A major factor in ensuring the efficient production of any manufactured good is the timely delivery of materials from suppliers and warehouses. The supply chain isn’t just about the flow of raw materials, however; it has a major impact on the flow of time and money throughout an organization. Delays in getting parts into the hands of machine operators can lead to costly downtime that eats away at profits.
Let's take a look at supply chain challenges and how insights from machine data can allow production and inventory managers to more accurately predict when and where materials need to be routed to help them maximize capacity and eliminate bottlenecks.
The Challenges of Supply Chain Management
Managing the supply chain and the movement of materials throughout production can require constant adjustments. If you have a production line that's running slower than planned, procurement will need to adjust inbound material schedules to eliminate excess, idle inventory. If production lines exceed standard rates and finish projects ahead of schedule, materials will be needed sooner than anticipated for the next cycle, or risk halting production while waiting for parts to be delivered.
Adding to the complexity is the fact that every machine and its operators could perform at variable efficiencies depending on startup times, equipment conditions, parts quality, maintenance requirements and operator skills. The result is that the components used on each machine need to be replenished at different, changing time intervals.
Now that we have a clearer picture of the challenges of managing the supply chain, let's take a closer look at three areas where machine data can help ensure a realistic, attainable production plan that will help make the entire supply chain operate more efficiently.
1. Lead Times
Improving lead times requires detailed production plans that feed the current demand for materials to operators and accurately predict when parts need to be delivered. Up-to-the-moment internet of things (IoT) machine data can help alert procurement teams of potential slowdowns and, in the event of faster-than-anticipated output, trigger expediting activities for inbound materials.
Machine data collected from your production assets can allow for a very clear review of actual performance compared to the standards you’ve established. Not only are the necessary details provided to facilitate updated bills of materials (BOMs) and routings via notifications and dashboard indicators, but also trends in machine performance can be extracted and analyzed over time to determine when, and why slowdowns occur.
With this information in hand, procurement can work with suppliers to potentially delay orders so you don’t sit on inventory and clog up your warehouse. Data can also predict if you’ll be a few days ahead of schedule and need to replenish inventory faster so you can keep your lines running and ensure that materials hit production when they’re needed.
2. Cash Flow
A finely tuned supply chain is potentially the best way to optimize use of your resources — in regard to not only inventory but also cash flow. Metrics serve as a tool to maintain production standards and tight scheduling of inbound materials so that operators aren't left waiting around for work to do, causing labor costs per unit to rise. Managers can more accurately schedule capacity, assess labor requirements, predict material needs and make informed decisions about machinery performance — allowing them to anticipate and plan for potential capital purchases.
As indicated, recognizing a slowdown in productivity can help eliminate idle inventory of raw materials, which can cause inefficiencies and result in unnecessary purchases. On the contrary, recognizing iterative improvements in cycle times over a period of time would indicate that more materials will be needed more often, and present an opportunity for not only cycle time adjustments, but could allow procurement to negotiate better terms with suppliers because of increased volumes.
3. Compliance and Materials Quality
In the past, machine data was capable of showing when disruptions in productivity or equipment failure occurred but not necessarily the reasons behind them. This often left managers and maintenance teams scratching their heads over how to remedy the problem, wasting time and resources as they performed various troubleshooting tactics.
Classifying downtime reasons, such as operator errors or equipment breakdowns is key to accurate production planning and supply chain management. The collection of this type of information when and where it occurs is possible with modern IoT devices and dashboards. Machine metrics can even indicate when poor quality raw materials are the cause for lower output.
For example, if downtime occurred due to a bad part, a shop floor operator can classify the associated downtime reason code, which will alert managers that a part is out of spec and doesn't meet compliance standards. Using this information, procurement can notify the vendor of lost time due to these poor-quality parts and issue a chargeback for that lost time.
How to Get Started
Consistent, timely delivery of raw materials is critical to the efficient execution of production, and machine data can provide immediate recognition of your production status, resulting in rapid identification of issues and enabling quick adjustments to critical supply schedules.
Wipfli's Shop Floor Optics can offer the robust functionality required to maximize productivity, improve cycle times and help ensure the timely and accurate delivery of raw materials and goods. Reach out today for a free demonstration of how Shop Floor Optics technology can enhance your operations.