Now that we have a concrete understanding of how we can define the problem, lets try to understand what it takes to solve a problem. There could be a problem that could possibly be in any stage of its life, say fuzzy, the impact that it could create could be high with a moderately high frequency, and the nature of the problem could be predictive. Such a problem is really complicated if we try to understand it from its initial vibes. To make the example sound more concrete, lets assume that a renewable energy (solar) provider has one of its plants set up in a completely off-grid location to supply electric energy to a large college campus for its daily operations. The problem to solve would be predicting the amount of solar energy that would be generated based on weather and historic operational parameters. As the operations are completely off-grid, the admin of the campus would be keen to know the amount of energy that would be generated in the coming days so as to take necessary precautionary measures in cases of low production and high consumption. This would be a classic case of a predictive problem with a high impact and moderately high frequency and still in the fuzzy state. We know a few things about how to go about but no clear roadmap has been identified.
How do we solve this problem? What does it take in terms of skillsets or disciplines to get started with solving the problem? Decision Science, on a high level, takes multiple disciplines together to solve the problem. It generally takes the combination of math, business, and technology to design and execute the initial version of the solution, and then design thinking, behavioral science, and other disciplines to improvise the solution. Lets understand how and why this is required.
Solving the problem of predicting solar energy generation will initially require math skills where we would apply a variety of statistical and machine learning algorithms to get the predictions more and more accurate. Similarly, we would need technology skills to program in one or more computer languages based on the infrastructure where the data would be stored. The technology skills will help us extract data from various internal and external sources and clean, transform, and massage the data to render in the format where we can perform analysis. Finally, we will require business skills where we would have an understanding on how the college operates during the day, which operations are the most energy-consuming, how does the forecasted result add value for the college operations, and how do they plan to take precautionary actions for survival. The business skills required will make more sense if we would try to imagine a classic retail industry problem where we are trying to forecast the sales at a store level. We would need to take into account a variety of features and dimensions that are crucial from the business perspective but may be statistically insignificant. For example, the customer value bucket (high / medium / low) may appear insignificant mathematically during the analysis, but it would probably be one of the most crucial variables for business that may persuade us to consider the problem rather than ignore it.
Additionally, to get more and more granular in the problem solving phase, we would need skillsets on engineering and other disciplines. In our example, where we try to predict energy that would be generated in the future, a sound background of physics and engineering that would aid us in understanding the functioning of the photovoltaic cells and solar panel architecture and its engineering will be of great value when improving the solution becomes the core objective.
Similarly, in some other use cases, we would need disciplines of behavioral science and design thinking in more depth to study user behavior in specific scenarios and its implications in the business context. Thus, to solve any problem, we would need a curious mindset where our approach would be very interdisciplinary. With the use cases in IoT, the level of granularity of data that gets captured using sensors is altogether different. This mammoth and rich dataset now brings us the opportunity to deal with use cases at more and more granular levels than before. We can talk about use cases as abstract as increasing the product/asset life for an oil and gas refinery equipment or something as granular as reducing the vibrations in the gears of a diesel engine.
Now that we have a fair understanding about what skillsets are required to solve the business problem, lets try to understand how we go about solving the problem. Generally, the initial vibes that we get from a problem is the complexity. Not every problem is complicated; the simplicity of the problem is represented when it is broken down into smaller problems and we study how these smaller problems are connected to each other. Solution design gets easier when we think about one small problem at a time than the entire big problem.
Lets say that we are trying to solve the problem of increasing sales for a retailing customer. Here, increasing sales is the bigger problem that can be broken down into smaller and more focused problems where we deal with one small problem at a time. Increasing sales for a customer can be composed of smaller problems such as improving marketing campaigns, optimizing marketing channels, improving customer experience, designing customer retention programs, optimizing the supply chain model, and so on. The bigger problem can always be broken down into smaller and more focused problems. Similarly, when we solve one problem, it is also important to understand how these problems connect with other problems in the universe. The solution of the current problem may have a direct impact on another problem or solving this problem also requires solving the other connected problem. Here, we are talking about the art of problem solving rather than solving specific problems. Every problem is a part of a universe, where it may be connected to one or many other problems and may have a direct or indirect impact with other problems. Understanding the network of the problem is crucial before finalizing the design for our solution to the problem.
When we map the smaller problems connecting with each other to create the bigger problem, we have a universe of problems where each small problem can be identified with its life stage, nature, and type. Then we can solve each of these problems using a different approach meticulously drafted for its type and nature rather than using one generic approach. An incremental step-by-step approach to problem solving is not only time-saving but also impactful. The following diagram showcases the example discussed here visually. We can see how large problems are essentially smaller problems interconnected to each other:
The problem universe
