Search results

Introduction: The internet of things (IoT) is the emerging technology of interconnected objects that can be termed as ‘things’ used to exchange data, connecting with different devices on the internet. It is the future where connected devices are controlled remotely. The insurance sector is one of the leading industries providing financial protection services to their customers to recover losses. Like others, the insurance industry uses the services very efficiently to solve their customer-centric problems and provide the best services to them. IoT in insurance is enhancing customer services.

Purpose: To determine how the insurance industry utilises the different IoT technologies to provide the best services and solutions to their users. The insurance sector is working on other areas of expertise to offer outstanding facilities to their clientele.

Methodology: We reviewed published material covering five years on IoT and insurance and customer services in the media, newspapers, journal publications, and the web. We determined how the insurance sector adapted the new terminology to contribute its best services to the users.

Findings: We observed that IoT services and technologies benefit the insurance industry and the clientele. This shows excellent results in the growth of the sector and heightened facilities for the consumers.

Food Industries have to cater a plethora of consumers having variety of tastes. For sustaining in such environment companies create their unique selling point and big data helps them to analyze market situation for such purpose. In this book chapter, the supply chain of fruits and vegetables and the post-harvest losses encountered at each stage in absence of data analytics is discussed. This can be an opportunity for the food industries to reduce food loss and gain better returns on investment by going for a digital transformation. Companies combine big data with technologies like machine learning and artificial intelligence to get faster and more personalized experiences. This chapter includes comparative case studies of food and retail sector for better understanding of the outcome.

Currently, Mauritius is adopting landfilling as the main waste management method, which makes the waste sector the second biggest emitter of greenhouse gas (GHG) in the country. This presents a challenge for the island to attain its commitments to reduce its GHG emissions to 30% by 2030 to cater for SDG 13 (Climate Action). Moreover, issues like eyesores caused by littering and overflowing of bins and low recycling rates due to low levels of waste segregation are adding to the obstacles for Mauritius to attain other SDGs like SDG 11 (Make Cities & Human Settlements Inclusive, Safe, Resilient & Sustainable) and SDG 12 (Guarantee Sustainable Consumption & Production Patterns). Therefore, together with an optimisation of waste collection, transportation and sorting processes, it is important to establish a solid waste characterisation to determine more sustainable waste management options for Mauritius to divert waste from the landfill. However, traditional waste characterisation is time consuming and costly. Thus, this chapter consists of looking at the feasibility of adopting machine learning to forecast the solid waste characteristics and to improve the solid waste management processes as per the concept of smart waste management for the island of Mauritius in line with reducing the current challenges being faced to attain SDGs 11, 12 and 13.