So far, we have explored what kind of problems arise in a typical IoT scenario and how they can be classified into Connected Operations and Connected Assets. Let's now focus on designing and solving a practical business use case for IoT. We will explore how we can solve problems using the interdisciplinary approach of decision science in IoT.

We'll start with a simple problem in the manufacturing industry. Assume that there is a large multinational consumer goods company, say, Procter & Gamble, who owns a plethora of products. Consider their detergent product, Tide, to study our example. Tide is a detergent powder that comes in liquid form as well, has a variety of scents, different cleanliness levels, and so on. Assume that the company owns a plant in which one production line (the assembly line in which the goods are manufactured end to end) manufactures detergent powder. It manufactures 500 Kgs of detergent powder in a single go. The operations...

Problems in real life are often never solo; they are mostly interconnected with multiple other problems. Decision science is also no exception to this feature. While solving a decision science problem, we would often reach a point where we understand that solving the associated problem is more important than the current problem. In some cases, solving associated problems becomes inevitable in order to move ahead. In such cases, we would not be able to practically solve the current problem until and unless we solve the associated problems.

Let's take an example to understand this better. Consider that while solving the problem to identify the reasons for bad-quality detergent manufactured, we inferred that the vital cause for the problem is the difference in raw materials from different vendors or because of insufficient labor in the manufacturing plant (assume). In some cases, the machinery downtime or inefficiency can also be vital reasons for...

Designing the framework for heuristics-driven and data-driven hypotheses forms the foundation of the problem solving framework. The entire blueprint of the problem and problem universe can be captured in this single framework. This isn't a fancy document or any complicated tool. It's just a simple and straightforward way to structure and represent the problem solving approach.

There are three parts to it:

The heuristics-driven hypotheses is the final and refined version of the hypotheses list that we discussed earlier. The matrix captures every minute detail we need from the hypotheses. It helps us prioritize and filter the hypotheses based on data availability and other results. It also helps us gather all our results in one single place and assimilate in order to render a perfect story. Once the entire HDH is populated...

In this chapter, you learned about the IoT problem universe by exploring Connected Operations and Connected Assets in detail. You also learned how to design a business use case for IoT using a concrete example to understand the detergent manufacturing problem in detail and design a blueprint for the problem using the problem solving framework.

This was accomplished by designing the SCQ and understanding how to get started with defining the problem holistically. We also studied about identifying the associated and latent problems and finally explored how to design HDH for the problem.

In the next chapter, we will solve a business use case with a dataset using R. All the context and research gathered in this chapter while defining the problem and designing it will be used to solve the use case step by step.