Jon Bokrantz

Identification of maintenance improvement potential using OEE assessment

Purpose – The purpose of this paper is to identify maintenance improvement potentials using an overall equipment effectiveness (OEE) assessment within the manufacturing industry. Design/methodology/approach – The paper assesses empirical OEE data gathered from 98 Swedish companies between 2006 and 2012. Further analysis using Monte-Carlo simulations were performed in order to study how each OEE component impacts the overall OEE. Findings – The paper quantifies the various equipment losses in OEE, as well as the factors availability, utilization, speed, quality, and planned stop time. From the empirical findings, operational efficiency losses are found to have the largest impact on OEE followed by availability losses. Based on the results, improvement potentials and future trends for maintenance are identified, including a systems view and an extended scope of maintenance. Originality/value – The paper provides detailed insights about the state of equipment effectiveness in terms of OEE in the manufacturing industry. Further, the results show how individual OEE components impact overall productivity and efficiency of the production system. This paper contributes with the identification of improvement potentials that are necessary for both practitioners and academics to understand the new direction in which maintenance needs to move. The authors argue for a service-oriented organization. Keywords Manufacturing, Overall equipment effectiveness, Maintenance,Production service and maintenance systems Paper type Research paper

Handling of Production Disturbances in the Manufacturing Industry

Purpose – A common understanding of what events to regard as production disturbances (PD) are essential for effective handling of PDs. Therefore, the purpose of this paper is to answer the two questions: how are individuals with production or maintenance management positions in industry classifying different PD factors? Which factors are being measured and registered as PDs in the companies monitoring systems? Design/methodology/approach – A longitudinal approach using a repeated cross-sectional survey design was adopted. Empirical data were collected from 80 companies in 2001 using a paper-based questionnaire, and from 71 companies in 2014 using a web-based questionnaire. Findings – A diverging view of 21 proposed PD factors is found between respondents in manufacturing industry, and there is also a lack of correspondence with existing literature. In particular, planned events are not classified and registered to the same extent as downtime losses. Moreover, the respondents are often prone to classify factors as PDs compared to what is actually registered. This diverging view has been consistent for over a decade, and hinders companies to develop systematic and effective strategies for handling of PDs. Originality/value – There has been no in-depth investigation, especially not from a longitudinal perspective, of the personal interpretation of PDs from people who play a central role in achieving high reliability of production systems.

Data Quality Problems in Discrete Event Simulation of Manufacturing Operations

High-quality input data are a necessity for successful discrete event simulation (DES) applications, and there are available methodologies for data collection in DES projects. However, in contrast to standalone projects, using DES as a daily manufacturing engineering tool requires high-quality production data to be constantly available. In fact, there has been a major shift in the application of DES in manufacturing from production system design to daily operations, accompanied by a stream of research on automation of input data management and interoperability between data sources and simulation models. Unfortunately, this research stream rests on the assumption that the collected data are already of high quality, and there is a lack of in-depth understanding of simulation data quality problems from a practitioners’ perspective. Therefore, a multiple-case study within the automotive industry was used to provide empirical descriptions of simulation data quality problems, data production processes, and relations between these processes and simulation data quality problems. These empirical descriptions are necessary to extend the present knowledge on data quality in DES in a practical real-world manufacturing context, which is a prerequisite for developing practical solutions for solving data quality problems such as limited accessibility, lack of data on minor stoppages, and data sources not being designed for simulation. Further, the empirical and theoretical knowledge gained throughout the study was used to propose a set of practical guidelines that can support manufacturing companies in improving data quality in DES.

A data-driven algorithm to predict throughput bottlenecks in a production system based on active periods of the machines

Abstract Smart manufacturing is reshaping the manufacturing industry by boosting the integration of information and communication technologies and manufacturing process. As a result, manufacturing companies generate large volumes of machine data which can be potentially used to make data-driven operational decisions using informative computerized algorithms. In the manufacturing domain, it is well-known that the productivity of a production line is constrained by throughput bottlenecks. The operational dynamics of the production system causes the bottlenecks to shift among the production resources between the production runs. Therefore, prediction of the throughput bottlenecks of future production runs allows the production and maintenance engineers to proactively plan for resources to effectively manage the bottlenecks and achieve higher throughput. This paper proposes an active period based data-driven algorithm to predict throughput bottlenecks in the production system for the future production run from the large sets of machine data. To facilitate the prediction, we employ an auto-regressive integrated moving average (ARIMA) method to predict the active periods of the machine. The novelty of the work is the integration of ARIMA methodology with the data-driven active period technique to develop a bottleneck prediction algorithm. The proposed prediction algorithm is tested on real-world production data from an automotive production line. The bottleneck prediction algorithm is evaluated by treating it as a binary classifier problem and adapted the appropriate evaluation metrics. Furthermore, an attempt is made to determine the amount of past data needed for better forecasting the active periods.

A methodology for continuous quality assurance of production data

High quality input data is a necessity for successful Discrete Event Simulation (DES) applications, and there are available methodologies for data collection in DES projects. However, in contrast to standalone projects, using DES as a day-to-day engineering tool requires high quality production data to be constantly available. Unfortunately, there are no detailed guidelines that describes how to achieve this. Therefore, this paper presents such a methodology, based on three concurrent engineering projects within the automotive industry. The methodology explains the necessary roles, responsibilities, meetings, and documents to achieve a continuous quality assurance of production data. It also specifies an approach to input data management for DES using the Generic Data Management Tool (GDM-Tool). The expected effects are increased availability of high quality production data and reduced lead time of input data management, especially valuable in manufacturing companies having advanced automated data collection methods and using DES on a daily basis.