Planning and Control in Multi-Cell Manufacturing

This research compares Material Requirements Planning (MRP), Kanban, and Period Batch Control (PBC) as alternative approaches to the planning and control of multi-cell manufacturing involving flow cells and assembly. Since previous research on performance of these systems in cellular manufacturing has been primarily conceptual, the experiments reported here provide new insights into their comparative performance. The results show that the production environment is a major factor in system choice. Three operating factors—Master Production Schedule (MPS) volume variation, MPS mix variation, and setup time/lot size—clearly affect system choice. All systems performed well under Justin-Time (JIT) conditions; there was no advantage to Kanban. Under the mixed conditions of high MPS variation, but small setup time/lot sizes, PBC produced superior performance compared to Kanban and MRP. Under non-JIT conditions, MRP was seen as clearly more effective. Finally, the results indicate that when conditions permit very small lot sizes relative to requirements, Kanban may perform best, even when MPS variation is high.     

Evaluation of scheduling strategies for a dynamic job shop in a tool-shared,flexible manufacturing environment

A two-phase approach is used to examine the impact of job scheduling rules and tool selection policies for a dynamic job shop system in a tool-shared, flexible manufacturing environment. The first phase develops a generalized simulation model and analyses 'simple' job scheduling rules and tool selection policies under various operating scenarios. The results from this investigation are then used to develop and analyse various bi-criteria rules in the second phase of this study. The results show that the scheduling rules have the most significant impact on system performance, particularly at high shop load levels. Tool selection policies affect some of the performance measures, most notably, proportion of tardy jobs, to a lesser degree. Higher machine utilizations can be obtained at higher tool duplication levels but at the expense of increased tooling costs and lower tool utilization. The results also show that using different processing time distributions may have a significant impact on shop performance.     

Comparing reactive Kanban and reactive CONWIP

This paper compares a reactive Kanban system to a reactive CONWIP system under conditions of unstable changes in demand using simulation experiments. After an introduction, a model of the JIT ordering systems, the Kanban and the CONWIP systems, is constructed. In order to obtain the fundamental information for developing a control rule of buffer size, the performance of the two types of the JIT ordering systems is analysed under various stable-demand conditions by simulation experiments. Based on the results, the reactive JIT ordering systems are proposed, and the performance of the proposed systems is investigated. The results showed that both of the proposed systems can react to unstable changes in demand and maintain the mean waiting time of demand at less than the required level. In the reactive Kanban system, the total of the mean work-in-process inventories becomes much less than that in the traditional Kanban system without controlling buffer size. However, in the reactive CONWIP system, the total of the mean work-in-process inventories becomes much more than or nearly equal to that in the traditional CONWIP system without controlling buffer size under the strongly correlated or the weakly correlated processing times, respectively. Based on the results, it can be claimed that, in the proposed systems, the reactive Kanban system is more effective to react to unstable changes in demand than the reactive CONWIP system.     

Alternate Routing Strategies in Batch Manufacturing: An Evaluation

Batch manufacturing firms are experiencing significant changes because of technological developments in work center design, such as flexible manufacturing systems (FMS) and planning/control tools like computer-aided process planning (CAPP). These new developments provide production managers with some solutions to a number of complex problems. For example, numerical-controlled (NC) machine center installations are effective in providing quality parts because of tight tolerance specifications built into the equipment. However, these highly efficient centers create bottlenecks that constrain shop throughput, since production planners tend to rely too much on them. To help improve manufacturing planning, we introduce an important element to the batch production scheduling component of CAPP's mission—evaluating possible alternate routes. Production scheduling encompasses job route selection as well as machine center assignment (loading), job releasing, and setting due dates. In this paper, three routing strategies requiring different levels of shop floor information are tested and evaluated using computer simulation. Shop performance is measured by total cost and traditional measures of job flow time, lateness, and tardiness.     

A FRAMEWORK AND MEASUREMENT INSTRUMENT FOR JUST-IN-TIME MANUFACTURING

While Just-in-Time (JIT) manufacturing has emerged as one of the major tools to enhance manufacturing competitiveness, no attempt has been made to develop a reliable and valid measurement instrument for empirical research in JIT. Without such an instrument, generalization beyond the immediate sample is difficult or misleading. We have proposed a JIT framework and developed a valid and reliable instrument with 16 summated scales for dimensions that capture essential aspects of JIT useful in assessing its impact in manufacturing environments. In addition, we discuss in detail the interactive nature of JIT practice. And, we propose a step-by-step approach to reliability and validity testing. Four JIT practices (equipment layout, pull system support, supplier quality level, and Kanban) are identified as major contributing factors to JIT performance.     

MRP in a job shop environment using a resource constrained project scheduling model

One of the most difficult tasks in a job shop manufacturing environment is to balance schedule and capacity in an ongoing basis. MRP systems are commonly used for scheduling, although their inability to deal with capacity constraints adequately is a severe drawback. In this study, we show that material requirements planning can be done more effectively in a job shop environment using a resource constrained project scheduling model. The proposed model augments MRP models by incorporating capacity constraints and using variable lead time lengths. The efficacy of this approach is tested on MRP systems by comparing the inventory carrying costs and resource allocation of the solutions obtained by the proposed model to those obtained by using a traditional MRP model. In general, it is concluded that the proposed model provides improved schedules with considerable reductions in inventory carrying costs.     

An algorithm to dynamically adjust the number of Kanbans in stochastic processing times and variable demand environment

In a real-life environment, the just-in-time JIT system is subjected to various types of uncertainties such as stochastic processing times and variable demand. Since, JIT was only meant to operate in a deterministic environment, its performance is seriously affected by variations in processing times and demand. In this paper, a newly developed Kanban system is presented which uses an algorithm to dynamically and systematically manipulate the number of Kanbans in order to offset the blocking and starvation caused by the said uncertainties during a production cycle. The new system is termed a flexible Kanban system FKS . The steps of the algorithm are detailed and the effectiveness of FKS is demonstrated using an example model. For the example model, the solution procedure, results and a discussion are presented.     

An approach to learning from both good and poor factory performance in a Kanban-based just-in-time production system

In a JIT manufacturing environment it may be desirable to learn from an archived history of data that contains information that reflects less than optimal factory performance. The purpose of this paper is to use rule induction to predict JIT factory performance from past data that reflects both poor (saturated or starved) and good (efficient) factory performance. Inductive learning techniques have previously been applied to JIT production systems (Markham et al. , Computers and Industrial Engineering, 34 , 717-726, 1998; Markham et al. , International Journal of Manufacturing Technology Management, 11 (4), 239-246, 2000), but these techniques were only applied to data sets that reflected a well-performing factory. This paper presents an approach based on inductive learning in a JIT manufacturing environment that (1) accurately classifies and predicts factory performance based on shop factors, and (2) identifies the important relationships between the shop factors that determine factory performance. An example application is presented in which the classification and regression tree (CART) technique is used to predict saturated, starved or efficient factory performance based on dynamic shop floor data. This means that the relationship between the variables that cause poor factory performance can be discovered and measures to assure efficient performance can then be taken.     

Judicious order acceptance and order release in make-to-order manufacturing systems

This paper explores a two-stage input control system for fixed capacity make-to-order manufacturing systems (with heavy job tardiness penalties), that selectively accepts incoming orders and holds the accepted ones in a pre-shop queue prior to releasing them to the shop floor. Single-stage input control systems that only allow orders to be delayed in a pre-shop queue (i.e. they do not allow some orders to be rejected) have been previously investigated and found to negatively impact overall due-date performance. The hypothesis motivating this research is that judiciously rejecting a subset of incoming orders can prevent the order release queue from being overloaded when a surge of demand occurs. The input control system is evaluated via experiments using a discrete-event simulation model of a fixed capacity manufacturing system. The experiments reported here suggest that holding orders in the pre-shop queue does not improve due date performance, and that judiciously rejecting orders on its own is a viable alternative mechanism of input control that can deliver improved performance.     

Decentralized WIP-oriented manufacturing control (DEWIP)

DEWIP is a manufacturing control system for job shop environments aiming at achieving short and reliable lead times by establishing WIP control loops between the manufacturing work centres. The paper describes the mode of function, the setting of parameters and simulation results of the new manufacturing control system. The setting of parameters is done with the aid of the funnel model and the theory of logistic operating curves, both developed at the Institute of Production Systems at the University of Hanover. The simulation is conducted using industrial data and makes it possible to assess DEWIP with regard to lead times, WIP level, performance and schedule reliability. DEWIP is compared both with an uncontrolled process and with the manufacturing control systems Load oriented order release (LOOR), Conwip and Polca. The results suggest that DEWIP and the models employed for the setting of parameters are suitable for job shop production and therefore offer a valuable alternative to prevailing centralized manufacturing control systems.     

Integrating Kanban principles in a pharmaceutical campaign production system

Centralised planned campaign production is the predominant production system in process industries. In this study, we investigate whether a decentralised Kanban control system, which has proven to offer advantages in other industries, can be successfully integrated in a campaign production environment. The used research methodology combines model-based and case-study-based elements. Using the example of a pharmaceutical internal five-stage supply chain, we conceive a Kanban concept that integrates campaign formation, develop a discrete-event simulation model and conduct a range of explorative simulation experiments. We find that a Kanban campaign production system is not merely feasible but would also be favourable: throughput times can be reduced without increasing customer lead times. Sensitivity analysis shows that the system’s performance is relatively robust to changes in Kanban key configuration parameters such as number of Kanban cards or campaign size. We conclude by discussing our findings and formulating three propositions that might stimulate future research.     

REPETITIVE LOTS: FLOW-TIME REDUCTIONS THROUGH SEQUENCING AND DYNAMIC BATCH SIZING

This paper presents a new integrative concept for job sequencing, dispatching, and lot sizing. The interrelation between these procedures and their impact on flow-time performance is examined in a capacitated production environment. Generally, lot-sizing decisions are made without regard to shop conditions and do not consider their impact on job sequencing procedures. The repetitive lots (RL) concept (developed and tested in this paper) attempts to integrate these decision processes. RL uses a number of features which have not been considered jointly in either the lot-sizing or job-dispatching/sequencing literature. These include operation batch sizes which vary by operation, transfer of work within the shop in quantities less than operation batch size, and the use of overlapped operations. A simulation model is used to analyze flow-time characteristics in a hypothetical production system. Traditional measures of flow-time performance are compared to a set of nontraditional measures which capture the interaction between lot sizing and the sequencing procedure used.     

An Evaluation of Capacity Sensitive Order Review and Release Procedures in Job Shops

We investigate the performance of capacity-sensitive order review and release (ORR) procedures in job shop environments that have not been previously explored. Previous research has ignored the case of job shops which must perform to very tight due-dates because of time-sensitive customers. We propose and test a new capacity sensitive ORR procedure called path based bottleneck (PBB) in such environments, along with the modified infinite loading (MIL) procedure which has been shown to work well in several studies. We compare the performance of these two controlled release rules with that of immediate release rule under different conditions of capacity utilization and customer specified exogenous duedates. Our results indicate that PBB performs well in lowering total costs when due-dates are tight, while MIL is a better procedure with relatively loose to medium due-dates. We also show that in many cases, the shortest processing time (SPT) dispatching rule is a superior performer than a due-date based rule like critical ratio (CR); a conclusion which is contrary to the existing research in this area. In addition, the shop floor control policies recommended are shown to be sensitive to the cost structure of the firm. The managerial implications of this research in providing effective shop floor control in job shops operating under tight due-date conditions are also discussed.     

Dynamic performance of a hybrid inventory system with a Kanban policy in remanufacturing process

In this paper we study a hybrid system with both manufacturing and remanufacturing. The inventory control strategy we use in the manufacturing loop is an automatic pipeline, inventory and order based production control system (APIOBPCS). In the remanufacturing loop we employ a Kanban policy to represent a typical pull system. The methodology adopted uses control theory and simulation. The aim of the research is to analyse the dynamic (as distinct from the static) performance of the specified hybrid system. Dynamics have implications on total costs in terms of inventory holding, capacity utilisation and customer service failures. We analyse the parameter settings to find preferred “nominal”, “fast” and “slow” values in terms of system dynamics performance criteria such as rise time, settling time and overshoot. Based on these parameter settings, we investigate the robustness of the system to changes in return yield and the manufacturing/remanufacturing lead time. Our results clearly show that the system is robust with respect to the system dynamics performance and the remanufacturing process can help to improve system dynamics performance. Thus, the perceived benefits of remanufacturing of products, both environmentally and economically, as quoted in the literature are found not to be detrimental to system dynamics performance when a Kanban policy is used to control the remanufacturing process.     

AN EXPERIMENTAL MODEL FOR INVESTIGATING THE SENSITIVITY OF JOB SHOP PERFORMANCE TO JOB RELEASE TIME DISTRIBUTION PARAMETERS

Work flows in a job shop are influenced by the load per release and time interval between release factors. We focus on the latter factor, job release times. Building on Elvers' work, this study evaluates the impact of different job release time distributions on shop performance. Using a computer simulation of a random job shop and a full factorial experimental design, we demonstrate that the type of distribution does affect performance–a finding consistent with results from job shops characterized by good shop floor control practices. These findings are explained by examining the shape and variance traits of the underlying job release time distributions.     

Tardiness heuristic for scheduling Flexible Manufacturing Systems

Abstract

This paper evaluates the tardiness performance of a sampling-based adaptive heuristic in a dynamic manufacturing environment. A test bed, following a real world manufacturing system, has been developed. The proposed algorithm has been implemented in this simulated environment. After fine tuning the algorithm, it has been tested in various shop conditions. The results of these simulation studies are summarized.     

Due Date Assignment,Job Order Release,and Sequencing Interaction in Job Shop Scheduling*

Depending on the techniques employed, the due date assignment, release, and sequencing procedures in job shop scheduling may depend on one another. This research investigates the effects of these interactions with a simulation model of a dynamic five-machine job shop in which early shipments are prohibited. Performance of the system is measured primarily in terms of the total cost (work-in-process cost, finished goods holding cost, and late penalty) incurred by the shop, but a number of non-cost performance measures are also reported. The results support existence of a three-way interaction between the due date, release, and sequencing procedures as well as interaction between shop utilization and procedure combination. Statistical tests are used to identify those rules that perform best both overall and in combination with other rules.     

Load-oriented manufacturing control just-in-time production for job shops

The paper describes the principles of load-oriented manufacturing control as a new solution for job shops and its successful implementation in a pump-manufacturing factory. The idea of load-oriented manufacturing control is to limit and balance work-in-process inventory on a level as low as possible in order to accomplish a high work-centre utilization as well as a rapid and in-time flow of orders. As the new system keeps actual lead times on a planned level in a self-regulating way, it allows reliable due-date scheduling. Furthermore it points out the bottlenecks and performs applicable mid-term and short-term capacity planning.     

THE IMPACT OF SIMPLE PRIORITY RULE COMBINATIONS ON DELIVERY SPEED AND DELIVERY RELIABILITY IN A HYBRID SHOP

This study examines the effects of using different priority rules at different stages of a multistage, flow-dominant shop. A simulation model is constructed of a manufacturing system comprised of three stages: gateway, intcrmcdiatc, and finishing. As is typical of a flow-dominant shop, the overall flow of the simulated system (gateway to intermediate to finishing) is consistent with a flow shop, but processing in the intermediate stage involves multiple work centers and resembles a job shop. Shop performance is observed when four well-known priority heuristics are applied in different combinations in the gateway, intermediate, and finishing stages of the process. Multiple performance measures addressing the strategic objectives of delivery speed and delivery reliability are recorded under two different shop load conditions. Results show that the measures of both delivery speed and delivery reliability are affected by the priority rule combinations, and that a tradeoff exists between average performance and consistency of performance. Certain priority rule combinations affect performance in predictable ways, allowing the user to assess tradeoffs between delivery speed and delivery reliability.