Service parts supply chains are all about managing risk in an environment notorious for variable, intermittent demand, and variability in supply. Given the ever-growing strategic importance of service, it is imperative that OEMs optimize their service parts supply chains and exceed their customers’ expectations at lowest inventory cost.
The impact of a missed service target is long lasting, and encompasses expedite costs, penalties and can plummet customer satisfaction while backorders continue to pile up. It is difficult for OEMs to even estimate the impact after-the-fact, let alone try to proactively mitigate it.
The challenges in service parts supply chain start at the basic level. The service level you plan for in the planning system could be different from what you experience in real life! For example, you may have a 4-hr SLA at field locations, whereas planning systems plan for off-the-shelf fill rate and often use different formulas and approximations to estimate service levels.
In many businesses, customers may not count partial fulfillment, which adds the complexity of line fill rate, and your planning system may not be well suited to handle it. Differences between planning system and real life are typically handled by calibrating the planning system. The calibration tends to be subjective, needs heavy involvement from experienced users, needs to be updated regularly, and is not easily repeatable. These challenges grow exponentially as over time you need to make changes to your supply chain network, or change the planning system itself.
When making a substantial change, how can you objectively evaluate the financial impact (and especially the service impact) of the change, before you put that change into production? Many of the changes such as increase in inventory produce results at lead time or later. Years of experience strongly suggest using Monte Carlo simulation for objective evaluation of the planning solution. Service parts supply chain simulation provides a time-phased picture of inventory and service levels resulting from a stocking plan.
Service level and inventory impact is dependent on your stocking policy, current inventory position, forecast, and variability of demand and supply.
Whether you are looking to change your supply chain network or you are evaluating different planning systems, it is recommended that you run the solutions through Monte Carlo simulation and compare the results. This is the best way to ensure a fair comparison between systems because different planning systems may be using different formulas or approximations for service level and one may be overstating the service level when comparing results. To ensure unbiased evaluation, it is recommended that the simulation solution used be an independent third-party tool rather than something developed by the supplier of the planning system. One such resource is the team at SimAcumen led by Dr. Andrea Lobo.
Simulation techniques go beyond mere evaluation of service level and inventory over time. They are able to model real-life situations even better than the theoretical models that planning systems are based on. Simulation can tell you a 4-hr SLA or 8-hr SLA Fill Rate and Line Fill Rate with variability of line size, for example. In this regard, simulation complements the planning system and helps you calibrate it to meet your real-life challenges.
This method of calibration is quick, objective, independent of user skills and easily repeatable. Another common problem for most OEMs is supplier variability – their suppliers have variable lead times – often late, never early. Simulation can model real-life supplier variability, while theoretical models cannot. This approach helps OEMs understand the cost of supplier variability issues, and identify specific “problem parts” based on their impact on service. This insight and knowledge empower collaboration with the supplier to improve variability.
In summary, ensuring an optimal service parts supply chain is strategically important to OEMs. Monte Carlo simulations help proactively manage the high cost of inventory and mitigate the significant business risk associated with suboptimal supply chain performance. These service supply chain simulations complement the planning solution, provide a way to compare, calibrate and extend the planning, and as such, provide an insurance policy for the capital investment in inventory.
To delight your customers in today’s experience economy you’ll need to leverage every advantage available. Don’t overlook the value Monte Carlo simulations can have to improve the health of your service supply chain.