Supplier Scoring: To be effective, let's be objective! (but not only)
If not every truth is good to say, here is one that might disappoint the unconditional fans of pure modeling: when it comes to supplier scoring, efficiency is not only a matter of numbers. Let me explain.
The score quadrant
When we talk about scoring, we spontaneously think of a scorecard format that allows us to measure the performance of a supplier and its position in the current fleet according to a number of criteria: CSR commitment, quality of service and products delivered, not to mention a whole array of logistical or financial conditions.
However, reality shows that defining a satisfactory scorecard format is far from a simple matter, even when one tries to imagine scoring formats that vary depending on several parameters.
The reason? Simply because it is not enough to combine "Annual revenue" and "Average delivery delay" to achieve a reliable overall score - regardless of the weighting rules. Whether the score is out of 5, 20, or alphabetical, measuring the quality of a supplier solely based on their revenue is nothing short of difficult. Not to mention that beyond a scorecard per supplier, professionals quickly mention the importance of having a scorecard per season, if not per category of products...
Not stopping midstream
It is often suggested to rely on a logic of correspondence, with calculation rules based on a set of numerical data and performance indicators. To simplify things: if a supplier's delivery delay rate is less than 2%, they will be awarded a beautiful 5/5 in terms of "Delivery performance". If this rate is between 2% and 5%, it will be worth a 4, and so on.
Eureka? Not so fast. Indeed, what about our history of activity with this supplier? Its total volume of orders? The potential action plans already implemented to reduce its delays? Our strategic partnership objectives? Let's drive the point home: if I now want to establish a score by season AND by product category: how can I do it?
To be objective, let's be subjective
No one can claim that pure modeling alone would be sufficient and objective. Because we often forget that human perception and many "soft" criteria have a place of paramount importance in managing supplier relationships.
So, let's not put all the information in the same basket! The question at this stage is whether all the data related to a supplier must absolutely fit into a scorecard format?
Does the scorecard remain the only medium for supplier scoring?
No and no again. Because when we translate the expectations of sourcing and supply chain professionals, it quickly becomes clear that the best approach is still a combination of pure data... and subjectivity.
What do we mean by "pure data"? Simply the quantifiable KPIs: number of orders, delay rate, reinspection rate, discount rate, development lead time, number of product references... The desire (and need) to analyze each of these data from complementary angles (season, product category, factory) and to compare the figures of a supplier with those of its competitors for the same country, activity, or seniority seems quite logical, and for good reason!
It is precisely based on these objective and indisputable figures that teams - collectively or individually - will be able to assign scores according to different scorecard criteria, but also include in their rating elements of performance, experience, perception, according to a balance specific to each one.
Thanks to this dual approach of "Scoring" (subjective) + "KPIs" (objective), we finally obtain an effective scoring approach that does not sacrifice quality on the altar of quantity.
Winddle offers a Supplier Relationship Management module, which allows for an objective approach to supplier relationships, beyond personal perceptions, based on KPIs and scoring metrics. By collecting and consolidating data acquired throughout various stages of the supply chain, the data is made available in a structured and standardized way, end-to-end.
The objective is not to eliminate all of the team's personal perceptions, which have significant value, but to formalize them so that they can serve the optimization of the supply chain and supplement them with objective data. To learn more, visit our website or contact us.