Leveraging Internet of Things (IoT) in solving complex business problems

The increasing complexity in business

Businesses around the globe have become more and more complex over the years. The complexity arises due to multiple touch-points at which various people, IT and enterprise systems interact with each other. With ever expanding breadth of supply chains, more and more companies are becoming tightly interdependent across the value chain. It is the unpredictable interaction of these independent systems that creates complex challenges in businesses. These complex business challenges have multiple layers, multiple perspectives and multiple constraints that cannot be solved by traditional means. The lowest common denominator for these complex problems are the information that is being collected, communicated and transferred for various people and systems to act upon. And it is because of the challenges in managing this information; these complex problems can neither be expressed with certainty nor be addressed effectively by the traditional means.

Tackling the ‘wicked’ problems

A problem whose solution requires a great number of people to change their mindsets and behavior is likely to be a wicked problem. Wicked problems crop up whenever the organization faces constant change or unprecedented challenges. Business problems are not just systemic problems; they are also affected by the mindsets, beliefs, attitudes and behaviors of the people involved. If we recall the Nash equilibrium, the most optimum solution may not be achieved if all the parties get their best alternative. The optimum solutions are obtained only when all the stakeholders agree to compromise for the next best alternative. Hence these problems have multiple layers to it that encompasses processes, systems, behaviors, attitudes and mindsets. For such wicked problems, the general thrust of the problem may be clear, however considerable time and effort needs to be spent in order to map these problems clearly and decouple problems from systems and people involvesd. A large part of the problem solving activity, then, consists of problem definition and problem shaping. So how do you really define your problems?

 Business problems as information problems

Business systems and processes rely on the information that is being generated by business operations. Various people in the organization consume, transform and create information that goes into the data systems. Hence, the information becomes base for functioning of the entire business enterprise ecosystem. If we assume that all the actors in the ecosystem are perfectly aligning their actions with their information, we can express the anomalies in the business as a function of information. All systems, processes and people can be represented in the information domain by mapping how they work with information. Transforming business problems into information domain allows us to express them clearly and develop effective solutions.

The crucial role of IoT

IoT based solutions provide unique strengths and advantages when it comes to solving complex information problems. Firstly, IoT is really an information framework that is well equipped to extract information from multiple sources and streamlining it across enterprise. Any information that is not available can be acquired using plug and play data acquisition systems. Secondly, the architecture itself is so modular and scalable that it doesn’t need extensive setups. A few IP-enabled sensors at critical points and an IP internet gateway can get you started with your solution. Thirdly, IoT solutions are highly configurable that allows you to freely experiment with different designs and configurations. Fourthly, since data processing happens at the highest level, IoT solutions can theoretically be linked with any other IT system in an enterprise. Hence, IoT based solutions can enable development of multi-tier solutions for complex information challenges in the business context.

 Solution Approach

In order to solve complex information problems, we first need to transform and map it into simpler connected problem components that can be addressed by different layers of IoT architecture. This approach is very similar to the Fourier transform where a complex signal is first distilled into its base frequencies in the frequency domain. Later, a linear multistage solution is developed to address these individual frequencies as desired. Some frequencies are amplified, some are attenuated and others are altogether eliminated. This solution is then transformed back to the original domain and implemented with physical components. A similar approach can be applied to business problems by transforming in the information domain and solving them using IoT architecture.

Step 1: Exploring perspectives

First step in the solution approach is to define frames of reference. Business problems involve multiple stakeholders operating under their unique desires and motivations and the solution must consider existing systems and behaviors. Brilliant solutions fail to generate results because of human related issues. A solution can have a higher success rate if it fits into current systems and behavior patterns and if steps are taken to systematically transform these systems and behaviors. Hence, it is imperative that solution design process and solutions are people and systems centric.

Step 2: Problem transformation

The next step is to transform the problem from complex monolith to a set of layered interconnected simple problems that can be addressed effectively. People and systems in an enterprise work with information that is available to them. If we consider these people and systems as information processing nodes that collect process and transfer information, we can create an information map representing these systems and people by different types of nodes. Typical information transformations consist of information generation and collection, information sharing between nodes and logical flow of information in business processes. This representation exposes information gaps in the business problems that can be used for creating an solution blueprint using IoT information framework.

Step 3: Solution blueprinting

The solution blueprint consists of gap analysis on the IoT framework where different solution elements are recommended for different aspects of the problem. The M2M and cloud layers of IoT framework aggregate data from different sources and cluster them logically to solve the information acquisition aspect. The connectivity and mobility layers of IoT can define the message integrity and format for point-to-point communication problems. Finally, the ERP and applications layer of the IoT Solution can address the flow problems by maintaining correct routing of information and sequence of events. The final blueprint will recommend new IoT systems that will be required for the solution.

Step 4: Solving the problem

The IoT solution blueprint designed in the earlier step needs to be translated and integrated into the original environment by defining new roles, processes and systems. The solution will define the new roles for all relevant stakeholders and develop a change management plan to transform current behaviors. Similarly, based on the new data clusters, workflows and roles, solution will define the new systems and processes that will be required to solve the complex problem.

Conclusion

Business problems can be represented as information problems by creating information maps using various systems and people as information processing nodes. These information maps will highlight the information gaps through IoT solution framework. The solution blueprint will recommend methods and tools to streamline information asymmetry. This problem solving approach focuses on customers’ business problems rather than the technology itself. IoT could be a great tool in a business consultant’s toolbox. And this approach utilizes the strengths of IoT in solving real business problems where the value of IoT can be appreciated.

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