What's in the black box of factory performance?

When version A of an online ad for shampoo gets more clicks than version B, the marketing team can

Sudhir Arni Sight Machine

Sight Machine's Sudhir Arni

immediately analyze the data and shift spending to version A. When a mining-equipment maker loses a big expected sale, it can quickly forecast the expected revenue and profit hits and take corrective action. But when a vice president of operations—let’s call her Joan—discovers that a manufacturing plant in South Carolina is producing 20% fewer units than an identical plant in Pune, India, she historically has had little information to help her diagnose and solve the problem.

For other industries such as retail, corporate data has already provided instant visibility into enterprise-wide performance. The data is homogenous, allowing companies to gain immediate insight into multi-store operations the moment the data starts getting collected. Meanwhile, visibility into manufacturing operations has been a black box until recently. Not only was the factory data largely unavailable, or available only in retrospect, but the variety of manufacturing data also made it challenging to scalably combine in a way that leads to operational insights.

Now with the advent of IIoT technologies, many manufacturers have started collecting data from their factory floors. Corporations pursuing digitization have made good progress over the past few years in beginning to capture and store their production data. Machines are networked and sensor data is flowing into historians, data lakes or other databases. Additionally, manufacturers have worked to transform this raw data into snapshots of how individual machines or lines are performing.

With these technological advances, corporate leaders are poised to take operational insights—and excellence—to the next level by scaling visibility from individual machines and lines to insights into the entire network of factories. For the first time, executives have the capacity to translate real-time production data into visibility across the manufacturing enterprise.

To grasp how truly transformational this next level of operational visibility is, it is important to understand just how opaque and reactive operational insights have been. Consider, Joan, the vice president of operations with the underperforming plant in South Carolina. In the past, she would receive performance visibility into the fifteen factories she’s managing only on a monthly, quarterly, or even one-off basis. Pre-digital manufacturing, calculating these metrics was such a laborious, manual process that regular visibility into multi-factory performance simply wasn’t feasible. As a result, performance challenges could only be addressed retroactively, once the painful metrics-calculation process had been conducted and the bottom-line had, potentially, taken a substantial hit.

With the advent of IIoT and big-data technologies, manufacturers are finally gaining the same real-time visibility into their enterprise performance that corporate functions like finance and sales have enjoyed for years. Now Joan, who previously spent her time chasing down multi-factory performance metrics and responding reactively, can open her iPad every morning and have instant visibility into real-time production performance of her many factories spanning the corporate enterprise.

This fundamentally shifts operations management for manufacturing from reactive to proactive. The ability to identify reduced performance in a factory, line or even a single machine allows problems to be immediately identified and resolved in real-time. With this enterprise visibility, manufacturers are positioned to drive considerable productivity improvements across the network of plants, ushering in the next frontier of manufacturing productivity—a network-wide, data-driven approach to real-time performance monitoring across the entire manufacturing network and enterprise.

But manufacturers can’t wait. The technology for enterprise-wide visibility exists today and digitally advanced companies are already embracing these IIoT tools.

It’s becoming increasingly critical for manufacturing leaders to stay competitive by building the capability to drive enterprise-wide performance improvements from real-time insights. This is the new horizon of productivity that will transform the manufacturing industry in the coming years.

Sudhir Arni is digital manufacturing lead with Sight Machine Inc.