Design rationale: Concepts, techniques, and use

Abstract The human-centered AI approach posits a future in which the work done by humans and machines will become ever more interactive and integrated. This article takes human-centered AI one step further. It argues that the integration of human and machine intelligence is achievable only if human organizations—not just individual human workers—are kept “in the loop.” We support this argument with evidence of two case studies in the area of predictive maintenance, by which we show how organizational practices are needed and shape the use of AI/ML. Specifically, organizational processes and outputs such as decision-making workflows, etc. directly influence how AI/ML affects the workplace, and they are crucial for answering our first and second research questions, which address the pre-conditions for keeping humans in the loop and for supporting continuous and reliable functioning of AI-based socio-technical processes. From the empirical cases, we extrapolate a concept of “keeping the organization in the loop” that integrates four different kinds of loops: AI use, AI customization, AI-supported original tasks, and taking contextual changes into account. The analysis culminates in a systematic framework of keeping the organization in the loop look based on interacting organizational practices. 

/pdf/keeping-the-organization-in-the-loop-a-socio-technical-314jlltg.pdf

Keeping the organization in the loop: a socio-technical extension of human-centered artificial intelligence

This paper illustrates how an effective maintenance policy could influence the productivity and profitability of a manufacturing process. It was possible to show how changes in the productivity affect profit, separately from the effects of changes in the uncontrollable factors, i.e. price recovery. The main results of the case study performed at a Swedish paper-mill showed that a paper-mill machine could, ideally, generate extra profit of at least 7.8 million Swedish kronor (SEK) (approximately US$ 0.975 million) per year, i.e. 12.5% of its yearly maintenance budget, if it avoids all unplanned stoppages and bad quality production due to maintenance-related causes. Thus, maintenance is not a cost centre, but a profit generating function.

The Role of Maintenance in Improving Companies' Productivity and Profitability

In this paper we review the literature on multi-component maintenance models with economic dependence. The emphasis is on papers that appeared after 1991, but there is an overlap with Section 2 of the most recent review paper by Cho and Parlar (1991). The classification scheme that we choose is primarily based on the various options of grouping maintenance activities: grouping either corrective or preventive maintenance actions, or combining preventive maintenance activities with corrective actions. As such, this classification links up with the possibilities for grouped maintenance activities that exist in practice.

A Review of Multi-Component Maintenance Models with Economic Dependence

The big jump was between the chromosome of bacteria and that of eukaryotes. Once the chromosome left the cytoplasm to become sequestered in a nucleus, basic differences arose, both in organization and behaviour, with unexpected consequences.

Maintenance of organization

Toward a framework for a maintenance architecture

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.

/pdf/structuring-and-contextualizing-historical-data-for-decision-wp4nt8qqph.pdf

Structuring and contextualizing historical data for decision making in early development

Current industry practices illustrate there is no standard method to estimate the number of hours worked on maintenance activities; instead, industry experts use experience to guess maintenance work hours. There is also a gap in the research literature on maintenance work hour estimation. This paper investigates the use of machine-learning algorithms to predict maintenance work hours and proposes a method that utilizes historical preventive maintenance order data to predict maintenance work hours.,The paper uses the design research methodology utilizing a case study to validate the proposed method.,The case study analysis confirms that the proposed method is applicable and has the potential to significantly improve work hour prediction accuracy, especially for medium- and long-term work orders. Moreover, the study finds that this method is more accurate and more efficient than conducting estimations based on experience.,The study has major implications for industrial applications. Maintenance-intensive industries such as oil and gas and chemical industries spend a huge portion of their operational expenditures (OPEX) on maintenance. This research will enable them to accurately predict work hour requirements that will help them to avoid unwanted downtime and costs and improve production planning and scheduling.,The proposed method provides new insights into maintenance theory and possesses a huge potential to improve the current maintenance planning practices in the industry.

/pdf/predicting-maintenance-work-hours-in-maintenance-planning-yrv395qb44.pdf

Predicting maintenance work hours in maintenance planning

Maintenance costs are becoming increasingly important to running a profitable business. The foundation of lower costs is an effective maintenance process. Often the maintenance process is similar to sequential engineering, which is often considered inefficient. This paper looks into how sequential inefficiency was solved through concurrent engineering and how to apply this to the maintenance process. Four initiatives are derived and applied in a case study. Implementing the initiatives is shown to give a potential 12% cost reduction indicating a need for a more concurrent maintenance process.

https://www.designsociety.org/download-publication/42523/Initiatives+towards+a+concurrent+maintenance+process

Initiatives towards a concurrent maintenance process

One of the main challenges that maintenance organizations are facing is grouping of different equipment to be maintained together. The challenge of grouping equipment to be maintained according to the same preventive maintenance plans suffers from lack of academic and empirical studies. This study, by getting inspired from the product family and product architecture theory, will try to investigate how this theory can be used to design and develop equipment groups to reduce the complexity in developing and managing the preventive maintenance plans.

/pdf/a-framework-for-grouping-of-equipment-for-preventive-3r3dyv8y8u.pdf

A Framework for Grouping of Equipment for Preventive Maintenance Planning

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