Probability is one of the very interesting subjects we studied in the Data Analytic course in the LBS EMBA program.
Some of the objectives are:
- Obtain an understanding of the role probability information plays in the decision-making process.
- Understand probability as a numerical measure of the likelihood of occurrence.
- Be able to use the three methods (classical, relative frequency, and subjective) commonly used for assigning probabilities and understand when they should be used.
- Be able to use the addition law and be able to compute the probabilities of events using conditional probability and the multiplication law.
- Be able to use new information to revise initial (prior) probability estimates using Bayes’ theorem.
Furthermore, we learned that the concept of discrete probability deals with circumstances when a random variable’s possible values are countable and finite. The probability theory uses discrete phenomena like coin flipping and dice rolling as examples.
Contrarily, continuous probability applies when a random variable’s possible values are infinitely numerous and can take on any value within a specific range. Examples include a person’s height or an object’s weight. Calculus principles regulate continuous probability, which is used to determine the likelihood of intervals or ranges of values rather than individual values.
As powerful and useful as this tool could be in everyday business problems it can be wrongly applied. The story below illustrates this.
Musa Augie from Augie village in Kebbi State worked as a financial analyst for a medical supply firm in Kano that created a wide range of items for clinics and hospitals in Northern Nigeria. He was responsible for analysing sales data and making projections on future sales so that the company could adjust its production capacity accordingly. Musa was an expert in probability theory, including discrete and continuous probability theory, which he frequently applied to his forecasts.
One day, Musa received a report from the sales team that showed an unusually high number of sales of a particular product in the Jos area of Plateau State. Musa immediately used his knowledge of probability theory to predict that this trend would continue and recommended increasing the production of that product.
Musa’s company developed a sizable batch of the product in accordance with his prediction, but sadly, it did not sell as well as anticipated in the months that followed. Instead, the company experienced a sharp decline in sales and was left with a huge inventory surplus.
Further inquiry revealed that the initial increase in sales had been brought on by the product’s primary rival’s temporary problem, which resulted in a decline in their sales volume. When the problem was fixed, the sales of the rival company’s products were restored to normal levels, and Musa’s company’s sales did likewise.
This lapse in judgment resulted in a huge financial loss for the company. Musa’s projections were based on sound probability theory, but he overlooked the fact that a variety of factors, such as competition, market trends, and consumer behaviour, can have a significant impact on how well a business operates. These factors can be challenging to predict using only probability models.
This experience made clear how crucial it is to use other business analysis techniques, such as market research and trend analysis, linear and regression programming in addition to probability theory to prevent costly errors. Even with the most complex mathematical models available, thorough, and accurate analysis necessitates a wider assessment of all variables that affect corporate operations.
The Ace Automotive Case