Abstract
Nowadays, manufacturers are shifting from a traditional product-centric business paradigm to a service-centric one by offering products that are accompanied by services, which is known as Product-Service Systems (PSSs). PSS customization entails configuring products with varying degrees of differentiation to meet the needs of various customers. This is combined with service customization, in which configured products are expanded by customers to include smart IoT devices (e.g., sensors) to improve product usage and facilitate the transition to smart connected products. The concept of PSS customization is gaining significant interest; however, there are still numerous challenges that must be addressed when designing and offering customized PSSs, such as choosing the optimum types of sensors to install on products and their adequate locations during the service customization process. In this paper, we propose a data warehouse-based recommender system that collects and analyzes large volumes of product usage data from similar products to the product that the customer needs to customize by adding IoT smart devices. The analysis of these data helps in identifying the most critical parts with the highest number of incidents and the causes of those incidents. As a result, sensor types are determined and recommended to the customer based on the causes of these incidents. The utility and applicability of the proposed RS have been demonstrated through its application in a case study that considers the rotary spindle units of a CNC milling machine.
Highlights
IntroductionManufacturers are attempting to fulfill orders on-demand by conducting business processes over short-term networks while considering customer requirements (e.g., functional, structural, environmental, and performance aspects of product design offerings), quality of manufactured products, sustainability (e.g., producing manufactured products by using economically sound processes that minimize wastes and reduce negative environmental impacts while saving energy and natural resources, designing products to achieve targeted objectives (e.g., cost, quality, reliability, etc.) using systematic approaches such as the Design for Excellence approach (aka Design for X) [1]), time (e.g., reducing lead times), price, and other dimensions [2]
Manufacturers are attempting to fulfill orders on-demand by conducting business processes over short-term networks while considering customer requirements, quality of manufactured products, sustainability (e.g., producing manufactured products by using economically sound processes that minimize wastes and reduce negative environmental impacts while saving energy and natural resources, designing products to achieve targeted objectives using systematic approaches such as the Design for Excellence approach [1]), time, price, and other dimensions [2]
Based on this analysis and the failure modes at hand, sensor types for monitoring these critical parts can be determined and recommended to the customer. The analysis of these data is not supported by Product-Service Systems (PSSs) to improve data-driven decision making. This creates a demand for the adoption of novel techniques/approaches for analyzing such data to assist customers in making informed decisions
Summary
Manufacturers are attempting to fulfill orders on-demand by conducting business processes over short-term networks while considering customer requirements (e.g., functional, structural, environmental, and performance aspects of product design offerings), quality of manufactured products, sustainability (e.g., producing manufactured products by using economically sound processes that minimize wastes and reduce negative environmental impacts while saving energy and natural resources, designing products to achieve targeted objectives (e.g., cost, quality, reliability, etc.) using systematic approaches such as the Design for Excellence approach (aka Design for X) [1]), time (e.g., reducing lead times), price, and other dimensions [2]. PSS customization entails configuring products with varying degrees of differentiation to meet the needs of various customers This is combined with service customization, in which customers expand configured products to include smart sensors or IoT communication devices in general to improve product usage and facilitate the transition to smart connected products. These sensors (e.g., temperature sensor, humidity sensor, vibration sensor, etc.) are embedded in products to regularly monitor physical parameters in machinery such as vibration, temperature, pressure, etc., to detect changes that may indicate a developing fault
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