This is especially true with the need to extract data from multiple ERP, logistics, and CRM systems. Supply chain complexities exacerbate these calculations, as does the need for speed and flexibility.
Gartner defines cost-to-serve as a fact-based analysis used to calculate product profitability taking into account services, overheads, and operational charges associated with servicing customers. It’s about understanding the cost to service each and every customer. Cost-to-serve analytics help you determine appropriate profit margins and service levels consistent with customer needs and business objectives.
Cost-to-serve analytics allow companies to understand how much it costs to service their customers by market segment and individually. Specific benefits of cost-to-serve processes include being able to:
As indicated above, applying standard off-the-shelf analytics for cost-to-serve analysis is not always straightforward or easy. Many don’t have the intelligence, data-gathering ability or optimization capability to cut it. Here’s how prescriptive analytics makes the difference between success or failure.
Using prescriptive analytics, it’s possible to model your supply chain network, including procurement, inbound logistics, manufacturing, and outbound logistics. Mathematical formulae represent conversion rates. It’s crucial to include constraints, lead times, and capacities together with business objectives. Use this model to determine cost-to-serve analytics at different levels of detail.
Cost-to-serve models need live data. The solution should be able to extract data, in the correct format, from different data sources, preferably in real time. In this way, your model acts as a digital twin, accurately mimicking your real-life supply chain and accurately representing the cost-to-serve your customers.
Use what-if capabilities to tease out ways to optimize cost-to-serve performance. Apply your business objectives as the objective function and run different scenarios to determine the best way to manage cost-to-serve across the customer base. This will help identify those scenarios where margins are too low and how to improve them.
Adjust the level of detail to measure cost-to-serve at different operational levels and by market segments. It may be necessary to model functions in greater detail.
Choose a prescriptive analytics solution that offers supply chain visibility so you can visualize model performance. You don’t want a black box solution, but rather one where you can view each step and function in a visual display. Good visibility improves accuracy and allows you to adapt or modify models as needed.
Prescriptive analytics is best for cost-to-serve modeling because it incorporates capabilities absent from many off-the-shelf solutions. Some of these are: