The primary contribution of the Internet of Things (IoT) will be in transforming the manufacturing industry. IoT will redefine how manufacturers meet industry demand, especially as the cost of attaching intelligence across the production line decreases.

This transformation is possible because the acquisition and implementation of sensors, as well as wireless data connectivity, is no longer cost or time prohibitive—and consequently, sensor data is becoming more available and easier to collect in real-time. The measurability of operational metrics, such as Overall Equipment Effectiveness (OEE) and yield will benefit immensely as a result.

For manufacturers especially, access to real-time analytics is a compelling value proposition. It enables manufacturers to further improve upon managing downtime or increasing yield, which can often times mean the difference between expanding or contracting a manufacturing plant’s footprint.

Gaining Visibility Across the Production Line

Using sensors across the production line—from the production line’s beginning through shipping—manufacturers can collect their value chain’s data in real-time. They can then leverage purpose-built applications to collect that data from multiple machines and production lines, and gain a clearer picture of where efficiencies are (and are not) happening across their value chain. Calibrating their production line based on real-time operational metrics will allow their application users to redefine and maximize their production line’s capabilities.

A-la-carte intelligence through the purchase of customized solutions is another route for incorporating analytics across a manufacturer’s production line. Unfortunately, this approach does not provide transparency across the entire production line, but only for one point in the overall process. Furthermore, it limits your ability to nimbly adjust your entire production line for new technology updates. Integration costs can be quite costly and a-la-carte solutions will need to be upgraded independent of the whole production line, a pricey proposition. Manufacturers can overcome this cost by adopting a software solution that can sit on top of the solution stack, connect to multiple databases, and has an easy to use front-end.

With this information in hand, manufacturers can better respond to events in real-time, reducing both production line inefficiencies and downtime. For larger manufacturers, reducing inefficiencies by just one percent can equate to millions or tens of millions of dollars in cost savings. By reducing production costs and inefficiencies, manufacturers’ production responsiveness increases, and the organization as a whole becomes more nimble. The result is greater downside protection (i.e., a recession-type environment) and an opportunity to generate greater profits under normal operating conditions.

Competitors unwilling to adopt IoT strategies will face the tough task of price matching manufacturers who, through IoT strategies, are profitable enough to undercut them in any market environment.

There’s a lot of uncertainty surrounding IoT and many manufacturers have been skeptical about the benefits associated with adopting IoT strategies. But with the right IoT solution, calibrated for your specific use case, manufacturers who adopt IoT strategies will have an information advantage over the competition.

Calibrating IoT to Work for Your Specific Use Case

IoT devices like sensors generate significant amounts of data, and without the right calibration across the production line, manufacturers can end up drowning in data they don’t use, become confused with an application’s outputs, or can miss opportunities to improve business efficiencies.

There are two common IoT calibration strategies that we have seen manufactures implement: IoT data for operational efficiency purposes, where sensors inform managers to value chain inefficiencies in real-time; and IoT data for predictive maintenance purposes, where sensors alert production line managers when manufacturing machines are operating outside of their statistical control limits.

The ROI on machines located across the production line are often the difference between financial success or failure. The two previously mentioned IoT strategies can help the people located across the production line ensure that they are getting the most from their equipment investment and help them maximize their firm’s bottom line.

Empowering the End User

It’s essential to focus on who is going to use that data to make decisions. End user adoption is where manufacturers will need to focus their attention. Failing to account for the diverse roles and skill sets of their application’s user base will lead to poor adoption rates. Research has shown that a large percentage of the application user base for these manufacturers are on the move and out on the factory floor.

Simplifying software adoption use will increase time to value associated with building an active IoT strategy. The challenge will be bridging the knowledge gap and driving end-user adoption for employees who may have worked in roles that traditionally did not require an understanding of analytics.

Fortunately, modern business intelligence applications provide individualized views tailored to the roles of different users—empowering them to discover new insights and drive operational performance. Clients, managers, and employees need more data as technology increases market competition, requiring manufacturers to become more nimble and market reactive. It’s just a matter of time before the conversation shifts away from how to deal with a malfunctioning PLC towards developing best industry practices in developing an intelligence system across one’s production line.

Patrick Chartrand is a solutions strategy analyst at Logi Analytics, where he is responsible for market development, product strategy, solution demonstration development, and market research functions.