End-to-end SC segmentation
- Private Member Discussion
The key points below reflect contributions from multiple discussion participants and have been anonymised (except with permission) and edited to adhere to our 'Chatham House Rule' policy.
End-to-end customer segmentation
- Move from a ‘one-size-fits-all’ approach to customer service which aims at maximum service at minimum cost (which, anyway, tends to fall short somewhere) to a segmented approach which ensures that operational decisions are aligned to commercial priorities;
- High growth / revenue / profitability customers have supply chain resources prioritised and highest levels of service whereas others potentially have slightly lower service levels;
- Products might be segmented based on obsolescence, channel, complexity;
- The combinations of customer and product segments form a matrix which can then be separated into two or three (or more) clusters based on their priority level. Terms vary but they might typically be strategic / prime, standard / sustain and manage. In one scenario, the strategic / prime cluster accounted for 50% of profitability but only about 10-20% of the customer product combinations. In contrast, the manage cluster was between 1-5% of profitability but around 30% of customer product combinations;
- Strategic / prime clusters may go from 96% to 98% OTIF, particularly on the ‘in full’ element and may require higher safety stocks or more flexible delivery. Standard / sustain clusters might continue to have a similar level of service whereas manage clusters may have ordering limited to certain days of the week when cost is lower or capacity is greater, for example. This cluster also presents opportunities for SKU rationalisation;
- If / when supply is constrained but there is spare capacity, the default expectation is that should be used for a prime cluster product / customer whereas before it might have been taken by whatever was available or the customer was low on stock, even though that isn’t necessarily the most profitable item;
- Segmentation / clustering helps develop a common framework and understanding between commercial and supply chain teams to support better strategy and execution.
Challenges
- Data availability for cluster analysis: even standard cost data is good enough to make a meaningful difference to customer segmentation but, where possible, cost-to-serve analysis serves to sharpen those boundaries and build confidence that the changes will have a material impact, particularly because distribution and manufacturing costs tend to be attributed by volume;
- Buy-in from commercial / sales teams: they need to commit to defining service levels (including the implied trade-offs) and then monitor and manage the implementation through metrics like NPS;
- Potential complexity /maintaining the same clusters across different markets: for example, an A class SKU in a smaller, tier 3 market might be a C class SKU in a larger tier one market. Similarly, supply chains with centralised production that export to multiple markets can be harder to cluster. Local regulation governing how discretionary decisions over constrained supply can be as well as trade terms have an impact;
- Customer expectations: in cases where one customer takes a large / full range of products that cuts across SKU classes or segmentation clusters, the customer may expect the same level of service across the board. However, as part of perhaps a longer-term collaboration conversation customers are likely to see the value of having better fill rates;
- Multi-layer complexity: there may be segmentation for forecasting (e.g. by variability & value), service levels (e.g. variability & volume), replenishment, manufacturing and even raw / packs, each of which using different parameters and, therefore, defining different segments or clusters which are difficult to apply across the end-to-end network. However, customer segmentation can serve as a higher level framework helps rationalise and inform strategic and operational decisions that set the context for the sub-level segmentations.
Sign up for monthly updates & session outputs
Tell us which sessions you'd be interested in joining
Top SC Planning Best Practices...Digested
Real lessons learned distilled from a series of practitioner exchanges on...
Digital Transformation of Supply Chain Planning
Digital transformation has been on corporate agendas for some time already but, borne of necessity, tangible progress accelerated significantly since 2020. A silver lining of the pandemic may be that the business case is clearer so the focus has shifted towards implementing and scaling digitalisation initiatives.
Supply Chain Design Modelling and Analytics
Supply chain network (re)design used to come around every few years or so, often based on quite a high level view of customer segments and associated costs. Now, competitiveness and even business continuity relies on the capability to dynamically adapt network flows based on a much more granular understanding of cost drivers.
Volatility, Agility & Resilience: Next-Level Planning
Volatility, Agility & Resilience: Next-Level Planning
Volatility, uncertainty, complexity and ambiguity was already an increasing factor before the pandemic but, now, it is clear that we need the next level of demand forecasting, sensing, planning and execution.
