OPS 445 Week 5 Measuring Network Performance Example

One Hour, Two Hours, Next Truck: Measuring the Performance of a Redesigned Auto Parts Supply Network and Its Partners

[Student Name]

University of Phoenix

OPS/445: Strategic Supply Chain Design and Collaboration

Week 5 Assignment

[Instructor Name]

[Date]

Big River Auto Parts, its measures, results and figures are composites written for a model paper.

Over the past year, the invented Big River chain rebuilt its supply network: 16 hub stores, a contracted shuttle network, vendor-managed batteries, joint planning for brake parts and consignment for slow specialty parts. Its old measures, sales per store and distribution center cost per case, could not show whether any of this worked for repair shops. This paper designs a measurement system for the network and reports its first year.

Frameworks for Measuring a Network

Beamon (1999) argued that supply chain measurement systems often rely too heavily on cost and should include resource, output and flexibility measures. Lambert and Pohlen (2001) found that most so-called supply chain metrics were internal logistics measures and argued for measures that show how each firm's performance contributes to the value of the whole chain and to customers. Stewart (1997) described the SCOR model as a cross-industry framework for describing supply chain processes and comparing performance. Big River uses SCOR's attributes to organize its measures and adds partner scorecards to reach across company boundaries.

Measures by Attribute

Reliability: the shares of commercial requests completed inside sixty minutes and inside two hours, from the order system's request and delivery timestamps; the share of requested parts found at the first store or hub asked, known as first-time fill.

Responsiveness: average order cycle time from request to delivery for commercial orders.

Agility: time to restore normal fill rates after a supply disruption or demand surge, such as the first cold week of winter.

Cost: total supply chain cost as a share of sales, including distribution centers, hub inventory carrying cost, shuttles and commercial delivery.

Assets: network inventory, inventory turns by tier and the cash-to-cash cycle, which adds the days stock sits and the days customers take to pay, then subtracts the days Big River takes to pay suppliers.

Partner Scorecards

Shuttle provider: on-time arrivals within ten minutes of schedule; parts lost or misdelivered per thousand; tracking data completeness.

Battery maker: store in-stock rate for batteries; inventory turns of consigned batteries; response to cold snaps.

Brake manufacturer: forecast accuracy for the joint forecast; fill rate on Big River orders; exception resolution time.

Consignment suppliers: hub stock completeness against agreed ranges; replenishment time.

Each scorecard is reviewed with the partner monthly in the first year and quarterly after.

A repair shop does not care which company dropped the part; it cares that the part was late.

Collecting the Data

Most measures come from existing systems. Request and delivery timestamps come from the order system and drivers' handheld devices; fill rates from the inventory system; shuttle arrivals from the provider's tracking feed; battery in-stock rates from the vendor's daily report; and costs from the general ledger, with hub inventory carrying cost calculated at a 20 percent annual rate. A small analytics team builds the monthly reports and checks definitions with partners so that both sides calculate scorecard measures the same way. In the first two months, the team found that drivers sometimes recorded delivery when they left the store rather than when they reached the shop, which overstated one-hour fill; drivers now record delivery at the shop by scanning a code on the shop's counter.

The Network Dashboard

The leadership team sees six numbers monthly: commercial fill within two hours, first-time fill rate, total supply chain cost as a share of sales, network inventory, cash-to-cash cycle and a composite partner score. Each has a baseline from the year before the redesign and a target.

Results After Twelve Months

Commercial requests filled within two hours rose from 61 to 84 percent, and within one hour from 38 to 57 percent. First-time fill rate at hubs reached 93 percent. Commercial sales grew about 9 percent, recovering much of the business lost to the competitor. Network inventory rose by about $19 million, less than the $34 million planned for hubs, because consignment and vendor-managed batteries shifted about $15 million of stock to suppliers. Total supply chain cost rose from 6.8 to 7.2 percent of sales, within plan. The cash-to-cash cycle shortened by eight days.

Partners performed unevenly. The shuttle provider met 98 percent on-time arrivals after the pilot. The battery program reached a 99 percent in-stock rate and handled the first cold snap well. Consignment suppliers kept hubs about 96 percent complete. The brake joint forecast reached only 71 percent accuracy at the part-family level against an 80 percent target, mainly because Big River's promotion calendar changed after it was shared.

What the Results Mean

The network design worked for its main purpose, faster parts for repair shops, at a cost in line with the plan. Collaboration models that shifted inventory to partners kept the asset increase smaller than expected. The weak point is internal: Big River's own promotion changes undermined the joint brake forecast, so the fix lies in its marketing calendar, not the supplier.

What the Store Managers Saw

Store managers noticed changes the dashboard did not capture. Counter staff spent less time calling other stores to locate parts, since the hub's stock was visible on their screens. Some managers felt they had lost control of their own inventory to the hub and to vendors; the chain added a monthly call in which each region's managers review hub performance and suggest parts to add or remove.

Recommendations

Freeze promotion calendars 60 days ahead and share changes the same day; add two rural hub-like stores where two-hour fill remains below 70 percent; extend consignment to a fourth supplier of electrical parts; and add an agility measure for storm and cold-weather surges with a target recovery time of three days.

Conclusion

Measuring Big River's redesigned network required looking beyond its own stores to its tiers and partners, using SCOR's attributes for structure and partner scorecards for reach. After a year, repair shops get parts much faster, assets grew less than feared and partners mostly met their commitments. The one lagging measure points back to Big River's own behavior, which is exactly the kind of insight a network measurement system should provide.

References

Beamon, B. M. (1999). Measuring supply chain performance. International Journal of Operations & Production Management, 19(3), 275-292. https://doi.org/10.1108/01443579910249714

Lambert, D. M., & Pohlen, T. L. (2001). Supply chain metrics. International Journal of Logistics Management, 12(1), 1-19. https://doi.org/10.1108/09574090110806190

Stewart, G. (1997). Supply-chain operations reference model (SCOR): The first cross-industry framework for integrated supply-chain management. Logistics Information Management, 10(2), 62-67. https://doi.org/10.1108/09576059710815716