Control of Supply Chain Systems by Kanban Mechanism.

This research studies the control mechanism of a supply chain system to operate it efficiently and economically under the just-in-time (JIT) philosophy. To implement a JIT system, kanbans are employed to link different plants\u27 production processes in a supply pipeline. Supply chain models may be categorized into single-stage, multi-stage, and assembly-line types of production systems. In order to operate efficiently and economically, the number of kanbans, the manufacturing batch size, the number of batches, and the total quantity over one period are determined optimally for these types of supply chains. The kanban operation at each stage is scheduled to minimize the total cost in the synchronized logistics of the supply chain. It is difficult to develop a generalized mathematical model for a supply chain system that incorporates all its salient features. This research employs two basic models to describe the supply chain system: a mathematical programming model to minimize the supply chain inventory system cost and a queuing model to configure the kanban logistic operations in the supply pipeline. A supply chain inventory system is modeled as a mixed-integer nonlinear programming (MINLP) that is difficult to solve optimally for a large instance. A branch-and-bound (B&B) method is devised for all versions of it to solve the MINLP problems. From the solution of MINLP, the number of batches in each stage and the total quantity of products are obtained. Next, the number of kanbans that are needed to deliver the batches between two adjacent stages is determined from the results of the MINLP, and kanban operations are fixed to efficiently schedule the dispatches of work-in-process. The new solutions result in a new line configuration as to the number and size of kanbans that led to simpler dispatch schedules, better material handling, reduction in WIP and delivery time, and enhancement of the overall productivity. These models can help a manager respond quickly to consumers\u27 need, determine the right policies to order the raw material and deliver the finished goods, and manage the operations efficiently both within and between the plants

Determining Kanban Size Using Mathematical Programming and Discrete Event Simulation for a Manufacturing System with Large Production Variability

In order to become more competitive and aggressive in the market place it is imperative for manufacturers to reduce cycle time, limit work-in-process, and improve productivity, responsiveness, capacities, and quality. One manner in which supply chains can be improved is via the use of kanbans in a pull production system. Kanbans refer to a card or signal for productions scheduling within just-in-time (JIT) production systems to signal where and what to produce, when to produce it, and how much. A Kanban based JIT production system has been shown to be beneficial to supply chains for they reduce work-in-process, provide real time status of the system, and enhance communication both up and down stream. While many studies exist in regards to determining optimal number of kanbans, types of kanban systems, and other factors related to kanban system performance, no comprehensive model has been developed to determine kanban size in a manufacturing system with variable workforce production rate and variable demand pattern. This study used Stewart-Marchman-Act, a Daytona Beach rehabilitation center for those with mental disabilities or recovering from addiction that has several manufacturing processes, as a test bed sing mathematical programming and discrete event simulation models to determine 2 the Kanban size empirically. Results from the validated simulation model indicated that there would be a significant reduction in cycle time with a kanban system; on average, there would be a decrease in cycle time of nine days (almost two weeks). Results were discussed and limitations of the study were presented in the end

Implementation of Just in Time Production through Kanban System

Uncertainties brought about by fluctuations in demand and customers’ requirements have led many established companies to improve their manufacturing process by adopting the Kanban system. By doing so, they are able to manufacture and supply the right product, in the right quantity, at the right place and time. Implementation of the Kanban system resulted in reduction of inventory to minimum levels besides increasing flexibility of manufacturing. Successful implementation of the Kanban system furthermore reduces operational costs, consequently increases market competitiveness. The Kanban system is basically an inventory stock control system that triggers production signals for product based on actual customers’ requirements and demand. The system is controlled by the Kanban card which dictates the optimum production parameters. It is used to authorize production of any product to replenish those already consumed by the customer or subsequent process. This study covers pre-requisite activities in establishing a Kanban system, starting from designing Kanban flow, gathering manufacturing data, calculating optimum Kanbans in the systems, establishing pull mechanism and rule and finally evaluating Kanban performance using lean parameter. This paper studied the implementation of the Kanban system at a local auto-component company in Malaysia. The scope of implementation was focused at BLM Cylinder Head Cover assembly process. This paper concludes that implementation of the Kanban system reduced lead time, minimized inventory on floor and optimized storage area. The objective of this study is to show that Kanban system improves a manufacturing system as well as achieving Just In Time practice. Keywords: Just In Time, Kanban system, Manufacturing lead time reductio

Scheduling Coordination in a Supply Chain Using Advance Demand Information.

In an environment of mass customization where demand information can be placed in advance with sequencing orders, the question of the best use of this information arises in a supply chain. This situation led the authors to analyze the efficiency of current mechanisms of scheduling coordination when suppliers' processes are not completely reliable. Policies such as periodic replenishment or the kanban system, characterized by a replacement of the items to consume, cannot be exploited effectively with the current rules. This paper presents and justifies new scheduling coordination rules allowing synchronous production in an unreliable environment. This new approach has been benchmarked in the automotive industry as an appropriate method to avoid stockouts and decrease the safety stock.Chaîne logistique; Synchronisation de la production dans une chaîne logistique; kanban; Production synchrone; Point de Pénétration de commande;

A multi-echelon supply chain model for strategic inventory assessment through the deployment of kanbans

Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2008.Includes bibliographical references (leaves 100-102).As global competition in the manufacturing space grows, so do corporations' needs for sophisticated and optimized management systems to enable continuous flows of information and materials across the many tiers within their supply chains. With the complexities introduced by the variability in the demand for finished goods as well as by the variability in lead-time of transportation, procurement, production and administrative activities, corporations have turned to quantitative modeling of their supply chains to address these issues. Based on the data of a heavy machinery manufacturer headquartered in the US, this research introduces a robust model for the deployment of strategic inventory buffers across a multi-echelon manufacturing system. Specifically, this study establishes a replenishment policy for inventory using a multiple bin, or Kanban, system for each part number in the assembly of products from our sponsors tractor line. We employ a numerical simulation to evaluate and optimize the various inventory deployment scenarios. Utilizing several thousand runs of the simulation, we derive a generalized treatment for each part number based on an econometric function of the parameters associated with lead-time, order frequency, inventory value and order costing. The pilot for the simulation focuses on the parts data for three earthmoving products across eight echelons, but scales to n products across m echelons. Our results show that this approach predicted the optimal quantities of Kanbans for 95% of parts to a level of accuracy +/- 3 bins.by Philip J. Hodge and Joshua D. Lemaitre.M.Eng.in Logistic

The just-in-time system and its applicability in South Africa

This thesis discusses the philosophy and techniques of the Japanese Just-in-Time manufacturing system and its applicability in South Africa. The Japanese system consists of two types of procedures and techniques. They pertain to: 1) productivity; (2) quality. The aspect of the system dealing most directly with productivity is known as the just-in-time system. Just-in-Time addresses the material cost component of productivity. The diverse indirect effects are even more pronounced. Just-in-Time partially covers Japanese quality improvements but there are a host of other Japanese quality improvement concepts and procedures. Total quality control describes the set of Japanese quality improvement procedures which in turn encompasses some of the Just-in-Time techniques and improves productivity through the avoidance of waste. The two entities of the Japanese manufacturing system overlap

Analysis of delayed product differentiation under pull type policies

Delayed product differentiation (DPD) increases manufacturers\u27 competitiveness in the market by enabling them to more quickly respond to changes in customers\u27 demands. DPD has also been shown to require less Work-in-Process (WIP) than a non-DPD setup in some cases. Previous research was mainly focused on the level of semi-finished and/or finished good inventory under a base-stock policy. The control of WIP inventory was not considered. DPD may also improve response times under pull inventory control schemes, in which the amount of WIP is controlled directly. These systems can be modeled as closed queueing networks in which a fixed number of kanbans circulate as customers among each set of one or more processing stages.;In this study, we first developed models to analyze the performance of simple kanban and CONstant-WIP (CONWIP) controlled systems and set the number of kanbans to achieve a specified performance level. The models help us better understand the behavior of pull systems. The performance evaluation method uses nonlinear programming (NLP) models to bound the throughput for fixed number of kanbans or minimize the number of kanbans necessary to achieve a specified throughput. The model shows how random supplies and demands prevent equilibrium from occurring in a single-stage kanbans system.;We studied a model for a system of two products with unlimited supply and demand using three CONWIP loops to represent the common processes and the differentiated processes for each product. The same system after DPD has more common processes and fewer differentiated processes. The NLP model can determine numbers of kanbans for each loop to achieve specified throughput targets. Because the throughput bounds are not as tight as desired, we developed a heuristic algorithm that starts from the NLP solution and adjusts the kanbans using simulation to evaluate the performance. A comparison of the result of the heuristic algorithm for the systems with and without DPD indicates that DPD reduces the amount of WIP necessary to achieve a specified throughput. Furthermore, we show how models of systems with similar structure can be generalized

Application of lean scheduling and production control in non-repetitive manufacturing systems using intelligent agent decision support

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Lean Manufacturing (LM) is widely accepted as a world-class manufacturing paradigm, its currency and superiority are manifested in numerous recent success stories. Most lean tools including Just-in-Time (JIT) were designed for repetitive serial production systems. This resulted in a substantial stream of research which dismissed a priori the suitability of LM for non-repetitive non-serial job-shops. The extension of LM into non-repetitive production systems is opposed on the basis of the sheer complexity of applying JIT pull production control in non-repetitive systems fabricating a high variety of products. However, the application of LM in job-shops is not unexplored. Studies proposing the extension of leanness into non-repetitive production systems have promoted the modification of pull control mechanisms or reconfiguration of job-shops into cellular manufacturing systems. This thesis sought to address the shortcomings of the aforementioned approaches. The contribution of this thesis to knowledge in the field of production and operations management is threefold: Firstly, a Multi-Agent System (MAS) is designed to directly apply pull production control to a good approximation of a real-life job-shop. The scale and complexity of the developed MAS prove that the application of pull production control in non-repetitive manufacturing systems is challenging, perplex and laborious. Secondly, the thesis examines three pull production control mechanisms namely, Kanban, Base Stock and Constant Work-in-Process (CONWIP) which it enhances so as to prevent system deadlocks, an issue largely unaddressed in the relevant literature. Having successfully tested the transferability of pull production control to non-repetitive manufacturing, the third contribution of this thesis is that it uses experimental and empirical data to examine the impact of pull production control on job-shop performance. The thesis identifies issues resulting from the application of pull control in job-shops which have implications for industry practice and concludes by outlining further research that can be undertaken in this direction

Supply chain models for an assembly system with preprocessing of raw materials

An assembly line that procures raw materials from outside suppliers and processes the materials into finished products is considered in this research. An ordering policy is proposed for raw materials to meet the requirement of a production facility, which, in turn, must deliver finish products in a fixed quantity at a fixed time interval to the outside buyers. Two different types of raw materials, ‘unfinished’ and ‘ready-to-use’, are procured for the manufacturing system. The ‘unfinished raw materials’ are turned into ‘processed raw materials’ after preprocessing. In the assembly line, the ‘processed raw materials’ and the ‘ready raw materials’ are assembled to convert into the final products. A cost model is developed to aggregate the total costs of raw materials, Work-in-process, and finished goods inventory. Based on the product design and manufacturing requirement a relationship is established between the raw materials and the finished products at different stages of production. A non-linear integer-programming model is developed to determine the optimal ordering policies for procurement of raw materials, and shipment of assembly product, which ultimately minimize the total costs of the model. Numerical examples are presented to demonstrate the solution technique. Sensitivity analysis is performed to show the effects of the parameters on the total cost model. Future research direction is suggested for further improvement of the existing results

Evaluation of production control strategies for the co-ordination of work-authorisations and inventory management in lean supply chains

A decision support framework is proposed for assisting managers and executives to possibly utilise lean production control strategies to coordinate work authorisations and inventory management in supply chains. The framework allows decision makers to evaluate and compare the suitability of various strategies to their system especially when considering conflicting objectives, such as maximising customer service levels while minimising Work in Process (WIP) in a business environment distressed by variabilities and uncertainties in demand stemmed from customer power. Also, the framework provides decision guidance in selecting and testing optimal solutions of selected policies control parameters. The framework is demonstrated by application to a four-node serial supply-chain operating under three different pull-based supply chain strategies; namely CONWIP, Kanban, and Hybrid Kanban-CONWIP and exhibiting low, medium, and high variability in customer demand (i.e., coefficient of variation of 25%, 112.5%, and 200%). The framework consists of three phases; namely Modelling, Optimisation and Decision Support; and is applicable to both Simulation-Based and Metamodel-Based Optimisation. The Modelling phase includes conceptual modelling, discrete event simulation modelling and metamodels development. The Optimisation phase requires the application of multi-criteria optimisation methods to generate WIP-Service Level trade-off curves. The Curvature and Risk Analysis of the trade-off curves are utilised in the Decision Support phase to provide guidance to the decision maker in selecting and testing the best settings for the control parameters of the system. The inflection point of the curvature function indicates the point at which further increases in Service Level are only achievable by incurring an unacceptably higher cost in terms of average WIP. Risk analysis quantifies the risk associated with designing a supply chain system under specific environmental parameters. This research contributes an efficient framework that is applicable to solve real supply chain problems and better understanding of the potential impacts and expected effectiveness of different pull control mechanisms, and offers valuable insights on future research opportunities in this field to production and supply chain managers