Benefits of Big Data in Manufacturing and Traceability


Jason Spera, CEO and co-founder of Aegis Software

Big Data and Traceability

Taking a big data approach is a change of mindset.  In real terms it is all about viewing big data in manufacturing as the means to drive manufacturing excellence through an entire workflow, where traceability becomes a valuable byproduct rather than a cost. This yields many benefits of big data analytics and traceability in manufacturing. Here are four key advantages of big data analytics:

1) Continuous Process Improvement

A point-solution to answer a traceability requirement is not typically intended to support process flow improvement.  The data set it generates is meant to satisfy a trace requirement but little else. The big data approach gives the enterprise all the information it needs to leverage data toward process improvement and achieving operational excellence.  A big data solution provides this advantage by supporting analytics for managers, engineers and line operators so they are armed with actionable information to make better decisions. The process of big data also involves automated process interlocking and fail-safing to keep processes from going out of control even when humans fail to detect such conditions.

A big data solution provides a view into the entirety of operations that otherwise would be extremely difficult to achieve.  Real-time dashboards informing operators of impending issues or process performance, condition-generated reports sent immediately to mobile devices when needed, real-time process interlocking of machines and conveyors based on control conditions, visual quality data collection, repair guidance, diagnostics support, real-time detailed work in process monitoring, predictive process flow analysis, and much more can all be achieved simply and efficiently. These big data benefits all support greater control of variability, improved quality, and continuous operational improvement.

2) Shifting from the Psychology of Cost and Burden to Cultural Adoption

A critical factor in the success of any software solution in a manufacturing enterprise is cultural adoption by management and operators.  The most powerful and capable software system if considered a burden will rarely succeed.  The perception of burden can be the result of a poor user interface, additional transactional overhead slowing work down, or merely confusion as to why it is even necessary at all.

All these problems are common with narrow traceability systems.  It is not a problem with the big data approach because of the holistic nature of the system and the tangible benefits that go beyond data traceability.

Adoption of a system to harness all manufacturing data becomes part of the operational culture.  It simply is the way business is done.  This perspective evokes more enthusiastic adoption by operators as it is much like switching the entire factory over to a new data system and the old methods no longer exist.

Additionally the big data solution yields visible benefits to management and operators and everyone in between.  Dashboards, reports, real-time process interlocking, simplified quality data collection and feedback, real-time detailed work in process monitoring, predictive process flow analysis, etc. all give personnel real benefits to their daily work that results in an appreciation of the business’s implementation of such a solution.

3) Future Proof Solution

A point-solution for delivering a specific set of traceability to meet a demand soon requires modification to meet an expanded demand as regulatory requirements evolve, or where a customer demands greater traceability.  This never-ending cycle of customization, costs and delays results in a clumsy and complex solution that is difficult to maintain.

The big data approach does not suffer from such issues.  It provides an infrastructure to gather all data, and, as requirements evolve, it is simply a matter of querying that data.  The solution is inherently gathering the entirety of the data set from the product, process, and materials context of production, or, if deployed incrementally, is architecturally able to add data sources into the system elegantly with little or no customization.

4) Lower True Cost

When traceability is approached as the end-goal rather than a byproduct of harnessing manufacturing big data, the true cost of the initiative is greater.  A point solution offers no additional benefits from which to gain ROI for the system investment.  A narrow solution for delivering traceability essentially incurs only costs by existing as a layer of complexity used only to satisfy an externally imposed requirement.  As it has no other benefits for the enterprise, its ROI is based only on answering an external demand for traceability.  It may avoid risk, but it creates no benefit and is consequently expensive.

When approached as a natural byproduct of harnessing manufacturing big data, it becomes a ‘free’ benefit of a system that is yielding analytical and process improvement.

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