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December 9, 2019

Analytics has been a boardroom topic for a couple of years now as organisations realize its immense potential in driving business strategies and transforming the way they serve their customers. Many have incorporated C-level designates such as chief data officer or chief analytics officer to drive their organization’s analytics agenda.

Is shop floor analytics still a distant reality?

In the manufacturing industry, too, analytics plays a big role, especially in customer-focused functions such as sales and marketing, followed by supply chain or material planning. However, manufacturing analytics is not a priority for many enterprises even though there is a considerable amount of digital material on predictive analytics, equipment failure prediction, manufacturing quality, and more. Here we identify the common hurdles manufacturers face in adopting analytics for overall equipment effectiveness (OEE):

  • Strong preference for consumer-focused analytics vis-a-vis shop floor analytics

Analytics demands considerable investment in technology infrastructure, teams, and learning and development initiatives. Most executive teams have a predisposition towards customer analytics, as that has a direct impact on the top line. Manufacturing analytics, on the other hand, has a considerable impact on the bottom line and manufacturing lead-time. Most organizations prefer cost reduction programmes to analytics for bottom line improvements.

  • Complexities in developing a strong business case

Shop floor data is primarily of three types. The first is the machine data used to understand machine behavior, and in most plants, machine data is temporary. The second type is enterprise data, available across multiple systems like the manufacturing execution system, enterprise resource planning, and quality and plant maintenance. The third is manually stored data which may comprise of paper-based documents or spreadsheets. Whenever teams try to build a business case for shop floor analytics, the multiple data sources, varied systems, formats and ownership of data, and complex integrations make the data less reliable. Organizational silos are also another barrier to accessing clean data. As a result, technology companies opt for internet of things architecture or a data lake to assess data in an analytics program, which is again an additional investment. Owing to this, we often see analytics teams go for quick wins, starting with descriptive analytics that involves slicing and dicing data in multiple ways to generate insights. The business case, however, gets lost because it becomes difficult to justify how analytics improved the OEE of the plant, which is the core objective of any shop floor analytics programme.

  • Management dependency on traditional workforce and techniques 

Most manufacturing plants employ senior operators who have been in the company for decades. These employees know how the plant operates, are aware of its constraints and opportunities, and can intuitively connect the dots and suggest OEE improvements based on their observations. But this tacit knowledge can be lost when the senior workforce retires. Thus, analytics can play a crucial role here by capturing this implicit data.

  • Operational silos inhibit successful OEE analytics

In most manufacturing organizations, analytics is part of the IT function, or in some cases, the corporate organization. One of the downsides of this is that the analytics team does not have champions, or dedicated manpower, in every department. Even when they incubate champions, they usually have an IT background rather than one in business. Analytics demands cross-functional teams that comprise domain experts, business analysts, data scientists, IT architects, and more. Putting such a team in place and sustaining it is very tough. In consumer-focused analytics, most front-end folks are open to learning, as there are significant opportunities in the open market for those skills. This is not the case for shop floor analytics.

OEE-based analytics: A business imperative

OEE is a key performance indicator for manufacturers and combines measures of availability, throughput, and quality. Incorporating analytics in OEE can help identify problems before they occur, reduce costs, and improve operational performance, thus enhancing the returns on assets.

To overcome the barriers to establishing an analytics programme, organizations have to develop a consulting mindset, with focus on the big picture and adopting a disciplined approach to innovation. 

The next blog post will delve into the tools needed for disciplined innovation to build a transformation charter for shop floor analytics.

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Praveen Prabhu is a senior business consultant and Innovation Champion for TCS' manufacturing customers in the UK. He is responsible for shaping innovation-led programmes in the areas of smart manufacturing, future supply chain, and connected products. Praveen has 12 years of proven experience in the manufacturing industry across consulting; ideation, selection, and rapid prototype development; design thinking; and solution delivery with specialized focus on automotive and industrial manufacturing.


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