Referencing my last post (Double Marginalization and the Decentralized Supply Chain, August 9, 2007) double marginalization “DM” is defined as the “exercise of market power at successive vertical layers in a supply chain.” The problem that arises as a result of DM is tied to an impetus to mark up the product’s price above marginal cost. The sequence of mark-ups “leads to a higher retail price and lower combined profit for the supply chain.” In short, DM drives the paradoxical outcome of higher buy prices with lower sell profits.
The root cause of DM is linked to a downward sloping demand curve that is related to the buyers’ anticipated expenditure of time and effort to research the pricing and/or the characteristics of the product or products they are looking to acquire. Due to increasing demands placed upon the individual buyer once a particular seller has been selected their natural inclination is to “stay with that supplier as long as they find the exchange satisfactory.” It is the prospect of a lengthy research process that stifles buyer willingness to look elsewhere while simultaneously fueling supplier motivation to increase price. As a result when multiple firms within a particular supply chain are in this position, the “sequence of mark-ups” leads to the higher selling price and the lower combined profit.
Where you are in the “chain” makes all the difference What the majority of papers on the subject of DM and the corresponding download sloping demand curve have not touched on is stakeholder impact at various points in the transactional stream itself.
My own research confirms that there is a continuing diminishing rate of selling profit mirrored by a steadily increasing buying price as the transaction progresses through the supply chain to the eventual end-customer. In essence there is a chain reaction of profit decreases mirrored by an equal reaction of selling price increases. The point at which these two “paradoxical” lines meet is what I refer to as the “point of ideal price viability” (or PIPV if you like acronyms). This can also be referred to as the range of price reasonableness. (It is of critical importance to note that the PIVP is influenced to varying degrees by a number of factors including a product’s individual commodity characteristic. For further information on commodity characteristics and their impact on pricing performance please refer to my Acres of Diamonds paper. If you do not have a copy of the Acres paper, send me an e-mail at jhansen@procureinsights.com with “Acres” in the subject line and I will forward it to you.)
Within this range the overall value attributes (i.e. pricing, quality, service level, seller margin, FTE’s etc.) converge where the maximum win-win benefit is realizable by all transactional stakeholders. Knowing at what point in a transaction stream your organization’s PIPV occurs is critical to both short-term success and long-term “sustainable” profitability.
A Case in point (what not to do)
An IT Service organization “Provider” contracted with a third party logistics/parts reseller to improve performance on a major government contract that was on the verge of being cancelled. Specifically, the Provider’s 51% next day response rate fell far short of the 90% Service Level Agreement “SLA” response rate they were contractually obligated to meet. After a somewhat lengthy analysis it was determined that the acquisition and delivery of service parts was the primary reason for their poor results.
Given that service parts especially for an aging install base, usually demonstrate a Dynamic Flux characteristic (see the Acres Paper), the risk of missing the PIPV was dramatically increased. Factors such as the depth of the transactional supply chain combined with the Provider’s relatively narrow window of price variability knowledge further exacerbated that risk.
The Provider’s decision to outsource the procurement of service parts by virtue of the fact that their buyers were instructed to deal exclusively with the third party logistics/parts reseller completed the picture by firmly establishing a downward sloping demand curve environment.
The end result While their objective to improve contract performance was met within the first 3 months of engagement (the next day SLA response rate increased from 51 to 97%), the Provider’s strategy resulted in the end-customer “client” paying an average 150% mark-up on all part purchases. This is a premium that would likely have dampened the client’s enthusiasm in terms of the improved response rate. It is worth noting however that the client ironically contributed to the higher part costs by locking the Provider into a contractually agreed upon percentage mark-up. This meant that in order for the Provider to achieve their internal revenue objectives for the contract (a condition the client did not take into consideration) they were actually motivated to pay a higher per unit cost for each part.
This unfortunately is a common occurrence with most IT Service organizations and contracts in that the combination of limited price variability knowledge, with Dynamic Flux commodities and the desire to engage as few supply partners as possible creates little opportunity to recognize and achieve a true PIPV.
(Note: one of the questions that I am often asked deals with what I call supply chain compression. Specifically, if you reduce the number of stakeholders within the chain of supply will you not also reduce the number of opportunities for escalating mark-ups? Putting aside factors such as the difference between Dynamic Flux and Historic Flat Line commodities, or that the term “supply chain” is really a misnomer, just because your organization limits the size of its supply base does not mean that the size of the overall market and therefore its effect on pricing has changed. All it means is that your organization has narrowed its window of price variability knowledge and created an artificial downward sloping demand curve. This is particularly troublesome when the strategy is broadly applied across the entire enterprise. I will delve further into this all too common practice in an upcoming segment.)
Conclusion As I had indicated in my August 9th post, the difference between traditional “supply chains” and the emerging Metaprise supply networks is that the research expenditure time on the part of the buyer is dramatically reduced. As a result the propensity on the part of the supplier to increase prices is kept in check due to the buyer’s expanded window of visibility over a much broader supply base. This in turn helps to increase the opportunity to identify the “point of ideal price viability.”
Double Marginalization and the Point of Ideal Price Viability - To learn more about this author, visit Jon Hansen's Website.
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