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Smarter Decisions - The Intersection of Internet of Things and Decision Science
Smarter Decisions - The Intersection of Internet of Things and Decision Science

Smarter Decisions - The Intersection of Internet of Things and Decision Science: A comprehensive guide for solving IoT business problems using decision science

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Smarter Decisions - The Intersection of Internet of Things and Decision Science

Chapter 2. Studying the IoT Problem Universe and Designing a Use Case

IoT is spread across the length and breadth of the industry. It has touched every possible industry vertical and horizontal. From consumer electronics, automobiles, aviation, energy, oil and gas, manufacturing, banking, and so on, almost every industry is benefiting from IoT. Problems arise in each of these individual business areas that need to be solved connoting the industry it is addressing, and therefore people often segregate the wide spectrum of IoT into smaller and similar groups. Thus, we see names such as Industrial IoT, Consumer IoT, and so on being referenced quite often these days. Keeping aside these broad divisions, we can simply divide the problems to solve in IoT into two simple categories, that is, 'Connected Operations' and 'Connected Assets'.

In this chapter, we will study about the IoT problem universe and learn to design a business use case by building a blueprint for...

Connected assets & connected operations

With the swift progress of IoT in every dimension in the industry, the associated problems also diversified into the respective domains. To simplify problems, industry leaders took the most intuitive step by defining logical segregations in the IoT domain. Today, there is a plethora of articles and papers published over the Internet, which cite different names and classifications for IoT. As of now, we don't have any universally accepted classification for IoT, but we do see different names such as Consumer IoT, Industrial IoT, Healthcare IoT, and so on. All the IoT-related problems and solutions in the industrial domain were termed as Industrial IoT and so on.

Before studying Connected Assets and Connected Operations, let's explore a simplified classification for the IoT domain. This is definitely not the most exhaustive and widely recognized one, but it will definitely help us understand the nature of the problem better:

Connected assets & connected operations

When we look...

Defining the business use case

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...

Sensing the associated latent problems

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 heuristic driven hypotheses matrix (HDH)

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:

  • Heuristics-driven Hypotheses Matrix (HDH)
  • Data-driven Hypotheses Matrix (DDH)
  • The convergence of HDH and DDH

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...

Summary

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.

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Key benefits

  • Explore real-world use cases from the Internet of Things (IoT) domain using decision science with this easy-to-follow, practical book
  • Learn to make smarter decisions on top of your IoT solutions so that your IoT is smart in a real sense
  • This highly practical, example-rich guide fills the gap between your knowledge of data science and IoT

Description

With an increasing number of devices getting connected to the Internet, massive amounts of data are being generated that can be used for analysis. This book helps you to understand Internet of Things in depth and decision science, and solve business use cases. With IoT, the frequency and impact of the problem is huge. Addressing a problem with such a huge impact requires a very structured approach. The entire journey of addressing the problem by defining it, designing the solution, and executing it using decision science is articulated in this book through engaging and easy-to-understand business use cases. You will get a detailed understanding of IoT, decision science, and the art of solving a business problem in IoT through decision science. By the end of this book, you’ll have an understanding of the complex aspects of decision making in IoT and will be able to take that knowledge with you onto whatever project calls for it

Who is this book for?

If you have a basic programming experience with R and want to solve business use cases in IoT using decision science then this book is for you. Even if your're a non-technical manager anchoring IoT projects, you can skip the code and still benefit from the book.

What you will learn

  • Explore decision science with respect to IoT
  • Get to know the end to end analytics stack – Descriptive + Inquisitive + Predictive + Prescriptive
  • Solve problems in IoT connected assets and connected operations
  • Design and solve real-life IoT business use cases using cutting edge machine learning techniques
  • Synthesize and assimilate results to form the perfect story for a business
  • Master the art of problem solving when IoT meets decision science using a variety of statistical and machine learning techniques along with hands on tasks in R
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Table of Contents

9 Chapters
1. IoT and Decision Science Chevron down icon Chevron up icon
2. Studying the IoT Problem Universe and Designing a Use Case Chevron down icon Chevron up icon
3. The What and Why - Using Exploratory Decision Science for IoT Chevron down icon Chevron up icon
4. Experimenting Predictive Analytics for IoT Chevron down icon Chevron up icon
5. Enhancing Predictive Analytics with Machine Learning for IoT Chevron down icon Chevron up icon
6. Fast track Decision Science with IoT Chevron down icon Chevron up icon
7. Prescriptive Science and Decision Making Chevron down icon Chevron up icon
8. Disruptions in IoT Chevron down icon Chevron up icon
9. A Promising Future with IoT Chevron down icon Chevron up icon

Customer reviews

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Anish Shah Aug 24, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
A must for data science enthusiasts interested in application of data science concepts in the manufacturing industry i.e in Industrial Internet of Things. Excellent combination of IOT and data science to forsee predictive maintenance, predictive quality and predictive analytics...
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