Data Driven Manufacturing: Right Data, Right Place, Right Time

The basic requirement of a successful data driven production or ‘transparent’ factory are the ability to collect data and to store data centrally. But that data is largely useless if it does not enable improvement, corrective action and the pursuit of manufacturing excellence.  This can only be done if the data is properly mined and appropriately displayed to the right people at the right time.  Data driven manufacturing means that the right data, at the right time, delivered to the right person in a manner that they can act upon, is the best formula to achieving real operational excellence.

The phrase ‘I can’t see the forest for the trees’ is often used when data is delivered to a single source, in a poorly organized format.  The ability to process and act upon data starts with the ability to visualize that data in a way that can be analyzed and interpreted.  This doesn’t mean delivering all the data to one person with a huge number of performance indicators or measurements.  It means delivering the data needed to do the best job.  For a line operator the data required may be minimal and may reflect simple elements like machine performance, up-time or shortages.  An engineer might need data on the performance of a particular product in test, analyzing the reasons for failures.  And the production planner will need to see data that allows them to better plan and consider what-if scenarios around increased volumes or supply chain disruptions.

Those with a vested interest in timely precise data, span the entire organization from the shop floor to the boardroom and everyone in between.  The data that helps the operator accelerate a changeover will contribute to the data that allows the COO to make the right capital equipment investment or outsourcing decisions.  The visible factory is an enterprise-wide strategy with enterprise wide value.

Having established the clear and undeniable value of the transparent factory, what are the critical elements required to making information visible and useful to the enterprise?

The types of data visibility required to achieve a transparent factory are related directly to the activities and roles of those who require information in their day-to-day jobs.  The data needs are either real-time or historic, and these are delivered to the user in several formats such as dashboards, analytics, reports and most recently mobile applications for those needing access to data on the move.

Real-time manufacturing information is most commonly delivered in what we currently call dashboards. A process engineer or a line manger will often use this kind of data presentation to maintain and improve the performance of the line.  The data has to be instantaneous, with any delay likely to cause or extend down-time, with the subsequent impact further along the line.  This kind of data delay can also cause quality challenges and reduced yield, as the overhang of faulty assemblies increases while an error is detected and corrective action taken.  This real-time data in manufacturing is best delivered in a simple graphical manner with strong visual and even audio alerts delivered when a potential problem is likely to occur. The need for instant data here is obvious: the faster the alert, the faster the reaction and the eventual solution.

Historical data is needed for production control, engineering, quality, test and for management to monitor, analyze and adjust production based on current and predicted status.  This is where traceability enters the equation.  Traceability, like visibility, is an essential cornerstone for any manufacturing excellence program that seeks to add value, reduce costs and mitigate risk to an enterprise.  The word history here is used to define anything that has happened, and not ancient history.  If it’s not real-time production data, it’s historic. The delivery method and presentation style for historical data can take many forms.  Reports and detailed analytics are the basics of any transparent system and the details included are essential in informing many decisions about product selection, business and market planning as well as the full gamut of supply chain decisions, such as vendor selection, logistic programming and fulfilment solutions.