I was walking to my hotel from dinner not too long ago and saw a man under a streetlight on his hands and knees rummaging through the grass. I asked him what he was doing and if he needed any help. He told me he had lost his keys and was looking for them. “Is this where you lost your keys?” I ask. “I don’t know,” he replied, “but this is where the light is.”
Now this story isn’t true, but it is applicable in many manufacturing companies.
In today’s world, manufacturing companies track a variety of KPIs and leading indicators to determine the operational efficiency of their overall production run of various SKUs. This tracking is done by analytics, often at an aggregate level of data.
The concept of Digital Factory or the 4th Industrial Revolution (Factory 4.0) is not new. I would however, point out that the intersection of manufacturing and the digital world is not a fantasy. It is real, and it is here now. The concept is to drive tangible business value through increased visibility into real time operations within a corporation down at the individual machine level. This value is not simply within the production process; rather it stretches much father, impacting MES and MRP processes, supplier/vendor pricing and costing, as well as distribution logistics and channels. The topic that gets discussed more often than not these days is Preventive or Predictive Maintenance. In my opinion, that is relevant, if under-ambitious.
Industrial Analytics goes beyond just the idea of Preventive or Predictive Maintenance, it now drives measurable business value by driving forward P/E through production run efficiencies.
Imagine you are a discreet manufacturer with a stamping machine that typically runs at 3,000 RPM. You’ve installed sensors in your machines as part of a ‘Digital Factory’ initiative and you are now measuring this RPM speed in order to effectively predict downtime or scheduled maintenance. As such, you have a range of acceptable RPM speeds of 2,500 to 5,000 RPM. Anything outside of those parameters would trigger an action by the maintenance team to keep things humming. While that may be good, much of the data that monitors the speeds within the acceptable range is completely ignored. In reality, the RPM of the machine has a material impact on the production efficiency (and thus cost) of that particular product.
What is the cost of production impact of 2,500 vs 4,500 RPM? Is one costing more? What is the optimum speed of the machine? Tracking this can not only impact overall downtime and unplanned outages; it can impact the net cost of defects and production; impacting gross margin by driving operational excellence. There is a second dimension to all OE initiatives; when the production quality improves, customer quality metrics like perceptual quality also improve, driving customer retention.
How does the stock market view this data? It doesn’t show up on the balance sheet, but it is clearly an important part of the value of a company. In effect, the forward P/E of the stock is the proxy by which the market votes on the durability of the earnings estimates.
In today’s market, with quantitative easing driving down the value of savings, the average forward P/E for companies is 20. Above that and the market is saying your company has a bright future. Below that and the market is voting that there are doubts about the durability of those earnings. My colleagues, Jasen Judd and Richard Carr have an interesting take on Quality Analytics as a Service, which addresses this very notion of forward P/E impact of production costs. The focus is on first driving production quality, then driving perceptual quality to drive customer retention.
With a variety of companies offering engines and applications for the collection, ingestion and monitoring of manufacturing data, such as GE Predix and ServiceNow, the market is opening wider and faster than ever before. The time is now to review your company’s goals for growth and operational excellence within your shop floors.