Category: Quantitative Methods Should We Bother

Even if the problem is too complex to rely on the decision proposed by the solution of a model, we should not underestimate the value of model building per se. The model building process itself is a valuable activity as it requires the following ingredients: If model building and model solving result in a solution… Continue reading QUANTITATIVE ANALYSIS AND PROBLEM SOLVING

A quantitative model can be Typical examples of descriptive models that we cover in the book are All of these models are used to generate information that helps in coming up with a decision, but they are not aimed at generating the decision directly. Examples of prescriptive models whose output is the decision itself are… Continue reading Descriptive vs. prescriptive models

Hopefully, the examples in the previous sections have shown the relevance, as well as the limitations, of quantitative analysis for real-life business decisions. In the following chapters we cover a wide variety of tools, which could be somewhat confusing for the reader. Hence, it is a good idea to set a conceptual framework to classify… Continue reading QUANTITATIVE MODELS AND METHODS

An asset (e.g., a stock share) is sold now at a spot price S0 = $50, where t = 0 is current time. The spot price is the prevailing price at which the asset is exchanged at any moment. The spot price in one year is, of course, uncertain, but say that the expected price in one year is… Continue reading The effect of organized markets: pricing a forward contract

A colleague of mine draws the line between engineers (like myself) and economists as follows: In fact, the need for analyzing the interactions between noncooperative decision makers was the driving force for the development of game theory. We will consider related issues in Section 14.3, where we investigate the role of misaligned incentives when multiple stakeholders… Continue reading ENDOGENOUS VS. EXOGENOUS UNCERTAINTY: ARE WE ALONE?

In this section, we consider the mother of all random experiments: coin flipping. This is a good way to get acquainted with probabilities, which are not necessarily relative frequencies, as well as with investments under uncertainty. We illustrate plain coin flipping first, and then some more interesting variations on the theme. Plain coin flipping Assuming that… Continue reading Variations on coin flipping

As we have remarked in the previous section, we often have to cope with the impossibility of gathering in advance all the information we need to make a decision, partly because of uncertainty about future events, and partly because some variable cannot be observed directly. However, this is no good reason not to do our… Continue reading Squeezing information out of known facts

In the product mix problem, we assumed perfect knowledge of future demand, but, unfortunately, exact demand forecasts are a bit of scarce commodity in the true world. Indeed, the standard trouble in supply chain management is purchasing an item for which demand information is quite uncertain. If we order too much, one or more of… Continue reading A problem in supply chain management

We often have to make decisions here and now, without complete knowledge about problem data or the occurrence of future events. In distribution logistics, significantly uncertain demand must be faced; in finance, several sources of risks affect the return of an investment portfolio. In all of these settings, the future effect of actions is not… Continue reading THE ROLE OF UNCERTAINTY

We solved the previous example by a simple rule: Let us pick the most profitable item and try producing as much as we can; if we hit a market limitation, consider the next most profitable item, and go on until we run out of resource availability. However, there must be something more to it. To… Continue reading The case of heterogeneous products