پشتیبانی تصمیم گیری الکترونیکی برای مدیریت تأمین تجهیزات: شواهدی مبنی بر اینکه آیا کامپیوترها می توانند در خصوص تأمین تجهیزات تصمیمات بهتری بگیرند

We analyse how well purchasing managers are able to judge the likelihood of problems for a given purchasing transaction. The literature on clinical versus statistical prediction suggests that humans in general, including purchasing managers, are often outperformed by relatively simple statistical formulas for such kinds of tasks. Based on a vignette experiment of real purchasing transactions, we compare the performance of purchasing managers with freshmen students and with a statistical formula based on a cross-validated sample. The results show that the formula outperforms the humans, and that experienced purchasing managers do not outperform freshmen students. We conclude that it would make sense to use decision support systems in the daily practice of purchase management so that humans can devote their time to what they are good at, while being guided by statistical software that takes care of multi-dimensional decisions in noisy environments.

Some purchasing transactions can be foreseen to run smoothly without large investments in time and effort. For other purchasing transactions, a substantial investment in time and effort is necessary. Most people would agree that at least one of the tasks of a purchasing manager is to be able to decide whether a transaction belongs to the first or the second category. Stated otherwise, one of the tasks of a purchasing manager is to decide which of a set of transactions needs purchase management more. For some transactions it makes sense to ask for a lot of tenders, invest a lot in the screening of suppliers, involve lots of time in negotiating, and put a serious effort in writing a detailed contract. For some transactions such investments are not necessary or not efficient (Batenburg et al., 2000). There are, however, compelling arguments on the basis of the literature on clinical versus statistical prediction that suggest that purchasing managers — like all other humans — are typically not good at making precisely these kinds of judgments. We set out to test this assertion. First, we briefly review the literature on clinical versus statistical prediction.

The main thing to note — we cannot emphasize this enough — is that essentially the results as shown here, although perhaps counter to our intuition, are in line with results in the literature in other fields of application. Humans are just not very well equipped to make judgments that combine several dimensions in a noisy environment, and purchasing experts are no exception. One reason for this is that humans suffer from a list of well-documented ‘mental flaws’ that make precisely these kinds of tasks difficult: ignoring base rates, wrongly weighing the different dimensions, failing to take regression-to-the-mean into account, availability bias, and a lack of adequate feedback on the accuracy of past judgments are just a few (see, e.g., Kahneman et al., (1982) or any general introduction into psychology). Perhaps more importantly, one should not only acknowledge that these results exist, but also accept them as an empirical fact and act accordingly. Actually, the main barrier resides in our heads. Grove et al.'s conclusion, supported by the results in this paper, is hard to accept, because it hits us in what we think is one of our proficient skills: choosing wisely in our own area of expertise. Swallowing that pride and analysing the data objectively is the sensible way to proceed: do not let humans do what they are not good at, and do not let purchasing managers do what can be improved upon by using statistical prediction.