Revisiting local TOC measures in an internal supply chain: a note

While the global theory of constraints (TOC)-based measures of throughput, inventory and operating expense are well known, the literature on local measures of throughput dollar-days and inventory dollar-days (TDDs/IDDs) is sparse and inconclusive. This paper shows how adherence to these measures naturally puts a non-TOC company on a continuous improvement path by assisting in identification of the constraint and implementation of a drum–buffer–rope (DBR)-like system. Thus, by simulating a small company, this note highlights the relationship between TDD/IDD measures and a DBR system, which further promotes TOC principles. Ultimately, we provide future research avenues into the role these measures can play in the development of a holistic incentive system and the implementation of a DBR-like system across the supply chain network.     

Theory of constraints - A status report

The Theory of Constraints (TOC) was first applied to production scheduling. For many people this application is the only one TOC brings to mind. Over the past several years TOC has been applied to other areas such as performance measures, supply chains, marketing, sales and managing people. In this paper I review how TOC has been applied to these areas.     

基于制约的时间管理理论研究

针对作业成本法和制约理论的缺陷,同时利用二者的互补性,提出了基于制约的时间管理理论。在决策方面,该理论结合了作业成本法与制约理论的优点,通过分析特定环境下系统的时间、成本与现金收益之间的关系进行决策。在持续改善方面,该理论以制约理论为基础,结合作业成本法的作业分析技术以合理确定制约并对制约进行分类,通过对不同类型的制约采取不同的步骤以实现持续改善：对于非稳定制约,应采取措施尽快提升制约,使之发生转移;对于稳定制约,应在提升制约的同时,使非制约的行为服从于制约。     

A reactive decision-making approach to reduce instability in a master production schedule

One of the primary factors that impact the master production scheduling performance is demand fluctuation, which leads to frequently updated decisions, thereby causing instability. Consequently, global cost deteriorates, and productivity decreases. A reactive approach based on parametric mixed-integer programming (MIP) is proposed that aims to provide a set of plans such that a compromise between production cost and production stability is ensured. Several stability measures and their corresponding MIP model are proposed. An experimental study is performed to highlight the effectiveness of the reactive approach with regard to the proposed performance measures. It is observed that an improvement in stability does not mean a significant increase in the total production cost. Furthermore, the procedure yields a set of plans that in practice would enable flexible management of production.     

Integrating ABM/TOC approaches for performance improvement: A framework and application

During the last 20 years, new philosophies such as the Theory of Constraints (TOC) and Activity-Based Management (ABM) have emerged as viable, distinct alternatives for measuring and improving the performance of world-class manufacturing companies. In the early phase of their evolution, these philosophies differed fundamentally in their approaches; but there is increasing evidence that both philosophies have now evolved to a stage where an integrated framework can be developed for enhancing cross-functional coordination, thereby improving organizational performance. In this paper, a general framework is presented that integrates the basic concepts, as well as recent advances of TOC and ABM philosophies, into a cohesive framework that can be used to guide performance improvement initiatives . A successful implementation in a world-class manufacturing company is described to validate the framework and to demonstrate that these philosophies can indeed work together in positive ways. Insights are provided into the reasons for implementing such an integrative framework, e.g. identifying the most profitable mix of products, allocating scarce resources optimally, establishing priorities for process improvements, analysing the strategic investment (or expansion) alternatives, and exploiting new market segments.     

Robust scheduling of a two-machine flow shop with uncertain processing times

This paper focuses on manufacturing environments where job processing times are uncertain. In these settings, scheduling decision makers are exposed to the risk that an optimal schedule with respect to a deterministic or stochastic model will perform poorly when evaluated relative to actual processing times. Since the quality of scheduling decisions is frequently judged as if processing times were known a priori, robust scheduling, i.e., determining a schedule whose performance (compared to the associated optimal schedule) is relatively insensitive to the potential realizations of job processing times, provides a reasonable mechanism for hedging against the prevailing processing time uncertainty. In this paper we focus on a two-machine flow shop environment in which the processing times of jobs are uncertain and the performance measure of interest is system makespan. We present a measure of schedule robustness that explicitly considers the risk of poor system performance over all potential realizations of job processing times. We discuss two alternative frameworks for structuring processing time uncertainty. For each case, we define the robust scheduling problem, establish problem complexity, discuss properties of robust schedules, and develop exact and heuristic solution approaches. Computational results indicate that robust schedules provide effective hedges against processing time uncertainty while maintaining excellent expected makespan performance     

Optimisation of the broiler production supply chain

In this paper, we propose a mixed integer programming (MIP) model for the Chicken Flock Sizing, Allocation and Scheduling Problem (CFSASP), which is an important planning problem in the broiler production supply chain. To solve the CFSASP efficiently, two variants of rolling horizon heuristics (RHHs) have been developed and applied on the case of a Norwegian broiler production company. Computational results show that the RHHs successfully obtain high-quality solutions within a reasonable time. The value of optimisation is verified through comparison with the case company's plans, where the solutions from optimisation outperforms the current solutions. Sensitivity analyses are also conducted to provide managerial insights regarding certain strategic decisions, such as how many and which days to use for hatching of chickens. Due to the promising results, the case company is now implementing an optimisation-based decision support system based on the MIP model and solution methods shown in this paper.     

Modelling and performance of distributed algorithm for scheduling dissimilar machines with set-up

Distributed arrival time control is a highly decentralized scheduling approach where each part entity autonomously controls its arrival time to meet the due-date in real time. This paper presents differential equation-based models for distributed arrival time control of parallel dissimilar machines including sequence-dependent set-up and flowshop scheduling. The main objective was to show that the behaviour of general systems under distributed arrival time control was predictable. Convergence properties of the resulting nonlinear systems were established using the theory of discontinuous differential equations. Geometry was used to gain insight into the behaviour of these nonlinear systems. An approximation model was proposed for mean arrival times when the dynamics resulted in a non-unique steady-state. The model was tested using numerical simulation and agreed well. Geometric insights were also used to investigate scheduling performance of distributed arrival time control. Simulation results indicated that distributed arrival time control could provide significant improvement, typically more than 20%, over commonly used dispatching rules for due-date-based measures. Improved predictability and favourable performance made distributed arrival time control an attractive approach for decentralized control of Just-In-Time production.     

Effect of load,processing time and due date variation on the effectiveness of scheduling rules

In real manufacturing environments, variations in production factors (i.e. processing time, demand, due-dates) are inevitable facts. All these dynamic changes, together with random disturbances (e.g. machine breakdowns) can seriously affect the system performance. In this paper we focus on load, processing time and due date variation and analyse their impacts on a scheduling system. Specifically, we investigate the impact of variation on dispatching policies in a job shop environment via simulation. The statistical analysis of the results leads to two major conclusions: first, the relative performance of rules is not threatened much by PV (processing time variation), LV (load variation) or DDV (due date variation) -- a result that can be a consolation for practitioners in the field. Secondly, the performance of the rules deteriorates, in particular at high levels of PV, LV and DDV -- a result that can provide new insights into the problem and produces useful information for researchers in their continuous effort to develop better dispatching rules.     

Linking management control system with product development and process decisions to cope with environment complexity

In this study, we report the results of a large-scale survey study on business strategy and innovations conducted in China in November 2002. We focus on the impact of environmental complexity on the choice of management control systems and the effects of management control systems on product development and process decisions. Our findings provide some valuable insights to strategic change. When the pressure for change comes from the external environment, the strategic control approach is usually chosen to cope with environmental complexity. Strategic control focuses on the process of benchmarking a firm's strategic and operational decisions with that of their competitor's. Additionally, strategic control emphasises R&D and new product development, and aligns manufacturing strategy with corporate objectives. This approach has a long-term impact on a firm's position in the market place. When the pressure for change comes from the inside of an organisation and the company is more interested in maintaining its current market position, financial control approach is usually chosen to cope with environment complexity. Financial control approach focuses on incremental improvement in the manufacturing process and short-term financial performance. This approach provides risk-averse benefits to the company.     

Adaptive scheduling in random flexible manufacturing systems subject to machine breakdowns

In a FMS dynamic scheduling environment, frequent rescheduling to react to disruptions such as machine breakdowns can make the behaviour of the system hard to predict, and hence reduce the effectiveness of dynamic scheduling. Another approach to handle the disruptions is to update the job ready time and completion time, and machine status on a rolling horizon basis, and consider the machine availability explicitly in generating schedules. When machine downtime has a small variation, the operation completion time is estimated by using limiting (steady-state) machine availability. However, steady-state analysis is sometimes unlikely to provide a complete picture of the system when there is a large variation in machine downtime and repair time, and frequent disruptions (e.g. tool failures) exist. Transient analysis of machine availability will be more meaningful in such a situation during a finite observation period. In this paper, an adaptive scheduling approach is proposed to make coupled decisions about part/machine scheduling and operation/tool assignments on a rolling horizon basis, while the operation completion time is estimated by a transient machine availability analysis based on a two-state continuous time Markov process. The expected tool waiting time is explicitly considered in the job machine scheduling decision. The effectiveness of the proposed method is compared with other approaches based on various dispatching heuristics such as Apparent Tardiness Cost, Cost OVER Time, and Bottleneck Dynamics, etc under different shop load and machine downtime levels.     

Assessing the performance of a vertically disintegrated chain by the DEA approach–a case study of Taiwanese semiconductor firms

The competitiveness of an individual firm depends upon the competitiveness of the value chain to which it belongs. The core constraint of virtually every chain is that chains are structured, measured and managed in parts, i.e. vertically disintegrated, rather than as a whole. Assessing the performance of vertically disintegrated firms can provide an insight of how each firm acts in such a value chain. This paper aims to study the operating performance of the vertically disintegrated chain in the integrated circuits (IC) industry. A performance evaluation was completed for 48 leading vertically disintegrated semiconductor companies in Taiwan, including 17 in design, 10 in fabrication and 21 in packaging/testing, using the data envelopment analysis (DEA) approach which inherently recognises tradeoffs among various measures. The empirical results indicate that the IC design firms perform better than IC fabrication and IC packaging/testing firms. Overall, semiconductor firms in Taiwan still have room for performance improvement. In addition, the empirical results suggest that an IC company's scale of size does play an important role in influencing its operating efficiency. One can increase performance by consolidating with other smaller units to achieve an optimal size. In addition, an analysis of operating performance by DEA can provide a semiconductor firms’ operations with insights into resource allocation competitive advantages, and help with strategic decision-making, especially regarding operational styles under an intense competitive environment.     

基于TOC的会计成本核算法在战略决策中的实证研究

研究了基于TOC的会计成本核算法在战略决策中的应用。基于TOC的会计成本核算法是由Dr Eli Goldratt提出的制约因素管理思想应用到会计领域,形成的一套成本核算指标体系。它的指标主要有产出贡献、库存及经营费用。把这一会计成本核算法运用到基于紧缩、成本及市场等三种战略决策中,采用Gedunken试验,并与传统的完全吸收式会计成本核算法进行了对比的实证研究。实证结果表明,在这三种战略决策的应用中,基于TOC的会计成本核算法优于传统的方法,使公司净利润增加明显,并极大的改善了组织性能。     

A Generalized Goal Programming Approach to the Minimal Interference, Multicriteria N × 1 Scheduling Problem

The conventional approach to the modeling and solution of most scheduling problems involves the development of a mathematical model which (1) employs discrete variables (e.g., linear integer programs), and (2) includes only a single objective to be maximized or minimized (e.g., minimization of makespan). Unfortunately, models involving discrete variables are inherently combinatorially explosive (i.e., methods such as branch-and-bound will exhibit computation times which grow exponentially with problem size). Further, scheduling problems encountered in the real world invariably involve multiple conflicting objectives, and thus using a single-objective representation can lead to gross oversimplification. In this paper we address a specific class of scheduling problem encountered in several real-world applications that may be efficiently addressed as a linear multiobjective model having only continuous variables. The model and its solution are compared with those of a highly acclaimed recent approach, and they appear to provide significant improvements.     

A priority scheduling approach for flexible job shops with multiple process plans

This paper considers the job shop scheduling problem with alternative operations and machines, called the flexible job shop scheduling problem. As an extension of previous studies, operation and routing flexibilities are considered at the same time in the form of multiple process plans, i.e. each job can be processed through alternative operations, each of which can be processed on alternative machines. The main decisions are: (a) selecting operation/machine pair; and (b) sequencing the jobs assigned to each machine. Since the problem is highly complicated, we suggest a practical priority scheduling approach in which the two decisions are done at the same time using a combination of operation/machine selection and job sequencing rules. The performance measures used are minimising makespan, total flow time, mean tardiness, the number of tardy jobs, and the maximum tardiness. To compare the performances of various rule combinations, simulation experiments were done on the data for hybrid systems with an advanced reconfigurable manufacturing system and a conventional legacy system, and the results are reported.     

制约理论在管理会计中的应用一例

制约理论是当今美国和欧洲比较流行的一套管理哲理和持续改进方法。通过实际案例，根据Goldratt的5个关键步骤，找到系统的制约，利用开发和释放制约从而使系统的整体能力得以提高。     

Impact of throughput-based objectives and machine grouping decisions on the short-term performance of flexible manufacturing systems

The most commonly-used objective in the literature for solving machine loading and grouping problems in flexible manufacturing systems is the maximization of steady-state throughput. In reality, orders arrive dynamically and the mix of products changes frequently, raising questions about the applicability of this objective in achieving shorter-term measures of the output rate. Moreover, the loading and grouping problems only determine which operations are to be performed on each machine, and whether any of the machines should be identically tooled so as to allow multiple routeings for some jobs. The actual short-term performance of the system also depends on scheduling and dispatching decisions. We study the impact of the various objectives ostensibly related to steady-state throughput and machine grouping decisions on the short-term makespan performance (in a static setting). Computational results suggest that minimizing the maximum percentage overload (relative to the optimal continuous workload allocation) across machine groups is an excellent objective. The results also indicate that reducing the number of machine groups and balancing workloads among the machines help to reduce makespan.     

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.     

Performance-based dynamic scheduling model for flexible manufacturing systems

A performance-based dynamic scheduling model for random flexible manufacturing systems (FMSs) is presented. The model is built on the mathematical background of supervisory control theory of discrete event systems. The dynamic FMS scheduling is based on the optimization of desired performance measures. A control theory-based system representation is coupled with a goal programming-based multi-criteria dynamic scheduling algorithm. An effectiveness function, representing a performance index, is formulated to enumerate the possible outputs of future schedules. Short-term job scheduling and dispatching decisions are made based on the values obtained by optimizing the effectiveness function. Preventive actions are taken to reduce the difference between actual and desired target values. To analyse the real-time performance of the proposed model, a software environment that included various Visual Basic Application® modules, simulation package Arena®, and Microsoft Access® database was developed. The experimentation was conducted (a) to determine the optimum look-ahead horizons for the proposed model and (b) to compare the model with conventional scheduling decision rules. The results showed that the proposed model outperformed well-known priority rules for most of the common performance measures.     

Learning dispatching rules using random forest in flexible job shop scheduling problems

In this paper, we address the flexible job-shop scheduling problem (FJSP) with release times for minimising the total weighted tardiness by learning dispatching rules from schedules. We propose a random-forest-based approach called Random Forest for Obtaining Rules for Scheduling (RANFORS) in order to extract dispatching rules from the best schedules. RANFORS consists of three phases: schedule generation, rule learning with data transformation, and rule improvement with discretisation. In the schedule generation phase, we present three solution approaches that are widely used to solve FJSPs. Based on the best schedules among them, the rule learning with data transformation phase converts them into training data with constructed attributes and generates a dispatching rule with inductive learning. Finally, the rule improvement with discretisation improves dispatching rules with a genetic algorithm by discretising continuous attributes and changing parameters for random forest with the aim of minimising the average total weighted tardiness. We conducted experiments to verify the performance of the proposed approach and the results showed that it outperforms the existing dispatching rules. Moreover, compared with the other decision-tree-based algorithms, the proposed algorithm is effective in terms of extracting scheduling insights from a set of rules.