In a past CWS 3.0 article, I discussed some basic tenets of ranking staffing partners in a contingent workforce program. In that article, I focused on leveraging the inherent competitiveness of an optimized, staffing partner supply chain to drive excellence in a contingent workforce program. Here, I’ll focus on the topic of SOW partner permutation optimization — how SOW solution partners can consistently produce high-level quality results on specific engagement types, and further, which specific engagement managers are a best performance fit for these SOW solution partners.

SOW partner permutation optimization is a management practice that reviews several possible SOW solution partner variations, in which a set or number of elements can be ordered, arranged and operationally leveraged.

The main permutation elements are:

  • The SOW solution partner’s expertise, capabilities and available resources.
  • The SOW engagement type that can broadly range from a project/product deliverable to an ongoing service.
  • The engagement characteristics inherent in an engagement manager’s style and approach to SOW engagement management.

The goal is to sort through SOW engagement performance data and match the best SOW solution partner with the best projects/services and the best engagement manager.

Of course, other considerations that will affect SOW partner permutation optimization will be elements found in a standard engagement formulation, such as time, cost and quality requirements. These elements can be sorted through using the “Iron/Constraints” triangle methodologies when deciding which SOW solution partner can best meet the constraints of an engagement situation across standard parameters.


Over time, the optimization of SOW supply chain resources will be based on tracking the performance of specific engagement types with specific engagement managers. Segmenting specific SOW solution partners into areas of expertise by engagement types will be a natural step. Then you can optimize for cost-effectiveness and known performance delivery levels. You will also have to consider demand levels for recurring engagement types along with strategically sourcing new additions to the SOW solution partner portfolio.

Most of the above is fairly straightforward. The more complicated level of SOW solution provider optimization is integrating a bench of SOW solution providers with the different operating characteristics of the SOW engagement managers in the organization. Unlike SOW engagements, this is not a significant requirement in staff augmentation services. The core reason for this is that SOW engagements deliver a specific required result that the engagement manager micromanages via a budget, time-frame, content specification and quality level. So executing well during the engagement with the end client is an important SOW solution partner optimization permutation to consider.

Obviously, building quantitative and qualitative knowledge of which providers can deliver a project/service cost-effectively, competently and with high engagement manager satisfaction is critical, but also complicated. This visibility will most probably have to be delivered via a process management application because there are too many management touch points in the lifecycle of an SOW engagement to manage manually. Such an application also creates a knowledgeable perspective on optimal SOW partner permutation performance. You will need some quantitative performance data to make permutation decisions while engagement managers will also offer some qualitative preferences.

SOW solution partner optimization will require a more complex approach than the standard staff augmentation partner optimization practices. It may take a number of actions to sort through the integrated relationships established with SOW engagement managers. But using effective SOW solution partner permutation strategies will create cost-effective and quality delivery value that will go a long way toward matching the best SOW solution partners with engagement managers’ needs and requirements.