One integral aspect of the after-sales supply chain is spare parts management. If you would like to find out why supply chain organizations currently face growing expectations from customers and how to respond with new technologies and strategies, you should first read this post.
1. Delink from Your Forward Supply Chain
Separate worlds have a positive aspect sometimes, particularly for your company’s forward supply chain and after-sales supply chain. In the organizations that fail to clearly separate their service-related and product-related supply chain tasks, the after-sales organization regularly loses out.
Product business is just too dominant, which means that investments in systems and processes primarily benefit the forward supply chain while the after-sales chain lags behind. Still, the requirements for each one differ significantly. So, as an after-sales organization, it is important to work with your own budget and invest to meet your specific requirements.
2. Document Any Bottleneck Situations Fully
Unfortunately, as it is often the case, spare part stocks don’t cover the service demand. The alternative would be disproportionately high investments in spare part stocks that no sensible company would ever accept.
Investigate why there are shortages and ensure that they are fully documented. In most instances, a delay somewhere along the supply chain is what’s causing the shortage. If you stay on the ball and analyze all delivery bottlenecks, you will unearth recurring patterns that can be incorporated into your spare parts planning.
3. Establish a Stock Check Process
Planning is obviously good, but control is even better. While you might run the risk of your service partners assuming that you are a micromanager, it is generally advisable to regularly check spare part stocks along the whole after-sales supply chain.
Spare parts logistics often cause issues. Spare parts are mainly regarded as a financial risk, particularly when repair shops and service centers work on their own account. To be sure that your partners don’t have too few spare parts in stock, it is important to keep control of stock levels.
Even though you might not have any direct influence on your partners’ purchasing behaviors, regular stock checks alone can have a positive effect. It suggests that everything is under control.
4. New Products Should Have Delivery Commitments
When your company is launching a new product, all the priorities shift to ensuring that the launch is perfect. Production should be capable of guaranteeing that demand is actually met and that your logistics ensure seamless distribution, But who’s ultimately responsible for the availability of spare parts?
To avoid spare part shortages immediately following the launch of new products, you as the after-sales organization should demand for binding delivery commitments from colleagues in product planning when you launch a new product.
Predicting how new products are likely to behave is difficult, which makes it all the more important that your production is capable of supplying spare parts at short notice if required.
5. Intelligent Connectivity to Improve Forecast Quality
Demand patterns that may be difficult to predict determine the need for spare parts. End user behavior is the greatest unknown factor because each customer uses their product in a different way. Service requirements may vary greatly depending on the conditions under which they use their devices.
Thanks to the Internet of Things that’s resulting in increased connectivity, we are taking a big step forward when it comes to solving the problem. Intelligent connected devices that come equipped with sensors send signals to the service organization that can then be used for precisely forecasting the demand for service and spare parts. The approach might still be a long way off, but the concept looks promising and the technical conditions are in place already.
6. Put Your Trust in Artificial Intelligence
The providers of specialized software have been developing algorithms to model in spare parts management demand behavior for years. The reality is that the big breakthrough was long overdue. The demand patterns are too unstable and the planning parameters are too varied. It is apparent that rigid algorithms cannot overcome this challenge.
Machine Learning, on the other hand, opens up a new perspective. Artificial Intelligence can process automatically and autonomously get to grips with large tasks that contain large volumes of data. While previous software solutions failed because of the complexity of spare parts management, the new attempts at a solution have shown great promise: millions of spare parts variants, fluctuating error rates and other variables – all this is a classic case for artificial intelligence.