Deciding a Multicriteria Decision-Making (MCDM) Method to Prioritize Maintenance Work Orders of Hydroelectric Power Plants

The current global competitive scenario and the increase in complexity and automation of equipment and systems demand better results from maintenance management in organizations. As maintenance resources are limited, prioritizing maintenance activities is essential to allocate them properly and to meet maintenance management objectives. In the face of these challenges, multicriteria decision-making (MCDM) methods are commonly used in organizations to support decision-making. Nevertheless, selecting a suitable MCDM method for maintenance planning can be complicated given the diversity of methods and their strengths and weaknesses. In this context, this paper proposes a novel knowledge-based method for deciding a multicriteria decision-making (MCDM) method to prioritize maintenance work orders of hydroelectric plants. As the main novel contribution, it translates the intrinsic characteristics of the main MCDM methods into questions related to maintenance planning to guide the recommendation of a suitable MCDM method for organizations through a decision tree diagram. This approach was applied to a maintenance case study of a hydroelectric power plant in order to demonstrate its use and contribute to its understanding. These findings contribute to maintenance management in selecting an MCDM method aligned with the context of its maintenance planning for the prioritization of maintenance work orders.

Evaluation and optimization of CES performances: application of Pareto principle to KPIs

In recent times the approach to health care has been mostly influenced by the growing number of biomedical equipment used in hospitals, which needs the presence of the Clinical Engineering Service (CES). The aim of this work is to suggest a methodology to improve the performance of a CES through the application of Pareto principle to main Key Performance Indicators (KPIs). The methodology is applied by focusing on the use of KPIs that represent a quantifiable measure of achieving goals set by an organization. In this study five KPIs are considered: Uptime, MTTR (mean time to repair), PPM (percentage preventive maintenance), MTBF (mean time between failures) and the COSR (cost of service ratio). The first three indicators express the measure of CES efficiency in ensuring regular maintenance. The first step consists in retrieving data related to work orders for the years 2015-2016 on 6000 installed devices, carried out by a management software. The second step is to get the results through the use of an environment for numerical calculation and statistical analysis. In order to identify the main critical issues that may be present, three indicators (Uptime, MTTR and MTBF) are analyzed by applying the Pareto principle (i.e. 20% of the causes produce 80% of the effects). Considering the totality of work orders, therefore, it is possible to concentrate on only 20% of them in order to focus on a small group to understand the correlations between them. Identifying these characteristics means identifying the main critical issues that are present, on which action must be taken, and which affect 80% of the overall behavior. The COSR and PPM indicators, instead, suggest distribution models that allow to focus attention on the most critical devices. In conclusion, the way to analyze the results is obtained, when possible, by applying Pareto principle. Therefore, a CES will be able to focus on a few causes of poor performance. The achievement of these results could allow the standardization of the method used, enabling it to be applied to any healthcare system.

Material Requirements Planning Using Variable-Sized Bin-Packing Problem Formulation with Due Date and Grouping Constraints

Correct planning is crucial for efficient production and best quality of products. The planning processes are commonly supported with computer solutions; however manual interactions are commonly needed, as sometimes the problems do not fit the general-purpose planning systems. The manual planning approach is time consuming and prone to errors. Solutions to automatize structured problems are needed. In this paper, we deal with material requirements planning for a specific problem, where a group of work orders for one product must be produced from the same batch of material. The presented problem is motivated by the steel-processing industry, where raw materials defined in a purchase order must be cut in order to satisfy the needs of the planned work order while also minimizing waste (leftover) and tardiness, if applicable. The specific requirements of the problem (i.e., restrictions of which work orders can be produced from a particular group of raw materials) does not fit the regular planning system used by the production company, therefore a case-specific solution was developed that can be generalized also to other similar cases. To solve this problem, we propose using the generalized bin-packing problem formulation which is described as an integer programming problem. An extension of the bin-packing problem formulation was developed based on: (i) variable bin sizes, (ii) consideration of time constraints and (iii) grouping of items/bins. The method presented in the article can be applied for small- to medium-sized problems as first verified by several examples of increasing complexity and later by an industrial case study.

A Predictive Analytics Tool to Provide Visibility Into Completion of Work Orders in Supply Chain Systems

In current supply chain operations, original equipment manufacturers (OEMs) procure parts from hundreds of globally distributed suppliers, which are often small- and medium-scale enterprises (SMEs). The SMEs also obtain parts from many other dispersed suppliers, some of whom act as sole sources of critical parts, leading to the creation of complex supply chain networks. These characteristics necessitate having a high degree of visibility into the flow of parts through the networks to facilitate decision making for OEMs and SMEs, alike. However, such visibility is typically restricted in real-world operations due to limited information exchange among the buyers and suppliers. Therefore, we need an alternate mechanism to acquire this kind of visibility, particularly for critical prediction problems, such as purchase orders deliveries and sales orders fulfillments, together referred as work orders completion times. In this paper, we present one such surrogate mechanism in the form of supervised learning, where ensembles of decision trees are trained on historical transactional data. Furthermore, since many of the predictors are categorical variables, we apply a dimension reduction method to identify the most influential category levels. Results on real-world supply chain data show effective performance with substantially lower prediction errors than the original completion time estimates. In addition, we develop a web-based visibility tool to facilitate the real-time use of the prediction models. We also conduct a structured usability test to customize the tool interface. The testing results provide multiple helpful suggestions on enhancing the ease-of-use of the tool.

Estimasi Jumlah Work Order Project Konstruksi Menggunakan Metode ARIMA (Autoregressive Integrated Moving Average)

A project consists of various types or work. Between one type of work with another type of work has a very close relationship. The types of work will show the scale or failure of a project. The more types of work to be done, the greater the scale of the project, and vice versa. The relationship between the type of work one with the other type of work on a large-scale project will be very complex, the smaller the scale, the relationship between types of work will be more simple. One of the construction companies in Gresik, namely CV. ANEKA JASA TEKNIK, applies the make to order strategy to respond to requests from consumers. That way, the company can send orders with quality and delivery time in accordance with the wishes of consumers. In this project the problem faced is how to forecast project work orders in the coming month. The data used in this study is work order data from January 2016 to April 2019. Data analysis uses the Autoregressive Integrated Moving Average (ARIMA) method. The tools used in this study are Minitab. The analysis obtained from calculations using the ARIMA model (1,1,1) and forecasting results until the month of October. The analysis results obtained from calculations using the ARIMA model (1,1,1) and forecasting results until October. Work order construction project is Zt = μ - 0,9647Zt-1 + at, Forecasting work order construction projects for the coming month, starting from January 2016 to April 2019 experiencing a gradual decline, comparison between work order forecast project construction not much different from the work order of the actual construction project.Output in the form of data prediction the number of work orders is displayed in the form of tables and graphs so that it is easy to understand.