By forcing price concessions and threatening to break contracts, many manufacturers damage their long-term supplier relationships. These aggressive, sourcing tactics reduce margins and create adversarial buyer-supplier relationships. In many cases, this damage will take a long time to undo. Fortunately, a method of collaboration, which depends on underlying analytics to draw out cost inefficiencies, is emerging.Initially, sourcing exclusively for the cheapest price, produced short-term gains for many buying organizations. But while it is always tempting to pressure suppliers for the “best price,” these actions can have unforeseeable long-term consequences. Many manufacturers depend on their supply base for innovation and joint-cost-take-out programs which can reduce total costs over time. But aggressive price discovery and competitive bidding activities can quickly damage a supplier’s willingness to co-invest with their customers if they think that their business relationship may be in jeopardy. In practical terms, bidding processes that drive to the lowest unit cost can actually drive up total costs. For example, by not factoring in the cost of quality, on-time performance, tax, tariffs, duties, and commodity price escalation, unit-cost based bidding can lead procurement organizations to the wrong buying decisions. Companies that use competitive bid processes based solely on price often develop bad reputations. They create an environment where suppliers, who cannot trust their motivations, approach new business opportunities and programs with suspicion. Consider, for example, that GM, a leading American automaker, and the perennial contender for the title of manufacturer that has extracted the most price concessions from its supply base, ranks worst of all the major automotive manufacturers in supplier satisfaction scores. This company can no longer react quickly to the changing market environment through supplier development activities. In addition the manufacturer and supplier can no longer work with the supply community to reduce costs jointly. In contrast, Toyota, one of the most progressive foreign car manufacturers, which has always preached a collaborative supplier management approach, has recently overtaken GM as the number one sales leader in North American sales, and is continuing to build out its global-supplier development operations to develop tighter, more collaborative relationships. Although not quite as detrimental to the supplier relationship as the “best-price” method, some manufacturers have chosen to match wits with suppliers by using activity-based costing models to understand the underlying costs to produce a part. They then leverage the results of these analyses in one-on-one negotiations with suppliers, challenging their partners to prove their assumption wrong. Although these types of processes may eventually root out some cost inefficiencies, they often cause contention and lead to long-term negotiations and analysis paralysis, as each group spends countless man hours unwinding assumptions on either end. Furthermore, these processes are based only on fundamental accounting principles, not engineering or manufacturing costing analysis. Companies can often be led down the wrong path by attempting to reverse costs based on an incomplete information set. Another approach is to make broad conclusions across families of parts based on models of only a few representatives. This is the equivalent of trying to predict a national presidential election based on polling data from only a handful of polling districts and before the voting is over! The one factor all of the above-mentioned sourcing strategies have in common is that the price is set arbitrarily, which can lead to inaccurate costing analysis. On the other hand, a feature-based cost-analytics approach creates an objective, engineering-driven approach to understanding the theoretical “should cost” of a part. Manufactur-ing data is first and foremost, and enough data is used to provide meaningful statistical analysis. In practice, this approach presents an entirely new paradigm for collaborating with suppliers: all parties to the buying process understand the engineering drivers of part-cost collectively. With an intelligent feature-based cost analytics system, engineers are able to understand, for example, the effect of the materials, features, and requirements on the cost of the part. They can then share this information with suppliers who can recommend design or material changes to reduce the cost of a part without impacting its performance characteristics. At the same time, this model enables a more collaborative relationship between the manufacturing OEM’s purchasing department and the supply community by creating a common, neutral language to help both parties achieve their goals. This dialog can enable procurement teams to drive cost reduction efforts while also enabling suppliers to preserve their margins by suggesting ways of reducing part cost or complexity. Moreover, by creating an environment where both the purchaser and supplier have unbiased information and a clear understanding of costs, manufacturers can foster a collaborative spirit for continuous improvement that overcomes the combative environment that many companies created prior to these technologies becoming available. In an example of feature-based cost-analytics in use, one OEM that uses an assortment of cast aluminum parts in their product was able to bring together their designers, procurement specialists, and suppliers to work collectively toward more cost effective designs. The product development team was given accurate cost and design information at the beginning of the process, so they were able to reduce design complexities and develop more commonalities among a parts portfolio, based on individual part features. The procurement specialists were then able to tie this information to a selection of appropriate suppliers with the right capability set, and were empowered to work with these suppliers on a feature-based cost basis to achieve a lower cost by modifying the design, materials and/or production process. The overall result was a significant 15% reduction in costs for the category with no decrease in margins for the suppliers. In today’s competitive manufacturing environment, those companies that choose to ignore the advantages of building closer relationships with key suppliers will face long-term consequences. But with the advent of feature-based cost analytics, manufacturing leaders will be able to work more closely with suppliers to bring products to market. By doing this, they will drive lower unit and total costs by modifying designs, materials and/or manufacturing production processes based on true insight into engineering cost drivers. And they’ll be able to get these results by creating an environment where both buyers and suppliers have transparency into the process and the true cost drivers of engineered materials creating a level, honest playing field which will foster better relationships and better margins for all.
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