A fuzzy-TOPSIS model for maintenance outsourcing considering the quality of submitted tender documents

The paper provides a multiple-experts Fuzzy-TOPSIS decision-making model for the selection among maintenance contractors based on the quality of tendering documents. The study introduces a set of selection criteria utilizing benefit and cost criteria from literature. The proposed model aggregates subjective linguistic assessments of multiple experts that express their opinions on the degree of importance of criteria and allows multiple decisionmakers to evaluate the compliance of contractors’ documents. For a case study, the model is applied to select among contractors tendering to maintain the heavy-duty cranes of an international steel company from literature. Several decision-making scenarios are investigated, and major changes in the final decision are observed. The changes in obtained results illustrate the need to better address uncertainties in rating and tendering an overqualified contractor at a higher cost.

Offshore Software Maintenance Outsourcing: Predicting Client’s Proposal using Supervised Learning

In software engineering, software maintenance is the process of correction, updating, and improvement of software products after handed over to the customer. Through offshore software maintenance outsourcing (OSMO) clients can get advantages like reduce cost, save time, and improve quality. In most cases, the OSMO vendor generates considerable revenue. However, the selection of an appropriate proposal among multiple clients is one of the critical problems for OSMO vendors. The purpose of this paper is to suggest an effective machine learning technique that can be used by OSMO vendors to assess or predict the OSMO client’s proposal. The dataset is generated through a survey of OSMO vendors working in a developing country. The results showed that supervised learning-based classifiers like Naïve Bayesian, SMO, Logistics apprehended 69.75 %, 81.81 %, and 87.27 % testing accuracy respectively. This study concludes that supervised learning is the most suitable technique to predict the OSMO client's proposal.

Optimal Contract for Machine Repair and Maintenance

Maintenance outsourcing is quite common in industries that rely on complex and critical equipment. Instead of investing in the maintenance facilities, firms outsource maintenance activities to specialized companies. However, it may be hard for firms (i.e., principal) to observe whether maintenance companies (i.e., agent) put sufficient resources into providing the best service, which gives rise to agency issues. In a dynamic environment in which an agent is responsible for both maintenance and repair of a critical machine, how the principal uses payments and termination to tackle agency issues is a challenging problem. In “Optimal Contract for Machine Repair and Maintenance,” F. Tian, P. Sun, and I. Duenyas provide theoretical guidance on designing the optimal contract to induce efforts from an agent to efficiently operate a machine. Although they consider the very general contract forms, the optimal contracts demonstrate simple and intuitive structures, making them easy to describe and implement in practice.

Using Mahalanobis distance and decision tree to analyze abnormal patterns of behavior in a maintenance outsourcing process-a case study

PurposeThe purpose of this paper is to analyze abnormal behavior patterns in a maintenance outsourcing process. Based on the results, the managers can focus on the abnormal behavior and the direction of the investigation can be narrowed. The abnormal behavior can be identified more easily.Design/methodology/approachMaholanobis Distance (MD) and Decision Tree (DT) are integrated to analyze for abnormal behavior patterns. To prevent abnormal behaviors, a maintenance outsourcing case must be passed by several managers in different departments. In this research, some criteria for pairs of managers are calculated first. Based on the criteria, the MDs of these pairs can be calculated. Pairs are categorized by their MDs. Any pair whose MD is higher than a threshold is labeled “abnormal” while the remaining are labeled “normal”. After oversampling the minority class of abnormal, a DT is built by Classification and Regression Trees (CART) based on the labeled dataset. Finally, the combination of criteria for abnormal categories is extracted from the tree.FindingsThrough the results from the DT, the combinations of criteria provide obvious characteristics of cases that are categorized as abnormal, and then provide a direction for investigators. Thus, the range of investigation can be narrowed. The empirical results show that the result of the proposed integrated methodology is helpful for abnormal behavior pattern analysis.Practical implicationsThis research is intended to help an organization to enhance their investigation in a large number of maintenance outsourcing cases. About 8,000 cases are collected for analysis.Originality/valueThe integration of MD and DT for analyzing abnormal behavior patterns in a maintenance outsourcing process is not found in the literature. Moreover, the empirical results show that the proposed integrated methodology is helpful in a real application.