How Manufacturing Monitoring Systems Work to Improve Shop Floor Performance

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Manufacturers face more pressure than ever to deliver quality products quickly, but any number of variables can throw off the best-planned production schedule—from machine and human error to unexpected downtime and materials shortages. Real-time production monitoring and process monitoring work in concert to provide immediate insights that enable manufacturers to rapidly identify and address potential issues before they impact the ability to deliver on customer commitments. In this article, Lynn Loughmiller, Software Engineering Manager at DELMIAWorks, takes a closer look at how production and process manufacturing monitoring systems work to improve shop floor performance.

Understanding Manufacturing Monitoring Systems: Production Monitoring vs. Process Monitoring

 Production monitoring and process monitoring both rely on real-time data from smart machines and sensors on equipment. However, they are effectively two different parts of a recipe for gaining insights into what is happening on the shop floor at any given time.

  • Production monitoring is similar to the top part of a recipe that lists the ingredients, centering around parts and materials. It tracks the production of parts starting from the raw material used, such as a plastic pellet or metal sheet, to counting parts as they are created, tracking their movement into inventory, and recording any associated scrap.
  • Process monitoring is much like the bottom of the recipe that looks at how things are being done. It focuses on conditions that affect processes, such as whether machine cycle times, temperatures, amperages, and lubrication levels are within specified parameters.

Five Ways Production and Process Monitoring Can Improve Your Operations

When real-time data from manufacturing monitoring systems is shared with a manufacturer’s enterprise resource planning (ERP), manufacturing execution system (MES), and other related software, the information can automatically trigger actions and support decision-making throughout the business. The following are five examples of how production monitoring and process monitoring—either separately or working together—can be used to improve operations.

  1.  Inventory Management – Production monitoring plays an integral role in ensuring that enough raw materials are in inventory because it tracks machine cycle times, which can be used to predict when a manufacturer will run out of a specific material. Production monitoring can also capture when more scrap than projected is being produced, signaling not just a potential production problem but also the need to re-order a raw material sooner. An MES storing real-time production and process monitoring data can feed this information into the purchasing model of an ERP system, which then auto-generates purchase orders for materials, ensuring the availability of materials to support production runs while also improving utilization.
  2. Warehouse Management – Production monitoring of machine cycles can help manufacturers get raw materials to the correct machine when they’re needed and avoid downtime. Here, real-time data in the integrated MES system is fed into warehouse management software, which uses this information to automatically direct forklift operators or other workers on where to deliver raw materials for use at a specific machine.
  3. Quality Control and Compliance – Manufacturers can use both monitoring approaches to ensure quality. For example, production monitoring together with a vision system can trigger a programmable logic controller (PLC) to detect bad parts. Similarly, process monitoring can capture factors, such as machine temperatures falling below acceptable parameters. In either case, the data can be used to automatically trigger automatic rejections of the affected parts. Additionally, process monitoring data fed into ERP and quality management systems can simplify compliance and certification by automatically verifying and documenting that parts were made in accordance with specified parameters.
  4. Preventative Maintenance – The two types of monitoring can be used to indicate when to perform machine maintenance. For example, production monitoring can capture that a particular machine has just produced 300,000 parts over several work orders and trigger a maintenance work order for that machine. On the other hand, when process monitoring captures a machine’s cycle counts, the number of cycles or length of cycles can indicate wear or a problem with the machine, again triggering an alert to schedule maintenance at a time that minimally impacts production.
  5. Auto-Scheduling – In those cases when a machine goes down, the part still needs to be produced. Production monitoring can show how long a work order is going to take. The data can then be shared with auto-scheduling functionality in the MES system. If the downtime is minimal, auto-scheduling may simply update production timing on the current machine. However, if the downtime will impact delivery to the customer, auto-scheduling can re-assign the part production to another machine.

Looking Ahead – Adding the Power of AI

The applications of production and process monitoring will continue to grow as manufacturing monitoring systems take advantage of advances in generative artificial intelligence (AI). Both forms of monitoring have made it possible to build huge historian stockpiles of data from machines on the shop floor, which can be used to build predictive models. Then, when mapped against real-time data in ERP, MES, and other manufacturing applications, these models will make it possible to forecast what is or will happen on the shop floor with much greater accuracy.

The resulting AI-driven insights will provide a strategic resource for training engineers and operators on how effectively their product “recipe” is working. Moreover, these AI-powered analytics and forecasts will become a critical factor in empowering manufacturers to make timely, highly informed decisions across their business. And that’s a recipe for success.

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