Overall throughput effectiveness (OTE) metric for factory-level performance monitoring and bottleneck detection

Seiichi Nakajima provided overall equipment effectiveness (OEE) to measure productivity and perform diagnostics at the equipment level. However, a literature review indicates that such metrics are lacking at the factory level. In order to address this gap, an overall throughput effectiveness (OTE) metric is developed. The purpose of OTE is twofold: it measures factory-level performance and can also be used for performing factory-level diagnostics such as bottleneck detection and identifying hidden capacity. The task of coming up with such a metric was achieved by defining a set of commonly occurring predefined subsystems including series, parallel, assembly and expansion. OTE was developed for each of these predefined subsystems. It also accounts for subsystems processing multiple products and performing rework. Any factory layout can be modelled using a combination of the predefined subsystems, which allows determination of the overall factory effectiveness (OFE). More importantly, OTE has the potential to automate the entire factory-level performance diagnostics, hence drive continuous productivity improvement quantitatively. This paper explains the OTE development methodology, validates the developed OTE metrics and demonstrates its diagnostic ability. Application of OTE to a wafer fab and glass manufacturing case study showed that productivity bottleneck and opportunities for improvement can be identified quantitatively.     

A Hierarchical structure of key performance indicators for operation management and continuous improvement in production systems

Key performance indicators (KPIs) are critical for manufacturing operation management and continuous improvement (CI). In modern manufacturing systems, KPIs are defined as a set of metrics to reflect operation performance, such as efficiency, throughput, availability, from productivity, quality and maintenance perspectives. Through continuous monitoring and measurement of KPIs, meaningful quantification and identification of different aspects of operation activities can be obtained, which enable and direct CI efforts. A set of 34 KPIs has been introduced in ISO 22400. However, the KPIs in a manufacturing system are not independent, and they may have intrinsic mutual relationships. The goal of this paper is to introduce a multi-level structure for identification and analysis of KPIs and their intrinsic relationships in production systems. Specifically, through such a hierarchical structure, we define and layer KPIs into levels of basic KPIs, comprehensive KPIs and their supporting metrics, and use it to investigate the relationships and dependencies between KPIs. Such a study can provide a useful tool for manufacturing engineers and managers to measure and utilize KPIs for CI.     

Developing performance measurement system for Internet of Things and smart factory environment

To cope with large fluctuations in the demand of a commodity, it is necessary for the manufacturing system to have rapid reactive ability. This requirement may be secured by performance measurement. Although manufacturing companies have used information systems to manage performance, there has been the difficulty of capturing real-time data to depict real situations. The recent development and application of the Internet of Things (IoT) has enabled the resolution of this problem. In demonstration of the functionality of IoT, we developed an IoT-based performance model consistent with the ISA-95 and ISO-22400 standards, which define manufacturing processes and performance indicator formulas. The development comprised three steps: (1) Selection of the Key Performance Indicators of the Overall Equipment Effectiveness (OEE), and the development of an IoT-based production performance model, (2) Implementation of the IoT-based architecture and performance measurement process using Business Process Modelling and (3) Validation of the proposed model through virtual factory simulation. We investigated the effect of the IoT-workability on the OEE, based on the final results of the simulation, both for the planned and actual productions. The simulation results showed that the proposed model represented the timestamp data acquired by IoT and captured the entire production process, thus enabling the determination of real-time performance indicators.     

Multiple input productivity measurement of production systems

The manufacturing manager is faced with the responsibility to effectively manage the productivity improvement of his production system. Critical to this challenge is the ability to measure the productivity of the various controllable resources. A multiple input productivity measurement model using data available within the manufacturing data base is presented which will perform absolute and trend analysis for the individual input factors along with the aggregate of the total system.     

Analysis and improvement of multi-product assembly systems: an application study at a furniture manufacturing plant

In this paper, we introduce an application study of modelling, analysis and continuous improvement of an assembly system at a furniture manufacturing plant using production systems engineering methods. Analytical models have been developed to characterise the assembly system making multiple products, and recursive procedures have been derived to evaluate line production rate. Lot size analysis is carried out, and bottleneck analysis methods are introduced to identify the bottlenecks, whose improvement can lead to the largest improvement in system performance. Such methods provide a quantitative tool for plant engineers and managers to operate and improve assembly line with high productivity, and are also applicable to other large-volume manufacturing systems.     

Automatic translation of plant data into management performance metrics: a case for real-time and predictive production control

A scalable and repeatable solution for linking shop-floor control system to a discrete event simulation (DES) model is presented. The key objective is to automatically translate the real-time data from the control system (e.g. supervisory control and data acquisition, SCADA) into KPI transfer functions of the production process. Such a seamless translation allows for the integration of engineering data emitted at plant level to higher level information system for decision-making. The solution provides a platform for researchers and practitioners to utilise the capabilities of real-time DAQ and control with that of discrete event simulation to accurately measure the key manufacturing systems performance metrics. In addition to the real-time capabilities, the predictive capabilities of the solution provide the managers to look ahead and to conduct What-if scenarios. Such capability enables line management to optimise performance and predict destabilising factors in the system ahead of time. A fully operational version of the designed solution has been deployed in a brewery’s live production system for the first time. The brewhouse production line model measures the utilisation of resources, Overall Equipment Effectiveness, and Overall Line Effectiveness in real-time and fast-forward mode simulation. The results of the predictive models (What-if-Scenarios) have been validated and verified by statistical means and direct observations. The accuracy of the estimated parameters is highly satisfactory.     

A framework for System Excellence assessment of production systems,based on lean thinking,business excellence,and factory physics

This article proposes a production system framework that synthesises lean production, business excellence, and factory physics. The framework, which draws on a deep state-of-the-art understanding, consists of a performance measurement system supporting the achievement of a target condition based on variability and lead time reduction, as well as approaches of continuous improvement. Based on four types of excellence, a System Excellence value is calculated, indicating the distance from a target condition and thus displaying relevant improvement potential. As a key result, the framework proposed provides a contribution to knowledge, as it combines the aforementioned schools of thought, resulting in a holistic framework for action. The measurement system offers a high level of robustness, as it draws on diverse data sources and reflects on the dynamic behaviour over time. It has been successfully implemented in automotive manufacturing plants worldwide, which may suggest considerable practical relevance. Another key result of this research is that through applying the framework, important bottom-line indicators, such as lead time, failure costs, or productivity, could be improved. As the plants are typical automotive industry high-volume plants, it is proposed that the solutions presented offer a suitable standard for this industry and type of plant.     

Quality factor analysis of metalworking process with AHP

Many manufacturing processes have various factors that affect the quality of the products, and the analysis and optimisation of these factors are critical activities for engineers. Although much research has been done on statistical methods to investigate the effects of these factors on quality metrics, these statistical methods are not always applied in real-world situations because of problems involving data integrity, lack of control/measurement, or technical/administrative constraints. On the other hand, conventional heuristic methods for the selection of critical quality factors are mostly devoid of metrics that can be examined objectively. This study, therefore, implements the analytic hierarchy process (AHP) for the quantitative prioritisation of the control factors involved in a flat end milling manufacturing process. In order to validate the metrics synthesised from the experience of skilled workers, the decision making is followed by a multivariate analysis of the variance based on the general linear model (GLM). The results show that AHP is able to provide fairly reliable metrics about the contribution of process parameters, and the group-wise judgment of qualified experts can improve the consistency of prioritisation.     

Measurement System Analysis in a Production Environment with Multiple Test Parameters

The purpose of measurement system analysis (MSA) is to separate the variation among devices being measured from the error in the measurement system. The total measurement system error can be further decomposed into variance components associated with the measurement equipment and repeatability. An analysis of variance approach based on a variance component model is used to model the variables of interest. Once estimated, the variance components are used to compute various metrics, which quantify the adequacy of the measurement system for the application in which it is used. Confidence intervals computed on the variance components and metrics indicate the amount of precision in the estimates. The MSA is typically conducted on a single measurement variable with a single measurement instance. The aim of this paper is to extend the univariate single-instance case to a common manufacturing test scenario where multiple parameters are tested on each device with a sequence of tests, which may include retest and test and repair steps. The methods presented are illustrated with examples from an industrial application.     

An inheritance complexity metric for object-oriented code: A cognitive approach

Software metrics should be used in order to improve the productivity and quality of software, because they provide critical information about reliability and maintainability of the system. In this paper, we propose a cognitive complexity metric for evaluating design of object-oriented (OO) code. The proposed metric is based on an important feature of the OO systems: Inheritance. It calculates the complexity at method level considering internal structure of methods, and also considers inheritance to calculate the complexity of class hierarchies. The proposed metric is validated both theoretically and empirically. For theoretical validation, principles of measurement theory are applied since the measurement theory has been proposed and extensively used in the literature as a means to evaluate the software engineering metrics. We applied our metric on a real project for empirical validation and compared it with Chidamber and Kemerer (CK) metrics suite. The theoretical, practical and empirical validations and the comparative study prove the robustness of the measure.     

Applying Job Shop Scheduling to SMEs Manufacturing Platform to Revitalize B2B Relationship

A small and medium enterprises (SMEs) manufacturing platform aims to perform as a significant revenue to SMEs and vendors by providing scheduling and monitoring capabilities. The optimal job shop scheduling is generated by utilizing the scheduling system of the platform, and a minimum production time, i.e., makespan decides whether the scheduling is optimal or not. This scheduling result allows manufacturers to achieve high productivity, energy savings, and customer satisfaction. Manufacturing in Industry 4.0 requires dynamic, uncertain, complex production environments, and customer-centered services. This paper proposes a novel method for solving the difficulties of the SMEs manufacturing by applying and implementing the job shop scheduling system on a SMEs manufacturing platform. The primary purpose of the SMEs manufacturing platform is to improve the B2B relationship between manufacturing companies and vendors. The platform also serves qualified and satisfactory production opportunities for buyers and producers by meeting two key factors: early delivery date and fulfillment of processing as many orders as possible. The genetic algorithm (GA)-based scheduling method results indicated that the proposed platform enables SME manufacturers to obtain optimized schedules by solving the job shop scheduling problem (JSSP) by comparing with the real-world data from a textile weaving factory in South Korea. The proposed platform will provide producers with an optimal production schedule, introduce new producers to buyers, and eventually foster relationships and mutual economic interests.     

Performance measurement using overall equipment effectiveness (OEE): literature review and practical application discussion

The quest for improving productivity in the current global competitive environment has led to a need for rigorously defined performance-measurement systems for manufacturing processes. In this paper, overall equipment effectiveness (OEE) is described as one such performance-measurement tool that measures different types of production losses and indicates areas of process improvement. Analysis is done on how OEE has evolved leading to other tools like total equipment effectiveness performance, production equipment effectiveness, overall factory effectiveness, overall plant effectiveness, and overall asset effectiveness. Two industrial examples of OEE application are discussed, and the differences between theory and practice analysed. Finally, a framework for classifying and measuring production losses for overall production effectiveness is proposed. The framework harmonizes the differences between theory and practice and makes possible the presentation of overall production/asset effectiveness that can be customized with the manufacturers needs to improve productivity.     

Quality and reliability assessment of hardware and software during the total product life cycle

A major challenge for the computer industry is the need for the development and implementation of an engineering languge which provides continuous linkages and measurement between the customer and all engineering functions internal to the company. Such a language would allow information solution developers and information service providers to be able to continuously monitor the quality and reliability performance of integrated hardware and software products during the complete product life cycle. A quantitative engineering language needs to be developed to provide seamless and continuous linkages between the customer—the user of integrated computer systems—and all of the engineering development and manufacturing functions tasked with designing and building solutions which meet customer needs. A methodology is proposed which addresses this challenge by the implementation of two metrics: total defects per unit (TDU) and the annual rate of events (ARE). These two metrics can measure all hardware, software and computer integration events during the total product life cycle. A methodology is presented which provides a rigorous translation of the ARE metric, monitored at the customer site, into the traditional reliability metrics used by engineering and manufacturing. Algorithms are presented which directly translate AREs into mean time between failures (MTBF), mean time between parts replacement (MTBPR) and mean time between system interruptions (MTBSI). The ARE metric meets the customer requirement of being able to clearly focus on the reliability and availability performance of total systems as a result of hardware and software components, the user interface, and environmental factors. The paper discusses the development and application of integrated TDU and ARE metrics and shows how the total product life cycle quality and reliability of a complex integrated computer and communications solution can be efficiently monitored and managed for improvement during design, manufacturing, and installation performance in an integrated customer environment.     

FMS Designer: An expert system for flexible manufacturing system design

Implementation of flexible manufacturing technology in the batch manufacturing environment has created major problems for designers and engineers who are responsible for specification and design of flexible manufacturing systems (FMS). The FMS design task appears to be an excellent application for expert systems techniques. This paper describes current results of an ongoing research effort to develop an expert system which analyses the output from an FMS simulation model, determines whether operational and financial objectives are met, identifies design deficiencies or opportunities for improvement, and proposes designs which overcome deficiencies or exploit improvement opportunities. An overview of the FMS design expert system is given and a case study is presented to illustrate how the system operates. Areas for future research are also discussed.     

Book reviews

In many current semiconductor manufacturing operations, headcount is manually allocated periodically based on man-machine ratio. Attributed to non-optimised allocation of direct labour to operations/machines, considerable productivity loss occurs. The problem is further complicated by some dynamic and uncertain factors such as constantly changing production targets and work in progress, overlapped labour skills, and variability in manufacturing operations and labour absenteeism rates. Motivated by the needs in real practice, this study aims to develop a model for allocating a direct workforce among semiconductor manufacturing operations to meet production targets and maximise labour productivity. This paper presents a two-stage goal programming model for the headcount allocation problem. To enhance the model's pragmatic use, a queueing module is introduced to account for the interferences among the multi-machine operations. Computational experiments are carried out to evaluate the performances of the proposed algorithms and pilot runs are implemented in a factory. Finally, a prototype system is developed and has been proved to be useful in practice.     

Dissertation Review

Abstract:

This research article provides valuable insights for practicing engineering managers on improving a firm's performance by applying a knowledge management (KM) based approach to quality management (QM). Traditional quality management systems do not provide sufficient knowledge management and knowledge creation opportunities for manufacturing firms to stay competitive in today's fast paced, unpredictable, complex, and rapidly changing global business environment. The world's body of knowledge does not include a quality management strategy where KM is integrated in QM, and where the effectiveness of such a KM/QM strategy is determined through quantitative empirical research over a defined time frame, thus omitting important performance improvement opportunities for manufacturing firms. The objective in addressing this research topic is to present a KM/QM strategy and to demonstrate its effectiveness. Engineering management areas, such as knowledge management and quality management, are leveraged throughout this research. Systems engineering aspects, such as operational efficiency improvement and system performance, are leveraged by integrating knowledge management and quality management to form an enhanced quality management system. The research demonstrates that a company with a KM/QM strategy is more effective than a company which does not have a KM/QM strategy, that the implementation of a KM/QM strategy contributes to product quality improvements over KM/QM strategy application time, and presents a framework that can be applied by practicing engineering managers.     

Sustainability characterisation for manufacturing processes

Manufacturing industries lack the measurement science and the needed information base to measure and effectively compare environmental performances of manufacturing processes, across resources and associated services with respect to sustainability. The current use of ad hoc methods and tools to assess and describe sustainability of manufactured products does not necessarily account for manufacturing processes explicitly, and hence results in inaccurate and ambiguous comparisons. Such comparisons do not proactively contribute to sustainability improvement. Further, we identified that there are no formal methods for acquiring and exchanging information that help establish a consolidated sustainability information base. Our ultimate goal is to develop the needed measurement science and methodology to evaluate sustainability of fundamental manufacturing processes to ensure reliable and consistent comparisons. As a precursor, based on a literature study, this paper identifies the required elements to evaluate sustainability performance for manufacturing with a focus on the environmental impact. Societal and economic impacts, although equally important, are beyond the scope of discussion in this paper. In this paper, we first discuss identified manufacturing process classifications, sustainable manufacturing indicators and computable metrics, relevant information models and software tools, a conceptual model for sustainability characterisation, and finally, conclude with an overview of the future research directions.     

Improving productivity of a multi-product machining line at a motorcycle manufacturing plant

This paper introduces a case study to improve productivity of a multi-product transmission case machining line at a motorcycle manufacturing plant. First, the manufacturing process is introduced to characterise the production flow. Through structural modelling, such a process is simplified through aggregations and transformed into a two-stage Bernoulli line model with split dedicated machines and finite buffers. Using Markov chain analysis, the system throughput can be estimated. The results are validated by plant data. To improve system productivity, through numerical experiments, we investigate the impacts of increasing machine efficiency, varying demands and implementing different loading policies. Such a study provides a quantitative tool for plant engineers and managers to improve production operations.     

A conceptual framework for the ubiquitous factory

To cope with today's industrial demands requiring (1) coverage of the whole product life cycle, (2) environmentally conscious manufacturing, (3) competitive sustainability manufacturing, etc., a new manufacturing paradigm should be developed. In this paper, we develop a conceptual framework for a new paradigm called ubiquitous factory (u-Factory) by applying ubiquitous computing technology to the manufacturing system. The u-Factory is based on our previously developed paradigm, called UbiDMR [1], meaning product design, manufacturing, and recycling via ubiquitous computing technology. The essence of u-Factory can be represented by three key phrases: (1) information transparency, (2) autonomous control, and (3) sustainable manufacturing. This paper comprises two parts. In the first part, we show the derivation procedure for the framework of the u-Factory using problem analysis of the current manufacturing system, design consideration and derivation of the architecture for the ubiquitous factory. In the second part, to demonstrate the validity and impact of the derived architecture, we develop the TO-BE model for manufacturing resource management. This is followed by a comparison with the AS-IS model.     

Guaranteed performance in reaching mode of sliding mode controlled systems

Conventionally, the parameters of a sliding mode controller (SMC) are selected so as to reduce the time spent in the reaching mode. Although, an upper bound on the time to reach (reaching time) the sliding surface is easily derived, performance guarantee in the state/error space needs more consideration. This paper addresses the design of constant plus proportional rate reaching law-based SMC for second-order nonlinear systems. It is shown that this controller imposes a bounding second-order error-dynamics, and thus guarantees robust performance during the reaching phase. The choice of the controller parameters based on the time to reach a desirable level of output tracking error (OTE), rather than on the reaching time is proposed. Using the Lyapunov theory, it is shown that parameter selections, based on the reaching time criterion, may need substantially larger time to achieve the OTE. Simulation results are presented for a nonlinear spring-massdamper system. It is seen that parameter selections based on the proposed OTE criterion, result in substantially quicker tracking, while using similar levels of control effort.