Manufacturing analytics: Integrating business activities
Caroline Conway

Manufacturing has a rich tradition of analytics – from leveraging analytics for process design and engineering to using highly analytical techniques such as lean, just-in-time and six sigma to optimize productivity and costs on the shop floor.

Industry 4.0 represents the sector’s tipping point from the traditional into the digital age. This shift to Smart Manufacturing will entail not just a connected, productive and efficient shop floor but also a holistically intelligent manufacturing value chain. Manufacturers need to build on their traditional analytics foundations to bring in new technologies and capabilities to cope with a diverse range of incredibly diverse and exponentially complex digital age datasets. Concurrently, the sector also needs to ensure that the capabilities to convert data into actionable insights and strategic outcomes are aligned to address some of its key challenges today.

The biggest challenge for manufacturers will be to quickly and consistently demonstrate the enterprise value of their analytics investments. Manufacturers therefore need a two step approach to accelerate and maximize the value of their analytics investments. One is to take a systems thinking approach to map the business value of all the interactions, interconnections and interdependencies across their entire manufacturing value chain. This empowers manufacturers to define an investment strategy that is focused on addressing their immediate business challenges and on delivering sustainable business value. Two is to create a tool, technique and technology agnostic solution that custom blends strategy, design, analytics, process engineering and technology to address the unique needs, challenges and objectives of the business.

We can see how this approach works in three different contexts in the manufacturing value chain:


  1.  Integrated data & process design for efficient production
  2. Reducing supply chain variability through forecasting and planning
  3. Leveraging customer data for new product development

Integrated data & process design for efficient production

Manufacturers have always relied on a range of data sources and types to inform their productivity, efficiency and quality initiatives on the shop floor. Industry 4.0 programs have only expanded the scope and variety of these data feeds. But the approach is often bounded by a traditional view of the drivers of productivity, efficiency and quality. Therefore, production or production-adjacent data typically dominates production floor analytics.

A systems thinking approach considers interactions and interdependencies across the enterprise ecosystem, rather than just the production sub-system. An organic data-enabled understanding of how different subsystems interconnect and interact allows manufacturers to create more integrated and effective solutions. For example, analytics can help manufacturers identify/understand customer concerns about products or service levels. These concerns can then be mapped back to the root cause, like the specific production techniques/processes that need to be optimized or component sourcing strategies that need to be honed. By linking data sources across the manufacturing enterprise and understanding how different subsystems interact, manufacturers can effect more impactful, sustainable and valuable transformations to their production environments.

Reducing supply chain variability through forecasting and planning

Traditionally the supply chain has been viewed as a procurement activity distinct from production. But an intelligent, agile and integrated supply chain is the central nervous system of every smart manufacturing enterprise. An Industry 4.0 supply chain integrates with all critical business processes and systems, leverages real-time analytics to create a single source of truth and enables real-time decision making capabilities across the enterprise/supplier ecosystem.

Systems thinking enables manufacturers to map the organic flow of data and analytics across the organization. Understanding how actions, problems and decisions are connected allows them to use the right modeling techniques to enable the right decisions in an integrated environment. For example, manufacturers will be able to share any new analytical models that they use for customer-driven demand forecasting with their suppliers through integrated supply and operations planning. Manufacturers need to create interfaces for real-time data exchanges, build out further analytics based on that data and accurately map procedures between entities. This will give a deeper understanding of supply chain bottlenecks and improve real-time decision making.

Leveraging customer data for new product development

The systems thinking view can even be extended to new product development that incorporate data and analytics. This applies for both consumer and B2B customers who are both increasingly seeking IOT solutions for various needs.

With this mindset, the customer can be viewed as their own ecosystem where data can be captured, linked to other sources, analyzed, planned and measured. This data can then inform the design and development of new products that truly achieve the IOT expectations of the customer. In this case, both the right analytics and the right process mapping is needed to accurately reflect customer activities and needs. Beyond the development phase and post-launch, manufacturers can still leverage aggregate analytics based on customer data to continue informing production and the supply chain.

These are just three areas where a systemic approach to strategy combined with a holistic approach to problem solving can help manufacturers maximize the RoI of their technology investments.

In the connected manufacturing paradigm that is Industry 4.0, a discrete approach to problem solving, at the level of individual subsystems, will only yield incremental results. Manufacturers need to build a connected and organic understanding of their manufacturing ecosystem in order to build the capabilities required to create sustainable differentiation and competitive advantage. A systems view of the manufacturing ecosystem combined with some holistic problem solving frameworks will allow manufacturers to unlock more value from their investments.