Retooling Supply Chains After COVID-19By Jenn Fulmer Print
Consider whether your supply chain method is still viable despite COVID-19 hardships and how it might be improved with AI.
The COVID-19 pandemic showed us just how fragile our supply chains really are. Even a year later, some grocery stores are still limiting the quantities customers can buy of things like toilet paper and canned goods so they can serve as many customers as possible. Before we can get back to normal, businesses need to retool their supply chains to prevent disruptions when the next big disaster strikes. To make this retooling easier, we examine different supply chain methods and how you might incorporate artificial intelligence (AI) to improve productivity.
Jump ahead to:
- Materials requirements planning (MRP) supply chain
- Just-in-time (JIT) supply chain
- Flexible manufacturing systems (FMS) supply chain
- Optimized production technology (OPT) supply chain
- Choosing the right supply chain method in the wake of COVID-19
With a materials requirements planning (MRP) supply chain, companies get access to the materials they need for the next stage of production just before they need them. MRP plans for uneven customer demands and prioritizes the prevention of stockouts. It demands precise forecasts, accurate bills of materials, and disciplined employees. This system allows supply chain managers to identify any shortages or possible delays in advance, so they can reschedule release dates as needed.
MRP models usually work best for companies engaging in mass production. Companies that apply MRP will benefit from reduced inventories, streamlined operations, and optimized usage of both labor and space. However, MRP requires huge amounts of data input in order to function correctly, and the system can be extremely complex. It assumes that manufacturing centers have unlimited production capacity when that’s simply not the case. Additionally, a poorly implemented MRP system can cause resentment from employees and managers alike.
If you’re currently using the MRP model, you might consider incorporating AI into your forecasting and ordering software. Artificial intelligence can help you get more accurate forecasts for the materials you’ll need and ensure you have the right amount on hand. Additionally, by adding it to your ordering system, the AI can automatically place orders for new materials when your current supply drops below a certain threshold. This reduces some of the strain on your employees and optimizes your supply chain.
Like MRP, the just-in-time (JIT) supply chain model attempts to get supplies into manufacturers’ hands just before they need them. Unlike MRP, however, JIT can only handle small production runs. The main objective of JIT is high-quality production that keeps costs low and arrives on time by minimizing storage costs and idle time for workers and machines. One of the benefits of the JIT model is that it empowers workers and improves employee satisfaction. Any employee has permission to stop the production line if they notice a defect or if they simply fall behind.
Production must remain consistent when using the JIT method. The model starts to break down when there are deviations from average production conditions. The daily schedule for each part needs to be almost identical every day. Distance from suppliers, inclement weather, and other supply chain interruptions can severely affect manufacturers using the JIT method because they don’t keep any excess inventory on hand.
To improve the JIT model, you should choose an inventory system that includes AI. Artificial intelligence can track product as it makes its way to your warehouse and automatically place emergency orders should a shipment be delayed. It can also help you identify which suppliers are your most reliable, so you can prioritize them for new orders and reduce your reliance on unreliable vendors.
Flexible manufacturing systems (FMS) for supply chains combine planning for machinery operations with their computerized data systems, normally some kind of supply chain management software, to improve productivity and enhance the utilization of machinery. FMS models are best for companies that produce a lot of different products or need the flexibility to change their offerings as obstacles arise. In the case of COVID-19, for example, many alcohol distilleries adjusted from their normal production of spirits to make hand sanitizer and increase the supply to match demands.
To make an FMS work, supply chain managers have to be diligent about how they select their performance criteria and define rules and limitations. Once those requirements are in place, the system takes over, prioritizing and scheduling orders and production runs in the best possible way. An FMS brings production lines as close to completely automated as currently possible, determining when machines run and reducing the need for robust planning and strict controls. These systems prioritize reducing unit costs and lead times, improving flexibility, and providing better customer service.
AI is already incorporated into most FMS supply chains due to the automated nature of the system. However, if you wanted to take it one step further, you could add artificial intelligence into your inventory control system, ensuring that you always have the parts you need to keep production running. Additionally, AI can automatically schedule your employees based on their availability, reducing managers’ workloads and ensuring machines are manned when they need to be.
Unlike the other supply chain methods we’ve listed, the optimized production technology (OPT) supply chain system is a computer software package, rather than just a methodology. OPT determines what the optimum schedule and operation sequence are for production lines to better use critical resources and reduce lead times. By weighing each function of a production line against criteria important to the business, OPT decides which procedures take priority. Additionally, OPT can help determine the ideal batch sizes for each component and highlight the areas that are causing bottlenecks.
Unfortunately, production data is rarely completely accurate, so no schedule can ever be perfect. However, OPT doesn’t need a ton of data to work properly, and the data it does need is readily available. For the best results, companies that use OPT must be flexible, knowing that workers at some machines will have idle time while others will need to stagger breaks to ensure highly-used machines are always running. Any production stages where bottlenecks regularly occur will need to be planned in greater detail, while other phases can be planned in more general terms.
Adding AI to your optimized production technology can make better use of your production data to improve forecasting, scheduling, and production planning. Using the collected data, AI can provide your company with actionable insights and show you where your production priorities should be. Additionally, you can set minimum stock thresholds and ensure your inventory system automatically orders new supplies when those thresholds are met.
The decision to change your supply chain method is not an easy one, but it may be necessary with all of the obstacles that COVID-19 highlighted. Restructuring supply chains can be expensive and time-consuming, and everyone in your organization has to buy in to make the change successful. If you want more flexibility in your supply chain, consider switching to a flexible manufacturing system. For mass-production, you might need the materials requirements planning method. Whatever you decide, you should consider ways to incorporate AI to improve your productivity and reduce some of the stress placed on your employees.
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