Leveraging Scorecard Analytics to Optimize Changes to DSAs

June 2, 2016   |   Sunay Shah

In my last post, I explored how IntegriChain Scorecard Analytics can be used to validate whether the implementation of Distribution Service Agreements (DSAs) drives channel performance. In this post, we will deep dive with a step-by-step approach outlining how Scorecard Analytics can be leverage to optimize changes to DSAs.

First, let’s look at the metrics. DSA metrics come in two basic flavors – performance and data transmission The performance metrics are typically composed of a service level metric, purchase stability (or demand variability) metric, and an inventory level metric. The data transmission metrics are typically composed of data delivery timeliness, completeness, and coverage.

Step one in optimizing changes to DSAs is to take a high level allocation to the performance and data transmission metrics. Typically this is much more heavily weighted to the performance metrics and the ratio can range anywhere from four to eight times higher allocation to performance metrics.

Assuming the standard three metrics are being used for the DSA as referenced above, the next step is to determine how many basis points to assign to each performance metric. Depending on the brand family, perhaps service level is more important so that would get higher basis points relative to inventory level and demand variability.

Once this allocation is determined, the next step is to establish the measurement criteria for each of the metrics. For example, defining what an acceptable service level is for a tier 1 award, tier 2 award, tier 3 award, etc. The same holds for inventory level where a high and low DOH level needs to be established.

This is where the real power of Scorecard Analytics comes into play to help understand the impact of changes to the overall basis points allocations for a metric, the measurement criteria for the metric, and the individual tier awards within the metric. With Scorecard Analytics you can run what-if analyses on actual Scorecard data to analyze how overall payment to a trade partner would be impacted by making the various changes under consideration.

For example, let’s take a scenario where an IntegriChain Scorecard user decides to moves basis points from  inventory level to service and increase the service level tier one measurement from 98.5% to 99% while relaxing the measurement of inventory levels from a DOH of 10 to 20 days to 8 to 22 days. An IntegriChain Scorecard user can easily create a Scorecard with these criteria, generate it, and have the data available through Scorecard Analytics for comparison to the existing DSA metrics. The user can load this information through the IntegriChain Builder tool and create meaningful visualizations to make it easier to gain meaningful insights from the data.

Negotiating DSAs is a complicated process but the availability of Scorecard Analytics helps to empower IntegriChain Scorecard users with information to ensure they are constructing the best DSAs possible to drive the appropriate channel behavior with their trade partners.

Tags: analytics, DSAs, metrics, Scorecard