Economic Analysis of a Massively Populated Internet of Things System: An Agent-Based Simulation Approach

Abstract With 212 locations serving a geographical area three times the size of the state of Texas, our international client has been experiencing unexpected failures in his portfolio of thousands of refrigeration units, resulting in an associated loss of $1.4 million. These losses have been attributed to lapses in food quality caused by improper refrigeration, and the adopted reactive maintenance strategy of the refrigeration units. To assist our client in complying with the cold-chain regulations of food safety and mitigate such losses, the agent-based simulation was used to model and assess the viability of two distinct massively populated Internet of Things (IoT) alternatives. The first (alternative A) involves monitoring the availability of each of the refrigeration units and signaling for the initiation of repair processes and food removal from failed units when the cooling temperature differs from a specified threshold and a more sophisticated and expensive alternative B that involves the adoption of the added capability of a condition-based predictive maintenance strategy to reduce unplanned downtime and mitigate or eliminate the causes of failure. Using historical data, a simulation of the current operations was first modeled, validated, and then augmented with capabilities to address the operational characteristics of the proposed IoT implementations. Results of financial analysis results including the probabilistic risk analyses, that account for the variations and the probability distributions of the assumption parameters, showed alternative A to be superior, with a mean net present value (NPV) of $416,703, and a modified IRR of about 18.77% exceeding the project cost of capital of 12.80% with complete certainty. The client was also advised that a 30% reduction in the acquisition cost would make investment B as viable as investment A.