An article published in CSCMP's Supply Chain Quarterly provides some interesting perspectives for managing supply chain complexity. The authors present three step approach:
(i) uncover underlying drivers of operational complexity;
(ii) design differentiated supply chain segments tailored to address these complexities;
(iii) create customized end-to-end operational blueprints and performance metrics for each segment.
Using the example of consumer packaged goods industry, the paper presents a segmented approach for supply chain design. In the typology, the y-axis captures three customer segments ("A", "B", and "C") and the x-axis presents four types of product segment - High volume low variability, High volume high variability, Low volume low variability, and Low volume high variability. Five supply chain designs are prescribed. Supply chain design termed as "Lean/Steady Flow" is most appropriate for the customer-product segment characterized by "A" and "B" customers with high volume and low variability. "Responsive" supply chain design is recommended for "A" and "B" customer segments with high volume and high variability. For "C" customers with high volume but varying variability the paper suggests a supply chain design that "implements menu options." "Basic replenishment" is the suggested approach for "A" customers who are characterized by low volume and low variability, whereas, for "A" customers with low volume and high variability the focus should be on "managing complexity." Finally, for "B" and "C" customers with low volume and varying levels of variability the authors suggest the companies should "rationalize SKUs."
For high tech industry the paper presents a somewhat different framework. In this typology, the y-axis represents two types of customers - (i) those who expect short lead times and (ii) those who allow longer lead times. The customers expected short lead times are further segmented into "high priority customers" and "low priority customers." The x-axis is divided into two broad categories: High configuration (i.e. Engineered) and Low configuration. Low configuration products are further segmented into three types - Stable, Non-trendy, and Trendy. For high and low priority customers who expected short lead times as well as high configurations, the paper suggests a "late stock-up" approach whereby inventory is pushed up to the step before massive SKU explosion. For high and low priority customers who expected short lead times and stable or non-trendy products, "reactive loop" criteria is recommended in which production at every step is driven by inventory reorder points at next step. The paper suggests that for high and low priority customers who expected short lead times and trendy products, companies should incorporate "early stock up" where inventory bets are placed to minimize lost sales. For customers that allow for longer lead times (irrespective of the specific product segments desired), the paper suggests an "order to order" approach in which no safety inventory is maintained in the supply chain. In this situation undifferentiated inventory is positioned at suppliers to allow six weeks lead time.
Using the example of apparel/softgoods, the paper emphasizes the need for defining individual segments and then identifying the value drivers for each segment.
Three article informs that in the high tech sector, segmentation typically improve service levels by 5 to 10% while reducing inventory levels by 15 to 20%. In the food processing industry with seasonalities associated with supply and demand, segmentation reduces inventory by up to 40% while simultaneously improving service levels. With appropriate use of segmentation, retailers can also improve margins by 5 to 15% while reducing overall logistics and warehousing costs by as much as 15 to 20%. Three key lessons offered by the article are:
- Pick the right number of segments
- Let customer requirements determine your focus
- Implement segmentation in steps
Source: Swan, D., Pal. S., and Lippert, M. 2009. Finding the perfect fit. CSCMP Supply Chain Quarterly, Quarter 4/2009, 24-32.