analyzing a case study, you should follow these steps:

Clarify the problem : Ensure you thoroughly understand the problem statement and the scope of the analysis.

Make assumptions : Define your assumptions to establish a feasible framework for analyzing the case.

Gather context : Acquire relevant information and context to support your analysis.

Analyze the data : Perform calculations, create visualizations, and conduct statistical analysis on the data.

Provide insights : Draw conclusions and develop actionable insights based on your analysis.

How can you effectively interpret results during a data scientist case study job interview?

During your next data science interview, interpret case study results succinctly and clearly. Utilize visual aids and numerical data to bolster your explanations, ensuring comprehension.

Frame the results in an audience-friendly manner, emphasizing relevance. Concentrate on deriving insights and actionable steps from the outcomes.

How do you showcase your data analyst skills in a project?

To demonstrate your skills effectively, consider these essential steps. Begin by selecting a problem that allows you to exhibit your capacity to handle real-world challenges through analysis.

Methodically document each phase, encompassing data cleaning, visualization, statistical analysis, and the interpretation of findings.

Utilize descriptive analysis techniques and effectively communicate your insights using clear visual aids and straightforward language. Ensure your project code is well-structured, with detailed comments and documentation, showcasing your proficiency in handling data in an organized manner.

Lastly, emphasize your expertise in SQL queries, programming languages, and various analytics tools throughout the project. These steps collectively highlight your competence and proficiency as a skilled data analyst, demonstrating your capabilities within the project.

Can you provide an example of a successful data analytics project using key metrics?

A prime illustration is utilizing analytics in healthcare to forecast hospital readmissions. Analysts leverage electronic health records, patient demographics, and clinical data to identify high-risk individuals.

Implementing preventive measures based on these key metrics helps curtail readmission rates, enhancing patient outcomes and cutting healthcare expenses.

This demonstrates how data analytics, driven by metrics, effectively tackles real-world challenges, yielding impactful solutions.

Why would a company invest in data analytics?

Companies invest in data analytics to gain valuable insights, enabling informed decision-making and strategic planning. This investment helps optimize operations, understand customer behavior, and stay competitive in their industry.

Ultimately, leveraging data analytics empowers companies to make smarter, data-driven choices, leading to enhanced efficiency, innovation, and growth.

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