Enterprise: Brunswick Corporation is a leading global manufacturer and marketer of boats and other sports and leisure equipment and accessories.
Category: Global Freight spend for 600 shipping lanes and 3 container types.
Strategy Employed: Multi-stage e-RFx, utilizing an embedded optimization engine for decision analysis.
Description: Brunswick needed a faster, more effective way to source its global freight spend. Brunswick structured a multi-stage e-RFx that accepted final bids in sealed bid format from 20 suppliers. The sourcing team then utilized the bid optimization engine to create various award scenarios for the 6,000+ data points based upon different combinations of allocation and business constraints including container size, number of suppliers per region, and cost.
Results: While the award decision was not the lowest cost scenario, significant savings were achieved. The sourcing team now has a full view into the different cost components of their global freight suppliers and will be better positioned to track/predict future price movements. The sourcing cycle time for this category was cut in half and the team now has a template to leverage for future e-sourcing events in this category. Brunswick also believes that the development of an RFx template combined with bid optimization will save the team an additional six months on each future freight bid.
Enterprise: Food and beverage company
Category: $80 Million in Diesel Fuel
Strategy Employed: Reverse auction and bid optimization
Description: Facing dramatic increases in diesel fuel prices, the sourcing team took an advanced sourcing approach to managing costs while balancing the needs of 5 separate business divisions. They conducted a series of reverse auctions by geography that focused on supplier margins and transportation costs (cost components of delivery vs. actual fuel costs) to capture supplier bids. The reverse auction results flowed directly into a bid optimization engine which was used to determine optimal supplier awards given specific business constraints per business unit and location.
Results: The company captured competitive pricing and optimized total cost awards within 3 days. In an escalating fuel market, they achieved savings on more than half of locations while ensuring all business constraints were met. The team also gained a much better understanding of the supply market and the cost structures in this category.
Category: $30MM in Pallets to be delivered to 25 unique distribution centers.
Strategy Employed: Flexible bidding with optimization after earlier reverse auction.
Description: The purchasing and supply management team at USPS needed to source its supply of shipping pallets for 25 DCs across the U.S. After an earlier reverse auction, the category team felt that there was an opportunity to improve the results based upon their historical offline strategic sourcing experience. Using the reverse auction as a baseline, USPS created a flexible bidding event with optimization that enabled suppliers to participate in a second round of bidding where unconstrained by specific requirements. Suppliers were able to define the structure of their entire bid and express conditional discounts.
Results: USPS’ use of flexible bidding with optimization enabled suppliers to define: a) specific bid bundles, which created new volume discounts, b) production schedule changes c) flexible delivery times d) supply locations, and e) payment terms. An incremental savings of 9% was achieved by allowing the suppliers to define the market for pallets. An additional benefit was a key location no longer being supported by a single source.
These examples are all taken from the recent Aberdeen study on advanced sourcing and negotiation and show how companies use advanced techniques in an eSourcing application. All of the categories are traditionally thought of as “difficult” which translates into un-started projects in many companies. The common theme shows that complex categories can be addressed through eSourcing technologies (even reverse auctions) and usually are supported by using many rounds of complementary functions within the application.