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.

Analyzing a manufacturing plant’s performance is not a new concept. Most manufacturing companies have at least some sort of LCD screen on the shop floor reporting actual versus goal production metrics to keep everyone on track.

While this is tremendously helpful for the production process, it’s really only one piece of the analytics puzzle. Manufacturers should be implementing a more comprehensive view to gain an advantage over their competitors.

Now more than ever, manufacturers need vertical visibility – from the shop floor to the top floor – to understand not only how their manufacturing plants are performing from an operational perspective, but also to understand the implications their performance has on the organization’s financial picture. The primary way of achieving this transparency throughout the organization is to embed analytics at every level.

The Shop Floor

People working at each level of a manufacturing company have different roles and, therefore, different analytics needs.

First, we start at the shop floor, which is usually comprised of line workers trying to meet daily production targets set by management. Most of these workers don’t need to delve deep into the data; they just need to quickly gauge how they’re performing against key performance indicators (KPIs). That’s why we see large, centrally located LCD screens installed on many factory floors today. They let everyone on the shop floor easily look at the metrics and immediately gauge how they’re performing in relation to their production targets. In effect, this enables shop floor workers to self-manage their performance and adjust to production changes in real time.

But some workers on the shop floor need to go beyond viewing these high-level KPIs. Unit managers, for example, are responsible for monitoring line workers. While they, too, can review the LCD screens to gain a general understanding of their production line’s daily status, they may also need to view individualized analytics (e.g., production reports) that allow them to react and manage the production line in real time.

To meet end users’ needs, analytics at the shop floor level also need to be mobile-friendly. Analytics embedded in an iPad or other portable device enables easy viewing while workers are walking around the shop floor, allowing unit managers to make proactive decisions to improve production efficiencies or avoid production line downtime.

Depending on the complexity of the production line, some plants also have engineering experts working on the shop floor. These experts are doing self-service analysis at the machine level in order to maximize overall machine performance. Engineering experts like these could benefit from purpose-built analytics solutions, which collect data from multiple machines and production lines. These tools give users a clearer picture of where efficiencies are (and are not) occurring across their value chain in real time.

With this information in hand, they can better respond to events in near-real time, reducing both production 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.

The Plant Manager

Sandwiched between the shop floor and the top floor is the plant manager, who is responsible for a manufacturing plant’s entire production process, from when raw materials enter the plant to when the product exits the plant for distribution. The plant manager needs to know what is happening throughout the entire production process in real time – and that requires monitoring analytics from all of the machines, people, and processes that constitute an organization’s production process.

A plant manager wants to see operational KPIs related to how the plant is performing, such as

meeting forecast production; the location of production line inefficiencies; how to get back on track; and how to reduce or eliminate production line downtime.

Plant managers—more than anyone else—need to be able to dive deep into the analytics of their respective production processes to determine which methods, machines, or people need adjustment. They also need an easy way to communicate down to their unit managers and up to their managing director. Having some form of write-back functionality within their analytics suite will help them communicate any changes or status updates to their process stakeholders. With the ability to create and share real time customized analytics reports, plant managers will be able to get on the same page with their constituents instantly.

The Top Floor

A manufacturing company’s top floor is comprised of vice presidents of operations, directors of operations, and presidents. At this level in the organization, employees want to view analytics at a higher level than a plant manager or shop floor employee. They have multiple plants and managers reporting into them, and typically they don’t have time to go too deep into the weeds of a particular plant’s operations.

Top floor managers are more focused on connecting operational KPIs with financial KPIs—e.g., forecasting, investing, and budget allocation—and correlating operational data with that of their company’s ERP system. These managers expect operations to move along in a certain fashion, and having the ability to analyze and visualize how their operations are running in comparison to their associated financial investments broadens their understanding and discussion of their organization as a whole.

Although they may be more focused on the holistic view of their organization, these managers still need to be able to customize their own reports and views through self-service analytics. The same form of write-back functionality that plant managers need should also be incorporated at the top floor level, since managers at this level need to respond to client and senior management needs in real time.

Ultimately, analytics can provide manufacturers essential information about how they can meet future demand – but the only way to gain such a complete and thorough view of operations is to embed analytics at every level.

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.