During the sourcing process, it is common for procurement to run several rounds of optimization scenarios. They are usually conducted either in advance of negotiation discussions with suppliers or before final award decisions become contracts. The importance of optimization is that it helps decision makers understand how their assumptions and constraints change the relative advantages of each qualified option. Context is king when it comes to optimized rankings in procurement.
But as we are so fond of delineating through the terminology we use, procurement is not the same as purchasing. These days, most of the actual buying is handled in a distributed fashion by colleagues working in other parts of the company. And even when there is an eProcurement solution in place to standardize the process, most buying activity is tactical at best. For the total value of each contract to be achieved, purchasing and inventory management have to be optimized as well, allowing changing circumstances and context to have the proper influence in each buying decision.
Realistically speaking, there is no one way to optimize individual buying decisions, at least not one isolated way. Instead, multiple interconnected steps have to be taken to ensure that orders are filled without tying up capital in unnecessary safety stock that is immediately in danger of becoming obsolete. Beyond being inefficient, non optimal buying carries the risk of consuming all of the savings procurement managed to negotiate in the sourcing process.
The three steps in an optimized buying program are:
Step 1: Intelligent Forecasting
Even when forecasts are based on solid assumptions, they have to be continually revised and brought up to date if they are to remain relevant. As orders are moved up, back, increased, or cancelled, item level demand forecasts have to be adjusted as well. But incorporating those factors in a demand forecast only addresses the changes that need to be made in response to placed orders. To deserve the label ‘intelligent,’ inventory demand forecasting has to get out in front and predict demand levels associated with orders that have not been placed yet. Managing the effects of seasonality, trends, and outliers across every item in the warehouse with enough precision to achieve efficiency requires daily calculations for buyers to incorporate as they make purchases.
Step 2: Dynamic Safety Stock
Even when forecasting is handled intelligently, companies are still going to need some safety stock on hand. The question is, how much is the right amount? The trick to setting appropriate levels of safety stock is to give them the same optimized treatment that the demand forecast received in step 1. All items will be affected by the historical accuracy of the demand forecast, but there are also item and supplier specific variables that must be considered. Some suppliers are more reliable than others, and some items require a longer replenishment time. In the case of safety stock, the level of each product on hand has to be dynamic in terms of how it responds to forecasts as well as in how granularly the influencing factors are reflected.
Step 3: Cost Optimization
Managing the forecast and determining the right levels of safety stock are key inputs that inform buying decision makers, but each specific purchase benefits from a cost optimization all its own. By ensuring that minimum order quantities, packaging units, ordering costs, and purchase prices are taken into account, every purchase meets the immediate needs of the buyer as well as the cost priorities of the organization. Cost optimization at the moment the order is being placed carries forward the spirit of the work done in forecasting and safety stock calculations – taking a unique set of items and requirements and making sure that the order is placed as efficiently as possible.
Just as procurement has learned in sourcing-based optimization scenarios, decision supporting analytics quickly expand beyond what spreadsheets and ‘back of the napkin’ calculations can handle accurately. Automation is required to bring intelligence to decision making without forcing the buyer off their process or adding time to the order process. In addition, having a solution with the right depth of functionality makes it possible to manage both high level and item or supplier specific factors in each decision.
All of the contingencies – forecast, safety stock, order conditions – that must be incorporated into each buying decision require far more optimization power than a person can keep to of mind while also placing an order quickly and conveniently. Organizations need to ensure that they incorporate automation where appropriate to make sure each purchase is placed under the best conditions possible.