A novel metric for determining the constraining effect of resources in manufacturing via simulation

This paper provides a novel method for determining the constraining effect of resources in a manufacturing system using discrete event simulation. Traditionally manufacturing systems are constrained by one or more bottlenecks. Eliminating or mitigating the bottleneck will speed up the system throughput. However, bottlenecking resources generally only refer to machines, and primarily focus on flow-shops not job-shops. One important resource we believe that is often overlooked is workers and their associated skills, and we propose that a particular skill could be flagged as a bottleneck resource. We define new metrics known as resource constraint metrics (RCM) for measuring the constraining effect of a resource on the entire manufacturing system. These metrics are flexible and differentiate between the constraining effects of machines and their requested skills. The metrics can also deal with complex workflows with alternative routing, alternative resources, calendars (a necessary consideration when dealing with workers), worker performance, and multiple modes of operation of machines (e.g. run, setup, and maintenance). The use of RCMs in simulation aids in real-world decision-making, by determining which resource should be focussed on and improved to reduce the overall system feeling constrained. This will have the effect of increasing throughput or at least providing the capacity for increased throughput.     

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.     

The use of bucket brigades in zone order picking systems

As the transactions through electronic commerce and TV home shopping increase, the warehouses often receive a large amount of small orders to be picked within tight time windows. One of the important warehousing activities is order picking, the process of retrieving a number of items from warehouse storage to meet a number of independent customer orders. This paper examines a new order picking method, bucket brigade order picking (BB picking). Bucket brigade is a way of coordinating workers who progressively perform a set of operations on a flow line. In the BB picking system, a worker performs operations on an order until the next worker downstream takes it over; then goes back to the previous worker upstream to take over a new order. We discuss distinct characteristics in order picking systems when bucket brigades are applied. We identify some efficiency losses under the BB picking and present a new BB picking protocol to improve the performance of order picking systems. The new BB picking is compared with the existing BB picking and zone picking through simulation experiments.     

Performance Opportunity for Workforce Agility in Collaborative and Noncollaborative Work Systems

To gain insight into the potential logistical benefits of worker cross-training and agile workforce policies, we study simple models of flexible workers in serial production systems. The primary control issue is how to assign workers to jobs/stations over time. Under assumptions of complete worker flexibility and collaborative work, we prove that a simple expedite policy minimizes along each sample path the cycle time (delay) for each job. Therefore, the expedite policy also minimizes work in process and maximizes throughput along every sample path. We also compute the performance improvement opportunity achievable using flexible workers as opposed to the optimal static allocation of workers. This enables us to examine th e factors that make workforce agility a potentially attractive strategy. We also consider the intuitive analog of the expedite policy for the noncollaborative work environment, which we call the pick-and-run policy; however, we demonstrate by counterexample that it is not always optimal. Finally, we extend some of our insights from the demand-constrained environment to a capacity-constrained environment operating under a CONstant WIP (CONWIP) protocol     

Managing worker flexibility and attrition in dual resource constrained job shops

Worker flexibility is an attractive option for enhancing manufacturing performance, since it provides several strategic advantages, and also allows the firm to buffer against uncertainty. In this paper, we investigate issues related to acquiring a flexible work-force in those dual resource constrained (DRC) job-shops that have high learning costs and the presence of worker attrition. Our results show that significant improvement in traditional shop related measures can be attained at even very high attrition rates by incrementally training each worker in one additional department. However, this improvement comes at the expense of productivity losses which shop managers may not be willing to accept. The nature of the tradeoffs that exist in acquiring this incremental worker flexibility are addressed. Two strategies for improving shop performance, based on incrementally training workers or reducing attrition rates, are also recommended for the DRC shop modelled in our study. Conditions under which either of these two strategies should be pursued by the shop managers are also identified.     

A socio-behavioural simulation model for engineering design optimization

This paper proposes a method for solving single objective constrained optimization problems by way of a socio-behavioural simulation model. The essence of the methodology is derived from the concept that the behaviour of an individual changes and improves due to social interaction with the society leaders. Leaders are identified after all individuals of a society are Pareto ranked according to constraint satisfaction. At the higher end, leaders of all societies interact among themselves for the overall improvement of the societies. Such overall improvement of individual societies leads to a better civilization. Four well-studied single objective constrained optimization problems have been solved to show the efficacy of the proposed methodology.     

The measurements of autonomy and technology for task svstems design

Numerous experiments in job enlargement, direct worker participation and autonomous workgroups have been reported. However, little cumulative knowledge has been gained through a lack of conceptual and theoretical bases. Moreover, worker autonomy and technology, the major variables in those experiments either were measured by questioning workers and supervisors and are consequently modified by their perception, or were measured in a very broad fashion.

In the present work more objective and specific measures for technology and autonomy, which can link job design research and experiments with job design practice, were developed and validated. Automony and technology are of course multidimensional concepts. Relevant aspects for job design are derived from a model for task related decision making which shows that the degree of worker autonomy is contingent on task technology and worker characteristics.

Technology is measured by cycle time, task repetitiveness, task skill level, disturbance level of a task and the disruption potential of a task system, through observation. A distinction is made between autonomy in regulation, which keeps the process on target by counteracting disturbances and autonomy in control which specifies targets. Measures for both types of autonomy are developed. The measures of technology and autonomy were tested in a sample consisting of 411 work stations in industry and proved to be effective and efficient for job design purposes.     

A comparison of mean reduction versus variance reduction in processing times in flow shops

A constant industry challenge is to find ways to improve production capabilities within the boundaries established by constrained resources. There are several types of production improvement that represent laudable management goals, and there are many ways to achieve production improvement goals. In this paper, we focus on two production metrics and two means of improvement. Specifically, we are concerned with finding ways to decrease manufacturing work in process levels and cycle times in flow shops. A common method of improvement is through mean processing time reduction that is frequently brought about by technological improvements in processing equipment. Another common means of improvement that is receiving increasing attention in the literature is variance reduction. Herein, we quantify and compare the levels of improvement brought about by reduction in both mean and variance of processing times.     

Multiple-Technique Approach for Improving a Performance Measurement and Management System: Action Research in a Mining Company

Abstract

Many enterprises encounter difficulty in managing strategic goals and performance measures because of a lack of a systemic and objective procedure and method. Action research was conducted through a case study approach at a mining company to investigate this problem regarding performance measurement and management (PMM) and to identify potential solutions for application. This research proposes a multiple-technique approach (including the Delphi method, analytic network process, system dynamics simulation, dashboard visualization, and radio frequency identification tracking techniques) and a creative implementation framework for overcoming the shortcomings within a case company and those in the relevant performance measurement literature. This study demonstrates a step-by-step approach for establishing a PMM system using a real case example. Furthermore, for sustaining the effectiveness and efficiency of the PMM system, key performance indicators (KPIs) were continually monitored against a changing operational environment through design, measurement, analysis, and improvement processes to address problem areas occurring in the production and maintenance departments of a mining company. The benefits of the PMM system in practice are demonstrated by the outcomes of the mining company, with noticeable improvements observed in many KPIs.     

Unsupervised Anomaly Detection via DBSCAN for KPIs Jitters in Network Managements

For many Internet companies, a huge amount of KPIs (e.g., server CPU usage, network usage, business monitoring data) will be generated every day. How to closely monitor various KPIs, and then quickly and accurately detect anomalies in such huge data for troubleshooting and recovering business is a great challenge, especially for unlabeled data. The generated KPIs can be detected by supervised learning with labeled data, but the current problem is that most KPIs are unlabeled. That is a time-consuming and laborious work to label anomaly for company engineers. Build an unsupervised model to detect unlabeled data is an urgent need at present. In this paper, unsupervised learning DBSCAN combined with feature extraction of data has been used, and for some KPIs, its best F-Score can reach about 0.9, which is quite good for solving the current problem.     

On the who-rule in Dual Resource Constrained (DRC) manufacturing systems

The who-rule is a labour allocation rule used in labour and machine-limited dual resource constrained (DRC) systems. A who-rule selects one worker out of several workers to be transferred to a work centre. By means of a practical instance, the paper shows that the who-rule plays a role in the daily practice of worker assignment. Previous simulation studies, however, either have not mentioned the who-rule or have treated it as a fixed factor. The present study will explore the need of including the who-rule in simulation studies. It will describe in detail at what decision moments the who-rule needs to be applied in simulation. Further, it will explore the flow time effects of applying different who-rules in several DRC systems where labour flexibility is limited and workers differ with respect to task proficiencies, the number of skills they possess and the loads of work centres for which they are responsible. As with other labour allocation rules, the impact of the who-rule depends on the specific DRC shop modelled. The paper will show that the average labour utilization, and the types and extent of worker differences, determine the impact of the who-rule on shop performance.     

Externally constrained plastic flow in miniaturized metallic structures: A continuum-based approach to thin films, lines, and joints

Mechanical behavior of materials at small length scales has received significant attention in recent years, due mainly to the development of devices and components having micro- and nano-scale feature sizes. In miniaturized structures, deformation is heavily influenced by the physical confinement imposed on the material. The present article is devoted to this type of constrained plastic deformation in metals. Continuum plasticity is used as a primary tool to describe the deformation features, for the purpose of establishing an overall mechanistic view which is often missing in the materials science community. We discuss recent progresses in understanding the externally influenced plastic flow behavior in selected metallic structures, including thin continuous films attached to stiff or compliant substrates, various forms of metal lines in modern semiconductor devices, and metallic joints in electronic packages. Special emphasis is on the evolution of local deformation pattern and overall mechanical response, and their implications in the interpretation of experimental results and the structural integrity for real-life applications. Aside from the review of current status of knowledge, we also address common misconceptions, remaining challenges as well as directions of future research in constrained small-scale plasticity.     

State-based analysis of labour productivity

Establishing efficient continuous improvement processes requires industrial companies to analyse their productivity quickly on different work system levels and to link productivity losses with suitable improvement measures in the course of productivity management. Common productivity analyses are either narrowed to certain functions of a production process or they do not possess a sufficient level of detail to derive goal-oriented improvement measures. The challenge is to gain production data with a relatively low effort and to gain broad transparency over productivity losses from the work place to the company level at the same time. This paper presents a new methodology for the comprehensive analysis of the various impacts on labour productivity, relying on state-based modelling of worker activities in serial production. Typical application areas include the automotive industry or the production of home appliances. The approach combines straightforward data acquisition methods with a structured evaluation process as foundation for the productivity management on different work system levels, including work stations, production lines, production segments and the plant. An integrated matching procedure processes the analysis results and yields a set of applicable improvement methods from a definable toolset. Compared with existing methodologies, the underlying model promises a reduced data acquisition effort and high usability. Its potential for practical application is shown with two industrial case studies.     

A framework for the continuous performance improvement of manned assembly lines

This paper proposes a framework for the continuous performance improvement of manned assembly lines for major appliances. The suggested framework consists of two main activities: (1) assembly work process improvement, including time and motion study and (2) improvement of line balance efficiency. Although there have been numerous studies on this topic, most of them deal with partial issues rather than the continuous performance improvement of the whole assembly line, which this paper addresses. To develop the framework, we categorised a manned assembly line into five analysis levels, (workstation, worker, operation cycle, work element and unit motion), and identified user requirements on each level. Among these five analysis levels, the workstation level supports line balancing, while the other four (worker, operation cycle, work element and unit motion) address work process improvement. The proposed framework has been implemented and tested with various examples from Korean assembly line based manufacturers.     

Combination of Lean value-oriented conception and facility layout design for even more significant efficiency improvement and cost reduction

Global market competition and fluctuating customers’ demands require manufacturing enterprises to focus on cost reduction and efficiency improvement to increase competitiveness and sustainability. The purpose of the research was the elaboration of the methodology and procedure of a new combined efficiency improvement method which basically applies Lean methods and also uses the facility layout design (FLD) method simultaneously, integrating the different advantages of these methods, which is even more efficient that applying each of the methods individually. The main significant added-value of the study is the elaboration of a new combined method, which results in even more significant improvement of efficiency and several KPIs, furthermore, cost reduction, which is confirmed by a real case study for the improvement of a manufacturing plant. In the case study, the application of 13 Lean methods and the FLD method (which aims at the minimisation of material workflow, travel distance of materials, material handling cost and space used for assembly) led to the improvement of 10 quantitative and 5 qualitative indicators: productivity; cycle-time; number of workstations and operators; WIP (work-in-process) inventories; space used for assembly; material workflow; travel distance of materials; material handling cost; labour cost; component supply; products’ quality; transparency; standardisation; workplace ergonomics.     

Process improvement in healthcare: overall resource efficiency

This paper aims to develop a unifying and quantitative conceptual framework for healthcare processes from the viewpoint of process improvement. The work adapts standard models from operation management to the specifics of healthcare processes. We propose concepts for organizational modeling of healthcare processes, breaking down work into micro processes, tasks, and resources. In addition, we propose an axiological model which breaks down general performance goals into process metrics. The connexion between both types of models is made explicit as a system of metrics for process flow and resource efficiency. The conceptual models offer exemplars for practical support in process improvement efforts, suggesting to project leaders how to make a diagrammatic representation of a process, which data to gather, and how to analyze and diagnose a process's flow and resource utilization. The proposed methodology links on to process improvement methodologies such as business process reengineering, six sigma, lean thinking, theory of constraints, and total quality management. In these approaches, opportunities for process improvement are identified from a diagnosis of the process under study. By providing conceptual models and practical templates for process diagnosis, the framework relates many disconnected strands of research and application in process improvement in healthcare to the unifying pursuit of process improvement. Copyright © 2011 John Wiley & Sons, Ltd.     

Factors affecting opportunity of worksharing as a dynamic line balancing mechanism

We consider the problem of optimal worksharing between two adjacent workers each of whom processes a fixed task in addition to their shared task(s). We use a Markov Decision Process (MDP) model to compute optimal policies and provide a benchmark for evaluating threshold policy heuristics. Our approach differs from previous studies of dynamic line balancing in that we focus on system architecture factors that affect the performance improvement opportunity possible through worksharing relative to a traditional static worker allocations, as well as practical heuristics for worksharing. We find that three such factors are significant whether we use an optimal or a heuristic control policy: ability to preempt the shared task, granularity of the shared task and overall variability of the task times. Understanding the role of these factors in a given production environment provides a means for determining where and how worksharing can have significant logistical benefits.     

A system of safety management practices and worker engagement for reducing and preventing accidents: An empirical and theoretical investigation

Objective

The overall research objective was to theoretically and empirically develop the ideas around a system of safety management practices (ten practices were elaborated), to test their relationship with objective safety statistics (such as accident rates), and to explore how these practices work to achieve positive safety results (accident prevention) through worker engagement.

Method

Data were collected using safety manager, supervisor and employee surveys designed to assess and link safety management system practices, employee perceptions resulting from existing practices, and safety performance outcomes.

Results

Results indicate the following: there is a significant negative relationship between the presence of ten individual safety management practices, as well as the composite of these practices, with accident rates; there is a significant negative relationship between the level of safety-focused worker emotional and cognitive engagement with accident rates; safety management systems and worker engagement levels can be used individually to predict accident rates; safety management systems can be used to predict worker engagement levels; and worker engagement levels act as mediators between the safety management system and safety performance outcomes (such as accident rates).

Implications

Even though the presence of safety management system practices is linked with incident reduction and may represent a necessary first-step in accident prevention, safety performance may also depend on mediation by safety-focused cognitive and emotional engagement by workers. Thus, when organizations invest in a safety management system approach to reducing/preventing accidents and improving safety performance, they should also be concerned about winning over the minds and hearts of their workers through human performance-based safety management systems designed to promote and enhance worker engagement.     

Performance study of marginal posterior density estimation via Kullback-Leibler divergence

In this article, we introduce Kullback-Leibler (K-L) divergence as a performance measure of marginal posterior density estimation. We show that the K-L divergence can be used to compare two density estimators as well as to assess convergence of a marginal density estimator. We also examine performance of the importance-weighted marginal density estimation (IWMDE) proposed by Chen (1994) under the K-L divergence and we further extend the IWMDE to some more complex Bayesian models where the kernel method, which is widely used for estimating marginal densities using Markov chain Monte Carlo (MCMC) sampling outputs is not applicable. Finally, we use a constrained linear multiple regression model as an example to illustrate our methodology.     

An Empirical Study of Large-Sized Companies With Knowledge Work Teams and Their Impacts on Project Team Performance

Abstract:

This article contributes to how teams comprised mainly of knowledge workers influence project team performance. This article highlights the need to examine knowledge work and knowledge worker teams' impact on performance. Three large-sized companies were investigated through their projects, knowledge work, team environment, and team success. Results showed that the percentage of knowledge workers on a project team and project objectives had positive associations on performance. Exploratory analyses revealed that project team responsibility influenced knowledge worker project team performance as defined by projects schedule, cost, and scope.