How are big data and lean management connected?
According to research, amalgamating the principles of lean management and advanced analytics could generate billions of dollars in additional profits for corporations.
Lean management principles are increasingly supported by applying larger, more complex datasets, powerful IT infrastructures, and advanced analytic protocols. These systems enable organizations to identify areas where they can save, empowering staff to apply streamlined problem-solving techniques. This increasingly detailed, efficient approach to business allows organizations to continuously improve and increase profit margins.
For instance, advanced analytics can reveal previously insuperable or unknown efficiency obstacles in multiplex manufacturing frameworks. When deployed, data analysis can identify bottlenecks, inflexibility, and inconsistencies in the production line, driving efficiency and reliability. Subsequently, these benefits can permeate related operations, including quality control, planning, and cross-departmental strategy. Here, we discuss how big data and lean management impact business operations and drive profit.
Why and how major manufacturers have implemented big data programs
Several major corporations began extracting innovation from big data almost 30 years ago. These pioneers of a data-driven approach drew inspiration from the financial sector, applying principles from risk management to manufacturing processes. For instance, leaders in natural resources implemented this approach in the 1990s when the market price of raw materials fell. In contrast, these industries have had to develop an agile response to rising commodity prices in the 2000s.
Thanks to big data, these companies have enjoyed rising profits in the face of adversity with many reporting an additional 2–3% rise in profit margins. When added to previous productivity gains, this accounted for up to an extra 10–15% increase in profit. This illustrates the important role advanced analytics play in lean management strategy in manufacturing. As more management consultants are beginning to find, big data represents billions of dollars in savings for several industries. Furthermore, there is enormous potential for executives to import these principles back into the service sector to generate huge savings.
Despite the clear benefits of these strategies, companies need to adjust their internal cultures. Many businesses benefit from establishing a dedicated data team. By forming a small working group of data scientists, statisticians, economists, and business management experts, companies can develop relevant analytic tools. From here, this research can be fed down to frontline staff, empowering these employees to drive improvement. This will not only enhance profit performance but also help staff develop their problem-solving skills.
Case studies that illustrate the synergy between big data and lean management
There are numerous examples from manufacturing that illustrate the value of marrying lean management with big data. For instance, a leading pharmaceuticals company wanted to reduce variation in their production line. Management identified that there were over 50 variables in the mix, but could not determine the connection between the disparate elements. By collaborating with data scientists, management used artificial intelligence to model the potential scenarios and associated outcomes. From these analyses, the team identified five key drivers. Once staff could pinpoint the main variables, they could better manage operations and streamline production. As a result, the company yield increased by 30%.
A further case study from the steel industry proves the benefits of combining lean management and data analysis. After 15 years of applying lean principles, a major steel manufacturer used big data to isolate revenue opportunities of over $200 million. The company used Monte Carlo simulation, a modeling technique widely used in sciences, engineering, and finance to predict probability. The steelmaker adapted this technique to model variables. By predicting thousands of scenarios using historical machine data, the organization could measure the likelihood of system failures, variations, and equipment availability across the production line.
The steel manufacturer identified the key bottleneck in the production process, which boosted output by 10%. However, when analysts consulted historic datasets, they identified that the production line contained multiple, shifting inefficiencies. Even though the main bottleneck had a 60% probability of causing issues, the other two also had the potential to seriously stymy productivity. With this intelligence, the organization could develop more streamlined improvement initiatives. Given the spread of the bottlenecks, it transpired that it was more efficient to address economical maintenance measures. This strategy improved equipment availability, which led to a 20% rise in throughput – and $50 million worth of savings.
Lessons learned from data-driven lean management case studies
In summary, there are three key takeaways from the case studies presented here. Firstly, the case study regarding the steel company shows that senior management needs to take an active role in operations. By and large, the intelligence companies need for a big data project is already at their fingertips, siloed in production logs, maintenance reports and equipment data. Furthermore, valuable data may also be stored in databases belonging to vendors, partners or intermediaries. Therefore, it is important that management takes an active role in letting data analysis teams know where to look.
Secondly, both case studies illustrate that a central principle of lean management and big data strategy is seeing data as a tool of continuous improvement, not a one-off quick-fix. The capabilities required to solve previously unknown or impossible problems obviously result in better operational decisions – and allows management to make these calls with greater speed and accuracy. In addition, these improved operational capabilities empower frontline staff to provide better customer service. By encouraging data-driven lean management principles to permeate the organization, a business can perpetually progress and profit.
Finally, perhaps the most important takeaway from these studies is how businesses can apply data-driven lean management principles to every aspect of operations. Once a business establishes full organizational buy-in to data, the company can begin to identify new areas to improve and expand. For example, advanced analytics can streamline production planning, empower marketing and sales executives, and allow external suppliers and vendors to produce more accurate sales forecasts. Together, every department of an organization can work together to increase efficiency, boost productivity, and deliver a better service. As a result of these capabilities provided by big data and lean management, the business can secure return custom and increase profit margins.