For several years, a major CPG manufacturer based in the Southern United States was consistently falling short of the perfect order target. The company offered a huge mix of stock-keeping units (SKUs) of various sizes, colors and shapes of products, and sold them through four market segments that included wholesalers, retailers, independent dealers and franchisees. The sales group had set one perfect order target for the supply chain to hit for all customer segments, and the idea was to push the demand fulfillment performance ahead of the competition. Segment-specific order requirements included custom labeling on packages, shipping on different size pallets and random customer pickups from company-owned warehouses. The order assembly and shipping process lacked information on stock allocation priorities, which caused, for example, low priority segment's, like franchisee, orders to be shipped ahead of a retailer's order. These complexities, along with setting the perfect order target in line with the competition, caused:
- The shop floor to often take expensive changeovers just to fill a few units for fulfillment, which did not add any more value to the customer.
- Increased investment in inventories to hit a pre-determined service target.
- Delays in launching new products, as filling for service got first priority.
The target was elusive during the peak selling season every year, in spite of increased operational cost. The problem was that the target was not validated by inputs from the different departments internally, and the company did not seek feedback from different customer segments on their fulfillment requirements. Slow moving and near obsolete brands got the same fill priority as high-performing brands. Additional labor and material expediting costs were incurred several times to deliver big wholesaler orders which could have been delivered in parts, as these were for ongoing projects.
In another case, out of a retailer's order of 45 SKUs, 44 were delivered perfect by the company, and one SKU was short by 15 units, and thus did not meet the requirements of a perfect order. But the system had no means to credit the 44 SKUs in the current measurement system. The customer got most of the order filled, and there were no complaints on a number of orders delivered that close. The actual fulfillment performance, as it applies to the customer, was understated and misleading.
In summary, by adopting metrics and targets to get ahead of the competition, the company incurred additional costs for delivering what sometimes was not important to the customer.
Developing Alternate Measurement Methods
The company therefore formed a cross-functional manufacturing/sales team to get customer inputs and develop alternate order fulfillment metrics to correctly reflect requirements of different segments. The method adopted was to set fill targets across brands, and across customer segments of wholesalers, retailers, franchisees and independent dealers.
The brands were classified as power, regular and tail brands for each customer segment. Power brands were the popular and the consistently high-performing products, as well as some low-volume, crucial, "gotta have it" products. Regular brands included the fairly steady performing products with some demand volatility, and the tail brands consisted of low-volume and near obsolete products.
Feedback from the scheduled requirements gathering from customers provided their views of value add, like cutting delivery lead times and response times to brand promotion events, which were then integrated into the measurement system. Additional factors considered for segment service differentiation included customer value, strategic business tie-ups and driving fulfillment performance of certain specialty brands.
As a strategy for growth, the perfect order target for power brands was set at 100 percent for all segments. For the regular and tail brands, the perfect order targets were set in line with historical performance, as customers were comfortable at these levels. The following table shows sample targets for the new metrics.
Benefits Derived From the New Measurement System
Calculating targeted inventory levels by brand to meet planned fill requirements stabilized production runs and cut unnecessary stock build up. The year-on-year investment in inventories dropped by $0.8 million.
System output modification allowed the capture of order fill data daily by brands and by segment, and this was used as a diagnostic tool to drive performance in the areas of manufacturing and distribution.