According to McKinsey & Co., big data could be worth tens of billions of dollars for Lean manufacturers in the automotive, chemical, FMCG and pharmaceutical industries, among others.1 This article examines several case studies to reveal why Lean Manufacturers are adding big data to their Lean toolkits and the benefits of this combination.
Larger data sets, faster computational power, and more sophisticated analytics tools enable remarkable progress on a range of priorities for Lean practitioners. Big data applications will be finding their way into the Lean toolkits of large manufacturers across a range of industries globally, as the diagram below demonstrates.
Sophisticated modeling can truly expand our horizons when it comes to identifying waste and determining continuous improvement initiatives. A well-implemented Manufacturing Execution System (MES) generates precise, real-time data with incredible accuracy on downtime, waste, and WIP – the same issues Lean looks to minimize – as well as meaningful context around that data.
Let’s examine a few big data case studies in the manufacturing industry.
1. Increasing Yield by Improving Process
A biopharmaceutical company was using live, genetically engineered cells and tracking 200 variables to measure the purity of its manufacturing process for vaccines and blood components.2 However, they found that two batches of the same substance manufactured using identical processes showed a yield variation from 50 to 100 percent. The inconsistency in capacity and quality was a problem; for one thing, it could attract regulatory attention.
The company decided to segment its manufacturing processes into areas of activity. Using big data analytics the team then assessed process interdependencies and identified nine constraints that directly impacted vaccine yield. By modifying target processes the company was able to increase vaccine production by 50 percent, resulting in savings between $5 and $10 million annually.
2. Customizing Product Design
One $2 billion company generates most of its revenue by manufacturing products to order.3 Using big data analytics they analysed the behaviour of repeat customers as this is critical to understanding how to deliver goods in a timely and profitable manner.
Much of the analyses focused on how to ensure strong contracts were in place. The company also shifted to Lean manufacturing to determine which products were viable or redundant.
3. Advancing Quality Assurance
Intel has been harnessing big data for its processor manufacturing for some time. It has to test every single chip that comes off its production line. Before utilizing big data, each chip would have to go through 19,000 tests.
Using big data for predictive analytics, Intel was able to significantly reduce the number of tests required for quality assurance. By analysing the data from manufacturing processes, they were able to cut down the testing time and focus on specific tests.
The result was a saving of $3 million in manufacturing costs for a single line of Intel Core processors. By expanding big data use in its chip manufacturing, the company expects to save an additional $30 million.
4. Removing Waste
A leading steel producer and 15-year Lean veteran used advanced analytics to identify margin improvement opportunities worth more than $200 million a year across its production value chain. Using a Monte Carlo simulation, the steelmaker ran thousands of simulations using historical plant data to develop a more complex picture of its processes. The simulations uncovered two previously unknown bottlenecks that had the potential to cripple outlook.
With this new insight, the company ran structured problem-solving exercises to find new and more economical ways of making improvements. After improving the availability of three key pieces of equipment, the steelmaker saw a 20 percent throughput increase that translated into more than $50 million in EBITDA improvements.
Another analytical tool the steelmaker employed was value-in-use modeling, more commonly used in procurement, to optimize the purchasing of raw materials.
"Lean Data" doesn’t only have the potential to radically improve processes, it can play an important role in the formation of Lean cultures. With the ability to solve previously unsolvable problems and make better operational decisions in real time, Lean businesses can grow into Lean powerhouses, particularly if they use the benefits of big data to encourage frontline decision making and empower the workforce.
Driving data-related issues at lower levels of the business reinforce a strong culture of continuous improvement, a scientific mindset and constant customer focus. It empowers Lean operators by providing clarity on waste and improvement opportunities, helping them focus exclusively on value-added activities.
But for Lean and big data to work together, most organisations have had to adjust their habitual approach to kaizen, the philosophy of continuous improvement. The most popular strategy is to set up special data-optimization labs or small teams of econometrics specialists and statisticians within organisational units to help frontline colleagues identify opportunities for improvement projects and teach others how to apply their Lean problem-solving skills in original ways.
Finally, big data can help Lean leaders find the answer to two of the most vexing questions at the core of everything they do:
- Who is buying what, when, and at what price?
- How can we connect what consumers hear, read and view to what they buy and consume?
By leveraging big data, Lean businesses can turn to more strategic questions about longer-term customer stickiness, loyalty, and relationships. The question is no longer what will trigger the next purchase, but what will ensure customer loyalty to a brand, even when a competitor offers a better price.
As companies are getting better at storing, sharing, integrating and understanding their data more quickly and confidently, the power of data is also spreading into other related areas of the business, such as quality and production planning, reinforcing Lean thinking across all functional areas.
As the examples above suggest, the key to applying advanced analytics in Lean environments is to view data holistically and through the lens of continuous improvement.
For this reason, senior executives must develop true knowledge of how to leverage big data, not just a superficial understanding of the various analytics models available. The information and data required for many big data initiatives most likely exist in silos within companies already – in shop-floor production logs, maintenance registers, real-time equipment performance data, even vendor performance-guarantee sheets.
So it’s not a question of where to find data, but how to use it. Without a strong understanding of how to effectively handle data – how to determine what to look for, where to get it, and how to use it across a dispersed manufacturing network – big data can create more confusion than clarity.
Sources and Citations
- When Big Data Goes Lean, McKinsey Quarterly, February 2014 - http://www.mckinsey.com/business-functions/operations/our-insights/when-big-data-goes-lean
- How big data can improve manufacturing - http://www.mckinsey.com/business-functions/operations/our-insights/how-big-data-can-improve-manufacturing
- Big Data Use Cases in the Manufacturing Industry, Ingram Micro Advisor - http://www.ingrammicroadvisor.com/data-center/4-big-data-use-cases-in-the-manufacturing-industry