Enabling SystemOriented Service Delivery in Industrial Maintenance: A Metamethod for Predicting Industrial Costs of Downtime Nowadays, companies often outsource activities not directly related to their core business to service providers. Service providersxe2x80x94especially in physical servicesxe2x80x94heavily rely on complex information and optimization systems to increase their efficiency. As of now, most of those systems implement the concept of provideroriented service delivery (POSD). Whilst POSD optimizes from a provider perspective, it neglects optimization potential on the customer side. Addressing this issue, scholars recently proposed the concept of systemoriented service delivery (SOSD). Although SOSD allows for significant cost reduction compared to POSD, it also requires additional customer cost data. In industrial maintenance, this additional cost data refers to the customerxe2x80x99s costs of downtime. Despite some pioneer work in this field, current knowledge does not suffice for the successful application of SOSD in the domain of industrial maintenance. Consequently, the objective of this work is to develop a method to determine a manufacturerxe2x80x99s costs of downtime.

CostBased Text Understanding to Improve Maintenance Knowledge Intelligence in Manufacturing Enterprises Abstract   Improving maintenance knowledge intelligence using text data has not been largely explored in the literature of production and engineering management. The stateoftheart approaches and solutions mainly focus on either clustering and classification of maintenance logs, or extracting additional (meta)data e.g. failure time data from maintenance text reports, operatorsxe2x80x99 workbooks and digital logbook. Knowledge Discovery from Text (KDT) enables finding undetected causalities, hidden patterns, frequencies, associative relations, and sentiments in maintenance text repositories. Applying KDT may enhance understanding the content of text data syntactically and semantically. However, advanced KDT approaches do not significantly provide meaningful and explainable outcomes, due to certain barriers in manufacturing enterprises, namely availability and quality of (longitudinal) maintenance text data.  To overcome these barriers in real world industrial maintenance, generate added value in industrial maintenance, and lay the ground for autonomous maintenance decisionsupport in the context of Industry 4.0, the first step is to adopt KDT methods and accordingly provide maintenancespecific solutions considering practical challenges and possibilities.  This paper discusses the lack of understanding maintenance text data and examines its effect on maintenance knowledge intelligence in manufacturing enterprises. A compositional framework for text understanding (TextPlan) is introduced. TextPlan explores quantification of text data in both syntax and semantic levels, i.e. how to vectorize an annotated maintenance report into numeric values, which represent cost data, hidden associations and sentiments. A prominent feature of TextPlan is costbased text analysis, which decomposes a maintenance text report into separate cost items, and then (re)composes the findings to estimate the total maintenance cost associated with the given report. Finally yet importantly, TextPlan consolidates the findings into a Text Understanding Map for assisting maintenance planner, based on three proposed measures of text comprehension, namely Association Measuring Index (AMI), Opinion Index (OI) and Cost Vector (CV).