Structuring and Contextualizing Historical Data for Decision Making in Early Development

Abstract

It is important to be able to compare and evaluate different solutions early in development. This paper proposes a method for structuring historical data into a data model that can support the evaluation of new design concepts. The data is contextualized by linking it to a hierarchical decomposition of existing products. Two case studies were conducted to evaluate the value of using historical data when evaluating new concepts. The cases confirm that the proposed method is useful for evaluation of new concepts.

Modularizing Maintenance for Improved Production Impact Clarification

“…The free text format can be difficult to compare in large quantities, rendering the data difficult to use (Agergaard et al, 2021). The process dimension pertains to aspects such as maintenance management processes (Ben-Daya et al, 2009;Deighton, 2016;Sigsgaard et al, 2020), decision making (Ruschel et al, 2017), information governance (Chilamkurti et al, 2014;Hodkiewicz and Ho, 2016), and human resources (Dansk Standard, 2008;Gulati, 2012). The maintenance management process consists of the steps taken to perform the maintenance (Deighton, 2016;Sigsgaard et al, 2020).…”

“…The process dimension pertains to aspects such as maintenance management processes (Ben-Daya et al, 2009;Deighton, 2016;Sigsgaard et al, 2020), decision making (Ruschel et al, 2017), information governance (Chilamkurti et al, 2014;Hodkiewicz and Ho, 2016), and human resources (Dansk Standard, 2008;Gulati, 2012). The maintenance management process consists of the steps taken to perform the maintenance (Deighton, 2016;Sigsgaard et al, 2020). The definition of this process varies, but the definition from a combination of multiple sources by (Sigsgaard et al, 2020) contains the steps identify, plan, schedule, execute, close-out.…”

“…The maintenance management process consists of the steps taken to perform the maintenance (Deighton, 2016;Sigsgaard et al, 2020). The definition of this process varies, but the definition from a combination of multiple sources by (Sigsgaard et al, 2020) contains the steps identify, plan, schedule, execute, close-out. The identification step focuses on defining the need and urgency of the maintenance at a location; the planning step is when the actions, resources, and time requirements of the maintenance are set; scheduling is placing the defined actions in time; execution is the actual performance of the maintenance; close-out is the documentation, quality control, and financial settling of the job.…”

“…The identification step focuses on defining the need and urgency of the maintenance at a location; the planning step is when the actions, resources, and time requirements of the maintenance are set; scheduling is placing the defined actions in time; execution is the actual performance of the maintenance; close-out is the documentation, quality control, and financial settling of the job. These steps are being performed in parallel for all the maintenance jobs in the pipeline at any given time (Sigsgaard et al, 2020). Data documenting the process dimension can be qualitative, such as work flow maps or semi-structured interviews (Sigsgaard et al, 2020), or by following maintenance job statuses in the CMMS measuring aspects such as change over time, maintenance response time, or amount of job rework (Kumar and Parida, 2008).…”

Toward a framework for a maintenance architecture

“…The equipment portfolio was visualized in each dimension, and a model was built to estimate the cost and performance for the different combinations. In their recent study, Sigsgaard et al (2020) explained the data model in detail.…”