Appendix: Procurement and Sourcing

Not every category deserves the same effort, and Peter Kraljic's 1983 Harvard Business Review article, "Purchasing Must Become Supply Management," gives the map that is still in daily use. The purchasing portfolio, often called the Kraljic matrix, segments what you buy along two dimensions: the profit impact of the item (its share of spend and its effect on the business) and the supply risk (how scarce or complex the supply market is). Those two axes create four quadrants, each with a different sourcing posture.

• Non-critical (routine) items have low profit impact and low supply risk. The play is efficiency: simplify and automate the buying so it costs little to transact.
• Leverage items have high profit impact but low supply risk, because several capable suppliers compete. Here the buyer holds the stronger hand, so competitive bidding and a should-cost analysis tend to pay off.
• Bottleneck items have low profit impact but high supply risk. The priority is continuity of supply, so you secure the source and reduce dependence.
• Strategic items have both high profit impact and high supply risk, so the posture is a deeper partnership with a limited set of suppliers.

The machined component in this event is a classic leverage item: it is a meaningful share of annual spend, and several vendors can make it to spec. That is exactly the setting where an independent should-cost estimate and a competitive bid earn their keep.

A vendor's quote tells you what the vendor wants to charge. It does not tell you what the part should cost. A should-cost model, sometimes called cost breakdown analysis, is the buyer's own bottom-up estimate, built from the ground up: the material content at current prices, the labor to machine and finish it, a reasonable overhead allocation, and a fair supplier margin. CIPS treats this kind of cost buildup as a core sourcing capability, because it changes the conversation.

The point of the should-cost is not to name a single "right" price and refuse anything above it. It is to give you an independent anchor. When a bid sits well above the should-cost, the buildup shows you which element is out of line, an inflated overhead or a rich margin, so the negotiation has a specific lever rather than a vague "can you sharpen your pencil." A should-cost is an estimate, so it is only as good as its input assumptions, which is why the assumptions travel with the number.

The award decision is the finish line, and the standard is best value, not lowest quoted price. Best value weighs the should-cost gap and the cost elements alongside the things a unit price hides: quality and defect history, on-time delivery and lead time, capacity and continuity risk, and the total cost of ownership over the life of the part. A bid that is cheapest on the sticker can still lose if its low price rests on an element that looks unsustainable, or if the vendor's delivery record would cost you more downstream than it saves per unit.

Here is why the task suits AI assistance. Once the bids are in and the should-cost exists, most of the remaining work is structured: total each bid from its elements, compute the gap to the should-cost element by element, annualize the savings against a stated baseline, and draft a recommendation in a consistent shape. That work is repetitive and rule-bound. The judgment, which vendor is the best value, which risks matter, and which lever to raise first, is where the category manager adds value. That split, deterministic arithmetic on one side and judgment plus narrative on the other, is what the rest of this appendix is built around.