An Algorithm for Designing Value Propositions in the IoT Space Addressing the Challenges of Selecting the Initial Class in Reference Class Forecasting
Abstract : Developing products and entering new markets in the Internet of Things (IoT) and industry 4.0 space are challenging because value propositions and business models are complex. IoT products are fundamentally platform products, whose embedded components can measure a disparate set of characteristics for several user types. Deciding in advance which combination of characteristics and users to focus on is daunting, and further complicated wherever the user is not the payer. It requires prediction. A common predictive technique, such as reference class forecasting (RCF), often suffers from the biased selection of initial reference classes. In this article, we propose combining RCF with our new biclustering algorithm, ECrfBimax, built from 200 use cases we distilled. ECrfBimax uniquely makes biclustering predictive. Our combined biclustering/RCF approach helps managers and designers design value propositions for IoT devices through a data-driven selection of initial classes. Applying the algorithm to readily available qualitative data from a high-dimensional dataset comprising 10 competitors of a real-life IoT company, 200 customers, and 230 product characteristics, we identify solution characteristics deployable by that company in different sectors. ECrfBimax identifies how far competitors are targeting users using similar/dissimilar combinations of characteristics. Quantitative proxies for size, success, and performance based on funding raised empirically support and statistically validate the algorithm. Uniquely too, our bicluster hierarchically groups customers with characteristics which either should or should not be jointly developed to target them. We also uniquely compare RCF and biclustering as methods for analyzing objects’ similarity.
? The remote diagnostic capabilities that IoT technology makes possible can maximize existing capacity and extend care to remote areas where there are few doctors or hospitals.
? While some of these telemedicine technologies already exist, adoption has remained limited because of high costs, systems that are not easy enough to use, and business models that inhibit innovation.
? To get the benefits of condition-based maintenance, existing regulations that mandate certain checks and routines may need to be revised.
? The Internet of Things heightens existing concerns about cybersecurity and introduces new risks.
? The age of equipment in the power sector and poor maintenance problems can lead to high level of energy losses and unreliability.
? The recent developments in digital technologies have provided a driving force to apply smart, IoT based solutions for the existing problems in a smart city context.
? The collaborative communication between different sectors, the smart grid can alert operators through smart appliances before any acute problem occurs.
? In terms of monitoring and maintaining assets of manufacturing, the big problem in factories is the depreciation of machines and mechanical devices.
• We exclude systems in which all of the sensors’ primary purpose is to receive intentional human input, such as smartphone apps where data input comes primarily through a touchscreen, or other networked computer software where the sensors consist of the standard keyboard and mouse.
• We define health applications here as those uses of IoT technology whose primary purpose is to improve health and wellness.
• In consumer applications, the rights to use data generated in the course of doing business and for what purpose should be clearly spelled out in the agreement that consumers must accept.
• IoT is an incredible source of data generated in the course of operations that can be used for other purposes.
? The use of sensor devices within IoT, in the energy sector largely improves diagnostics, decision-making, analytics, optimization processes and integrated performance metrics.
? In the energy consumption side, the temperature sensors are used to maximize the performance of a system when temperature changes during normal operations.
? This enables the operators to take actions to changes or deviations in the turbine operation conditions, leading to more consistent operations, optimized performance, and lower costs of energy.
? These sensors provide longevity and reliable position sensing performance in wind turbines.
? However, for deploying IoT in the energy domain, new solutions and trends are needed to improve the performance of IoT and overcome the associated challenges.
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