As I highlighted in Questions to Ask your Optimization Vendor, *not all optimization vendors are equal* … and, more importantly, *not all vendors that claim to have decision optimization even have it*! Thus, not only is it important to know what to look for when searching for a true strategic sourcing decision optimization, it’s also vital to know what to ask and what answer you want to hear.

In this post I’m going to cover five key questions that you should ask of *every* vendor you are considering. Some of these overlap the questions I x-emplified in my previous post (which you should re-read), and a few of them are new. Of all of the topics I am covering, this is probably one of the least understood – and since this is one of the few technologies with the capability to allow you to reduce your spend year-over-year when properly applied – this situation has to change.

** 1. Does the application satisfy the four pillars of strategic sourcing decision optimization? **

As outlined in the Strategic Sourcing Decision Optimization wiki-paper on the e-Sourcing Wiki [WayBackMachine], the four pillars of strategic sourcing decision optimization are:

*Sound & Complete Solid Mathematical Foundations*

such as simplex algorithms and branch-and-bound;

many simulation and heuristic algorithms do not guarantee analysis of every possible solution (sub)space given enough time, and, thus, are not complete in mathematical terms*True Cost Modeling*

many bidders bid tiered bids, discounts, and fixed cost components – the model must be capable of supporting each of these bid types*Sophisticated Constraint Analysis*

At a minimum, the model must be able to support reasonably generic and flexible constraints in each of the following four categories**Capacity / Limit**

allowing an award of 200K units to a supplier who can only supplier 100K units does not make for a valid model**Basic Allocation**

you should be able to specify that a supplier receives a certain amount of the business, and that business is split between two or more suppliers in feasible percentage ranges**Risk Mitigation**

let’s face it – supply chains today are all about risk management, and you should be able to force multiple suppliers, geographies, lanes, etc. to mitigate those risks without specifying specific suppliers, geographies, lanes, etc. to take advantage of the full power of decision optimization**Qualitative**

A good model considers quality, defect rates, waste, on-time delivery, etc.

*What-if Capability*

The strength of decision optimization lies in what-if analysis. Keep reading.

** 2. Does the application support the creation and comparison of multiple what-if scenarios? **

The true power of decision optimization does not lie in the ability to find a solution to one model, but the ability to create different models that represent different eventualities (as this will allow you to hone in on a robust and realistic solution), to create different models off a base model plus or minus one or more constraints (as this will help you figure out how much a business rule or network design constraint is really costing you), and to create models under different pricing scenarios (to find out what would happen if preferred suppliers decreased prices or increased supply availability).

** 3. Does the application automatically identify the most constraining and costly constraints? **

Let’s face it, not every constraint has a significant impact on the optimal solution, if it even has an impact at all. Restricting the highest cost supplier to 30% of the total award is unnecessary if the supplier is not going to get any award. However, restricting the lowest cost supplier to 20% of the award could be the most restrictive constraint in the scenario, as the supplier would get 80% of the award otherwise.

The solution should identify, in order of decreasing impact, which constraints are having the greatest effect on the optimal solution and, at the very least, provide a range estimate of how much the constraint is costing you in the model. Determining the constraints that significantly impact a scenario can be done deterministically – they are at their bounds. Determining the constraints that impact a scenario moderately can be done through a deterministic comparison with the optimal solution to the “unconstrained model” (where only supplier capacities, demands, and cost constraints are included). The rest of the constraints then impact the model slightly or not at all. Calculations that take into account the differences between the optimal solution to the model and the optimal solution to the unconstrained model can be used to provide a reasonable estimate of the cost of any particular constraint. Furthermore, an exact cost associated with the removal with any constraint subset can be determined by optimizing the modified model. This brings us to …

** 4. Does the application support the automatic creation and solution of relaxed and perturbed scenarios? **

After the constraints with the most significant impact, particularly from a cost (or risk) perspective have been identified, it’s only logical that you want to know not only how much they are costing you, but how much a relaxation (as opposed to a removal) of the constraint would save you. For example, if you allocated 30% of an award to a new vendor vs. 20%, what would you save? The reality is that you really want to understand not just the cost, but the “cost per unit” of the constraint. If you have allocation splits, you want to know the effect of minor and moderate changes to the splits. If you have limit constraints, you want to know how much you could save with increased capacity (and, thus, whether the company should be making an investment into new technology or more production lines or entering into a strategic partnership with a key supplier to lock up more capacity). If you have qualitative constraints, could you save more if vendors increased their quality by 10% across the board (which is equivalent to allowing a 10% decrease in quality level in the model)?

For each constraint type, it’s pretty easy to come up with a standard set of “perturbations” that you would want to analyze using what-if analysis. The application should support standard perturbation templates that you can use to set up an over lunch (or overnight) run against a well-formed what-if scenario that would generate a variance report that would tell you not only what constraint relaxations would save you the most, but how much a per unit perturbation against the constraint would save you and let you hone in on an award allocation that will have the lowest total cost of ownership over the life of the contract – and not just on the day you run the what-if scenario.

** 5. Does the application support make-vs-buy and arbitrary product substitution? **

If you’re only sourcing indirect, you might not care about make-vs-buy, but you should care about product substitution. Let’s say you’re a major player in the food service industry who caters to the average joe’s love of pizza. Since few pizzas are made without tomato sauce, you’re going to need a lot of it. But guess what – if you ask a supplier for “sauce” they’re going to say “how much”, “what type of packaging”, and “refrigerated or frozen”? Chances are there are 10 different “products” for you to choose from. And it’s not as easy as just saying “whatever is cheapest” or “I’m standardizing on form factor 27” because “whatever is cheapest” will vary by production plant (some types of packaging will be cheaper in some countries than others, some plants have newer technology for certain kinds of packaging, and packaging weights and volumes determine shipping costs). Furthermore, the availability of products is probably going to vary across locations. The way to get the lowest cost is to allow a supplier to bid ALL products that can meet your needs (and, of course, account for any variances in processing costs through adjustments). Thus, you want your strategic sourcing decision optimization solution to support arbitrary product substitution.

However, if you’re sourcing direct, you’re really going to want make-vs-buy analysis. Let’s go back to Mr. Martyn’s automative seat example. Do you source the seat? Do you source seat components and assemble them? If so, how do you define a “component”? Or do you source all the raw parts and assemble them? The reality is that even in this simple problem, you have over 1000 different options. Thus, creating a model where you source the finished components, creating a model where you source specific sub-components, and creating a model where you source all the parts, and doing a comparison report on their optimal solutions just doesn’t cut it. You might find that you save 10% by sourcing the components and having a third party assemble them, but who’s to say that there isn’t a different configuration of components that would let you save 20%? If you gave your suppliers the flexibility to choose their own components and the third party assemblers the opportunity to bid on the components they think they could assemble into the final product most cost effectively, who knows what innovation they might identify? In a make-vs-buy scenario, you can’t assume you know what the proper subset of models to analyze is. You really need to analyze ALL the options available to you.