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A continuous feed manufacturer had quality problems resulting in high scrap rates.  IIoT sensors and machine learning analytics enabled the manufacturer to analyze data every 5 seconds from hundreds of sensors embedded in equipment touching the continuous feed.  This analysis identified hidden relationships between production variables and quality variances.  The results were reduced scrap, higher operating efficiency and more predictable schedule-to-actual performance.

An electrical equipment manufacturer couldn’t provide the agility for detailed capacity or S&OP planning for its 2,000+ SKUs. Forecasting was monthly.  Raw material from overseas required a 2-3 month lead time but customer lead time was 1 month.  With data analytics on-time performance reached 98% for 11 out of 12 months.  Inventory turns went from 2-3 to 6 annually.  Profit margin increased 5x from 2 to 10%.

Pharmaceutical and food companies have some of the most stringent track and trace requirements of any industry.  Using chaining technology, a company can follow the trail of individual saleable units, recording every exchange of ownership. In a disruption from contamination, a company can trace every batch and package containing the tainted ingredient. 

The Changing Digital Technology Landscape


We are in the midst of an inflection point.  Every aspect of the enterprise is becoming digitally enabled.  This enables manufacturing to transform materials with less energy and waste.  To make the workplace safer.  To free people from tedious and rote labor to be talent which truly adds value. To design, engineer and make goods not previously feasible.  To operate and maintain equipment at unprecedented levels of productivity and reliability. To re-engineer manufacturing and production processes. 

Digital and Data in the search for Consistent Excellence

We know that some equipment and production lines work better on some shifts with some operators than others.  We can see the results in quality and yield.  In the past it was problematic to identify and control for the variables.  With the real-time insights of ubiquitous sensors, machine learning algorithms and control systems all synchronized in real time we can run a production operation as if the best people were running the best equipment 24/7.

Digital and Data in the Production Process

In the past era, data answered the basic question “How did we do?”  Data stood outside the producing process, after the fact, costly to collect and analyze.  Digital technology has transformed data.  Data is now embedded in the production process and low cost to collect and analyze.  It has changed the questions we can ask to:  How are we doing this instant (real-time)? What is about to happen (predictive)? What actions should we take and when (prescriptive)?  This enables us to simultaneously reduce variance, cut waste and step-up yield and productivity to unprecedented levels. 

Digital and Data in the Supply Chain

In the past era, as companies became more separated from the end consumer, they didn’t have data to accurately answer “What should we produce?” Over 80% of companies have lost the ability to plan effectively and are constantly expediting to meet demand.  This added we layers of cost to buffer and hedge and layers of delay to respond and act.  We became rigid and bureaucratic.  In the digital + data world, data flows through the value chain at the speed of business.  We know more about our customers than ever.  We can ask: “What is happening to end demand?” then flow it backwards through the supply chain. We have visibility to perform end-to-end track and trace on the movement of material and goods and the external conditions at each step in the supply chain.

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