Data-Driven Decision Making in Maintenance Service Delivery Process: A Case Study

Abstract

Data availability is changing the way companies make decisions at various levels (e.g., strategical and operational). Researchers and practitioners are exploring how product–service system (PSS) providers can benefit from data availability and usage, especially when it comes to making decisions related to service delivery. One of the services that are expected to benefit most from data availability is maintenance. Through the analysis of the asset health status, service providers can make informed and timely decisions to prevent failures. Despite this, the offering of data-based maintenance service is not trivial, and requires providers to structure themselves to collect, analyze and use historical and real-time data properly (e.g., introducing suitable information flows, methods and competencies). The paper aims to investigate how a manufacturing company can re-engineer its maintenance service delivery process in a data-driven fashion. Thus, the paper presents a case study where, based on the Dual-perspective, Data-based, Decision-making process for Maintenance service delivery (D3M), an Italian manufacturing company reengineered its maintenance service delivery process in a data-driven fashion. The case study highlights the benefits and barriers coming with this transformation and aims at helping manufacturing companies in understanding how to address it.

Keywords

• D3M framework
• maintenance
• product-service systems
• industry 4.0
• data-driven decision-making
• case study