At some point in every acquisition process, the dimension of price is considered. Below, I have displayed a typical Acquisition Capability Matrix with regard to pricing. The X axis shows an evolving process. There is nothing prejudicial about any of the elements, but as you move to the right there is escalation that builds upon the prior element. The Y axis reflects approaches that are possible as more data becomes available. In this case, it refers to pricing and/or costing data.
The behaviors in the far left column are typically internally driven, either by management procedure, critical need, etc. The approaches in the middle column are those that are prevalent in organizations that have a more restrictive internal process and look to use and manage external agents or factors. The last column reflects approaches that can best be summarized as outcome driven, where the reward is a being on the “win” side of a perceived win-lose negotiation.
There is no one starting point on this matrix and no fixed migration path to the end point where in all but one of the outcomes a final purchase is exercised. There is also nothing that prevents the discussion to return to any square more than once during the process. For example, in an environment where the organization (or individual) has a more external focus and there is an emphasis on justification of purchases, it is normal to have RFP’s and secure competitive proposals. Once a selected respondent has been identified, the pricing consideration may move up one square to fit the constraints of the budget. In most cases the migration does tend to advance to an adjacent square, but this seems to be more a norm than a rule.
The one other item that is brought into this construct is the z dimension of time, shown here:
Each individual component has various nuances when viewed through a time scale. For example, the need to buy at any price may be highest at the tail end of a project deadline. Alternatively, the financial calculations could be less impactful if the payback period is too far into the future.
Unfortunately, this is as sophisticated as most analysis and negotiations get.
In actuality, there are many multi-dimensional factors at play here. For instance, just looking at time alone splinters into other variables when you think about which timeline we are talking about: Vendor? Manufacturer? Distributor? End user? Integrator? And then what about the impact of the product lifecycle timeline within all of that that?
In order to make a truly data-driven and time-relevant pricing decision, you must have knowledge of the different levers and optimization inputs of the respective selling parties. An example of some of the unfolded dimensional evaluations is shown in the figure below:
Until relatively recently it was not viable to conduct a real time simultaneous optimization of these multi-dimensional variables. However, with the use of distributed processing frameworks, it is now possible to not only collect and compile the data, but to also run meaningful evaluations and comparisons. Using the data available in the public domain and applying focused analytics, it’s possible to obtain geographic specific market competitive pricing for a window of time. By applying Monte Carlo simulation, you can help provide a higher degree of confidence in the key metrics required.
Back in 1597 Sir Francis Bacon opined that knowledge itself is power. And with the correct data, analytics, and a framework of key processes, it’s possible to leverage the knowledge of what a market competitive price should be to avoid overspending.