Get started in manufacturing IIoT: Put a green patch in your brownfield

The other emphasis is on analytics. The question is, who has the process knowledge to effectively evaluate not just the data but also which data, and which actions should be utilized to optimize the process?

Also, how well will decades-old machine controllers be able to effectively affect the process with limited processing power, memory, connectivity, and response times? In a brownfield, integration can be a major challenge with assets that may be decades old. The machine control systems typically are not networked and do not communicate with each other. Once IIoT sensors, networks, and edge devices are overlaid on the existing assets and analytics are applied, the legacy control systems may not have the capabilities to optimize operations.

At the same time, it’s probably not advisable to wait for a greenfield plant to be built to start the learning curve. This is the rationale behind cultivating a green patch in your brownfield as a practical way to get started in manufacturing IIoT.

The concept is simple, borrowing from “focused factory” and “factory within a factory” strategies that helped advance stagnated manufacturing practices in the 1970s and 1980s. A greenfield incorporates fresh equipment, tooling, and state-of-the-art operational technologies (OT) and information technologies (IT) with the bandwidth, processing power, and memory needed to apply advanced software and processes. This definition also applies to a green patch, only on a more-limited scale. These capabilities support predictive maintenance, supply-chain visibility, energy management, rapid changeovers, lifecycle management and more.

The concept is to target your IIoT pilot project on a new product or process that will require CapEx investment and will use state-of-the-art machinery automation technology, and determine the true productivity potential of advanced monitoring, analytics, and process improvement techniques.

Consider the following additional criteria for a green patch project:

• High-value, complex products where IIoT can make a significant dollar impact on yield
• An application that will benefit from flexibility
• An operation where maximum uptime is essential that will benefit from predictive maintenance
• A production line or cell that can operate independently from the rest of the factory.
• An operation that can be isolated, with MES/ERP that may be self-contained and may be hosted on premise.

Deciding the right data to acquire for analysis is essential. The key is to include all relevant details in a structured, targeted, and consistent format. When supported at the enterprise level as a strategic priority and not merely a managerial tactical responsibility, advanced analytics drives predictive insights into where to invest, ways to optimize productivity, keys to reduce time to market, and other essential metrics.

The Lean Six Sigma key performance indicator overall equipment effectiveness (OEE) is an excellent metric for tracking progress on your green-patch project, but varieties exist: Some consider all downtime, including scheduled maintenance and holidays, while others exclude all but unplanned downtime. Hence, the green patch should define a consistent OEE metric for moving forward and therefore serve as a framework for future process improvements without impacting existing operations.

Determining the optimum (as opposed to fastest) production rate for a line is one of the most practical uses of the OEE data. Running a line at its fastest can cause the bottleneck machine (there always is one) to result in stoppages and actually reduce throughput compared to operating at a lower speed.

Advanced analytical tools close the loop and provide a mechanism to apply the data to process optimization. For example, total productive maintenance (TPM) can take OEE data and improve the operation of machines and lines, but without IIoT’s continuous, automated monitoring and feedback, TPM is limited to set line speed based on the bottleneck.

With an IIoT-enabled green patch, process optimization can take into consideration many more factors, such as:

• Variations in source material – for example lots, recycled content, and recipe
• Impact of environmental factors, such as ambient temperature and humidity
• Process factors such as heat buildup, mechanical wear, and supply constraints
• Anomalies resulting from human factors, such as operation, lubrication, cleaning and improper use of e-stops
• Logistical issues such as parts replenishment, coding system ink level, tooling, and change part availability.

Constraints are the same as for any other upgrade in a brownfield environment. The green patch must conform to the physical footprint, shared resources, and other constraints of the existing facility. These can include utilities, physical obstructions, air filtration, and even the available labor pool and zoning restrictions. Each issue and solution will need to be evaluated on the basis of potential payback and can help define project scope.

For example, does a clean room need to be built to mitigate airborne contamination from adjacent processes, or is it better to construct a new building if the land is available? Can critical equipment be purchased in an isolator configuration, and if so, what impact will this have on the efficiency of ongoing operations, maintenance, and replenishment?

The more time you spend in a brownfield, however, the better a green patch looks. By focusing on a single new line or cell, it can be much easier to get started with an initial deployment, learning how to apply IIoT in a controlled environment and scale, with new technologies designed for the task. The “green patch” is intended to propose a readily accessible approach for midmarket manufacturing enterprises to obtain the advantages of IIoT without running the risk of becoming overwhelmed by a large-scale rollout.

The full green-patch white paper is available from the Industrial Internet Consortium’s smart-factory task group at https://plnt.sv/1808-GP.