The Optimizing Decision Maker

Those responsible for the policy and assets of a food business are (usually without knowing it) optimizers. They try to achieve the best results in the business situation they face while recognizing the technological situation. This means conscientious managers will attempt to use resources efficiently. The usual procedure when optimizing is to array the alternatives, eliminate the infeasible ones from consideration, and then measure the remaining alternatives until a best one is determined (3). Sometimes this is an informal procedure, with little information gathering and minor mathematics, and sometimes it is very formal with structured information requirements and complex mathematics. The science associated with food materials often structures information requirements, the technology provides numbers and determines what is feasible, and the business objectives provide the measuring stick to choose among alternatives. This kind of decision making links the business to the technology in important and useful ways. Not only is the approach structured and systematic, but also for many kinds of decisions there are formal (mathematical) methods that can assist the decision maker.

Decisions that affect the efficiency of a food industry enterprise are made at many levels in the business. Sometimes they are onetime occurrences (should we invest in a new production process?) and sometimes they are recurring (how long should we process that product and at what temperature?). Because some decisions become routine, they are sometimes dismissed as trivial or unimportant. This is not always the case, as routine decisions about food formulations, delivery, processing parameters, production scheduling, and the like have important impacts on costs, profits, and the safety of the food product. If a recurring decision is not optimal, the result is regular losses to the business. In many cases, formal models can be built for these decision situations, and formal methods can be applied to give the decision maker good information to act on. Although the optimization opportunity itself is constrained by the quality of the information available to the user, it still will be the best the food manager can do in the situation he or she faces.

An important piece of optimizing is the objective measure (4). This device allows the user to choose between alternatives—providing a measure of which one is better. There is sometimes a perceived conflict between the objectives of the food technologist and the food industry manager. If the technologist insists on some kind of quality objective—best product, highest-quality product—this may come in conflict with profit objectives of the business. The inputs required to achieve such extreme quality objectives are often so expensive that the necessary product price becomes more than the market will bear. The contribution of food technology in this business context must be to specify what is feasible. Otherwise, the decision will be driven by two (or more) conflicting objectives and will never be optimal.

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