Many in Procurement and Strategic Sourcing espouse the use of should-cost or clean-sheet models, benchmarks and other behavior sensing models in support of negotiating prices on direct and indirect spend category products & services. And in theory, these models appear to have some value. But from a practical matter, most of the activity around the application of these theories are largely ineffective.
Benchmarks
I’ve written before about the ‘fool’s gold’ that is benchmarking. Benchmarks are a notoriously poor substitute for intelligence about market-based prices. This is so for numerous reasons including:
- Benchmarks are stale before they are published. It takes time to obtain, aggregate and publish benchmark data. The same data is sold and re-sold for many quarters, sometimes many years, despite changes to market conditions, technology evolution, competitive environment, and other facts that influences the unit price of the product or service. This data staleness renders the benchmark worthless.
- Benchmarks are an average of many price points. By definition, benchmarks are an average price-metric for a product or service across numerous data points gathered by the benchmarking organization. The problem? Who wants to pay an industry average price? Isn’t it your job to obtain the best possible price given your requirements?
- Benchmarks lack transparency of business requirements underlying the price. That is, most (all) benchmarks lack qualitative descriptions. For example, does the benchmark price reflect an order that is consistent with your needed demand profile or does the benchmark price reflect a profile that is 10%, 50%, 10x or 100x of your needs? Are the T&Cs – which typically drive a substantial premium or discount to base price – consistent with your demand profile? What about the skill of the negotiator(s) behind the benchmarks – did they get the best deal or did they just settle for an “average price”? Recognizing that volume, T&Cs and negotiating skill are a subset of the essential determinants for price, it is evident that most benchmarks are just “noise”.
Price benchmarks may alleviate some level of uncertainty about our deal pricing levels, but recognizing that most benchmarks aren’t worth the paper they are written on is essential for those working toward best-in-class deals and preferred pricing.
Should-cost price models
Should cost models are intended to reverse engineer the cost of products and services, thereby enabling buyers to dis-aggregate vendors’ true costs and the implied margin based on a given price. As with benchmarks, theoretically should-costs model can be valuable except for the harsh realities of the real-world, including:
- Building relatively accurate should-cost models is extraordinary difficult, and often fraught with assumptions stacked on assumptions. Even the simplest of services model is challenging to estimate with any level of precision. Consider a should-cost model estimate for labor in a BPO service: the pay rate (the salary & benefits paid by the service provider to its employee) can vary substantially, depending the country from where the service is being delivered. On top of this, estimating overhead (beyond the traditional 30% charge) is difficult. As a result, the implied profit premium can vary wildly thereby diminishing the buyer’s ability to identify cost reduction opportunity. And this complexity is for the simplest of services…
- Vendors’ sales team will of no help to you in developing these models. Most salespeople have no clue about what the true cost of the products & services they are selling nor do they have access or authority to opine on internal costs.
- Vendors unlikely to accept the validity or acknowledge the models. But let us just say you’ve built a beautiful model and you determine that should-be pricing for a widget is $10 while your bids are north of $20. What now? You tell your vendors their prices are too expensive, and they tell you that your model is wrong and that the pricing is what it is. In other words, you have bupkus.
The reality is that should-cost models are really only applicable when your purchasing power is immense with the potential to shift to market share distribution in each industry – a situation that does not apply to most firms sourcing most spend categories.
Other Models
A new breed of artificial intelligence (AI) models claim to be able to price based on “behavioral science” around the aptitude and motivations of the salesperson. Again, in theory this is an interesting approach, … if not for the myriad of real-world issues:
- Predicting human behavior is extraordinary difficult. On paper, we are logical animals – yet in practice, most will agree that our actions often contradict logical conclusions.
- Your exposure to vendors’ salespeople isn’t sufficient to power AI models. That is, you lack sufficient data to “feed” AI models. Your real interactions with salespeople during a deal is likely no more than a few months, and the average tenure of a salesperson and sales managers is just 12 – 18 months. Assuming the AI model was capable (and this is an enormous assumption), the lack of exposure to and the short tenure of a typical salespeople will eliminate any possible value an AI model can bring to bear.
- True decision-makers in most deals are rarely seen by buyers. Recognize that most salespeople have (very) limited authority to negotiate price & terms. The true decision-makers –VP Sales, CRO, CSO, CFOs, CEOs, etc. – are usually in the background and rarely seen by the buying organization.
Unless AI models are clairvoyant, most of these models will not have the needed data to provide any valuable insights.
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So, the deck is stacked against the buyer; and the tactics of benchmarks, should-be pricing, and behavioral science is not going to move the needle. What’s the answer?
Recommendations
There are no short-cuts on the road to generating material results. The best way to benchmark pricing is through a true price-discovery process wherein you bid volume along with requirements (business requirements, KPIs/SLAs, T&Cs, etc.) to the market. You do this intelligently to create transparency of your needs and obtain the needed information from the bidding vendors to enable you to identify most-favored-nations (MFN) pricing.
We recommend a comprehensive program, including:
- Apply Strategic Sourcing 101 principles. For example, eliminate one of the major obstacles to success by moving your sourcing activity from event-centric activity to category sourcing. Just consider – would you get a better price buying one pencil at a time or negotiating a deal for 1,000,000 pencils which covers your enterprise-wide demand?
- Consolidate, consolidate and consolidate. A typical firm has 1,000 vendors for every $1 billion in Revenue. Of these vendors, 600-800 vendors are considered the “tail” and are typically never sourced. That means that most of the buying with “Tail” vendors – equivalent to $60 million – $180 million in vendor spending – is at or near retail-level pricing. Consolidate spend to a small group of vendors and SKUs.
- Have vendors bid on dis-aggregated unit prices and obtain component pricing from OEMs (when possible). Doing so will enable you to leverage each vendors’ pricing during negotiations – with target price at or near Synthetic Minimum price point.
This is a lot of work! Are you crazy? I don’t have time for this…
Our experience with the above approach – together with other strategic sourcing best practices – yields total vendor spend cost reduction of 20%+ and ongoing unit cost reduction of 5%-10% annually. In addition, firms following this approach eliminate substantially all “busy work” and finally have time to engage stakeholders and vendors in value-added discussions. If your team lacks the bandwidth, I know of a consulting firm that can help you generate tangible results.
Are a few months of work worth $30 million – $50 million cost reduction on every $1 billion of revenue?