Just-in-Time Manufacturing Needs Better Data

Technology helps the manufacturing industry define “time” and adjust JIT inventory management processes to shorten lead times and optimize ordering and control costs.

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Working in the manufacturing industry currently feels like a constant balance between what we can and cannot control. Issues often originate beyond direct influence, so we constantly adapt our processes to better manage operations in response. One thing we can strive to control is creating lean, efficient and intelligent operations, which is especially vital now as the pandemic greatly challenged supply chains with unprecedented, constant disruption. This on the heels of the U.S.-China trade war in 2019 and even the Suez Canal blockage earlier this year. These events forced the manufacturing industry to reconsider the concept of what was once an achievable industry goal - “just-in-time” (JIT) manufacturing. Industry data indicates that some organizations are already placing orders for Q1 2022 to get ahead, showing that the “time” in JIT manufacturing is extremely relative and constantly changing.

It’s not just about being faster, but being smarter. Now, more than ever, manufacturers need to define what “just in time” means for their organization by establishing a robust asset management plan, bolstered by a streamlined system for ensuring business continuity.

The reality is that JIT methodologies can be adjusted to take into account supply chain issues and delays. In the past, when the supply chain was fully operational, actual on-hand quantities would likely be at relatively low levels. This saves valuable space, simplifies ordering and helps control costs. JIT is still possible, but companies must have a greater dynamic buffer.

First, companies need to have excellent visibility into their parts and procurement processes. This starts with solid parts management. Companies need to know:

  • A complete catalog of parts
  • Where parts are stored
  • Where they order parts from
  • Visibility into what is on order, on back-order and other quantity information
  • Visibility into parts with low supply, with ordering buffers that can accommodate the difficulties of finding parts
  • Visibility into parts needed for upcoming preventive/predictive maintenance

Visibility is part of the solution, but automation and better use of data enables smarter decision-making that’s built for the long term. Specifically, adaptive organizations maintain databases for their maintenance system and harness big data to inform decisions and increase efficiency and profit. Merging spare parts data with an inventory management system allows manufacturers to avoid stockouts for necessary parts, which leads to unplanned downtime that cuts into the bottom line.

Companies can optimize spare parts’ inventory in a myriad of ways to ensure that manufacturers have the right parts, at the right time, at the right cost. For example, technology provides manufacturing companies with automated visibility by tracking data around physical counts and part quality, as well as information on sources and suppliers. Maintenance technicians can scan barcodes with their mobile devices to get insight in the quantity of available parts. Additionally, manufacturing companies can work smarter by associating spare parts inventory with their assets’ bill of materials. In doing so, manufacturers can gain a greater and more accurate understanding of where the parts are and their availability.

Smart spare parts inventory management should also be incorporated into preventive maintenance procedures. Manufacturers must ensure they have a list of parts (including part “kits”) associated with preventive maintenance in the platform that can be accessed by the necessary staff members. Overall, integrating data to capture a holistic view that is available to the entire organization on the status of spare parts, their quality and their availability will allow manufacturers to build an efficient requisition or purchase order process ensuring part availability when they need it. This will ultimately reduce profit-killing downtime.

Both increased visibility and more effective use of data allows manufacturers to define time when it comes to JIT manufacturing. Many maintenance technicians can tell a story of standing in front of an asset needing a repair, walking across the plant to a spare part depot only to find out that part is out of stock. We all need our “steps” during the day, but they should not be a waste of time. At any point, a worker can find out the availability of a needed part and know whether they have it before spending valuable time searching the storeroom.

With the simple scan of an item barcode using a mobile device, workers can quickly locate the associated parts and see their on-hand quantity. The real-time updates provide instant access to the asset’s spare parts list and accurate on-hand quantities. Alerts can proactively communicate if the company goes below suggested reorder points. A modern computerized maintenance management system (CMMS) will make this process more effective, providing instant insight all before the technician walks across the plant. Up until now, JIT manufacturing methodologies required adequate lead times, which have been exacerbated now due to shortages spurred by the pandemic, the Suez Canal blockage and lingering U.S.-China trade tariffs. Given these circumstances, it’s easy for manufacturing companies to fall into unplanned downtime and lose profit caused by low-inventory stockouts. However, more and more manufacturers are leaning into the developments happening in software, artificial intelligence and machine learning to harness more intelligent operations.

Technology helps the manufacturing industry define “time” and adjust its JIT inventory management processes to shorten lead times and optimize ordering and control costs. And, as manufacturing companies continue to build more intelligent operations with technology, big data and predictive analytics will be the driving force behind the time in JIT manufacturing. JIT manufacturing isn’t dead – it is a dynamic concept where flexibility due to conditions is key, aided by big data to help create visibility and inform decision makers.