Using information flow model to specify the Kanban-controlled shopfloor control system

The implementation of Kanban-based production control systems may be difficult in make-to-order environments such as job shops. The flexible manufacturing approach constitutes a promising solution to adapt the Kanban method to such environments. This paper presents an information flow modelling approach for specifying the operational planning and control functions of the Kanban-controlled shopfloor control system (KSCS) in a flexible manufacturing environment. By decomposing the KSCS control functionalities, we have created the system information flow model through the data flow diagrams of Structured Systems Analysis Methodology. The data flow diagrams serve effective system specifications for communicating the system operations to participants of different disciplines as well as the system model for the design and development of KSCS.

Kanban devs modelling, simulation and verification

Kanban Control Systems (KCS) have become a widely accepted form of inventory and production control. The creation of realistic Discrete Events Simulation (DES) models of KCS require specification of both information and material flow. There are several commercially available simulation packages that are able to model these systems although the use of an application specific modelling language provides means for rapid model development. A new Kanban specific simulation language as well as a high-speed execution engine is verified in this paper through the simulation of a single stage single part type production line. A single stage single part KCS is modelled with exhaustive enumeration of the decision variables of container sizes and number of Kanbans. Several performance measures were used; 95% Confidence Interval (CI) of container Flow Time (FT), mean line throughput as well as the Coefficient of Variance (CV) of FT and Cycle Time were used to determine the robustness of the control system.

Intelligent kanban : evaluation of a supply chain MAS application using benchmarking

The real world challenges of incomplete information access and bounded computational resources in supply chain management motivate us to propose a bottom up approach to supply chain intelligence, built over a widely used reactive card-based replenishments system (kanban). The rationale is to use agent technology to improve the performance of the traditional kanban system while maintaining its recognized usability. Instead of optimizing a system utility function, we encode the system goal in desired behaviours of individual agents that reason about their own behaviours in the local context. This paper discusses a rigorous framework for evaluation of the proposal based on the concept of benchmarking. Preliminary results from these simulations show remarkable improvements over the traditional system. Furthermore, use of the benchmarking framework gives confidence that these results translate into real performance gains in practical implementations.

Experiments toward a practical implementation of an intelligent kanban system

This paper presents laboratory experiments to test a bottom up approach to production control and supply chain management. Built upon the successful traditional kanban (Card) system, the new intelligent system associates a kanban agent to each physical kanban. Instead of relying on demand forecast and planning, kanban agents reason about their own movements to adapt to changing demands. After previous simulations results of the intelligent system showed significant performance improvements over the traditional system, we further use the Auto-ID Laboratory at Cambridge University to test the feasibility of the idea in a realistic manufacturing environment. The results from the experiments demonstrated the superiority on several performance measures of the intelligent system compared to the traditional system used as a benchmark. Moreover, the implementation of the experiments exposed several real world constraints not shown in the simulation study and practical solutions were adopted to address these.

Simulating the change from a push to pull production system

This paper will document the initial discrete-event simulation performed to study a proposed change from a push to a pull system in an existing manufacturing company. The system is characterised by five machine lines with intermediate buffers, and five major part groupings. A simulation model has been developed to mimic the flow of kanban cards in the physical system, by using a series of requests that propagate back through the facility, which the machines must respond to. The customer
demand therefore controls the level of activity in the plant. The results of the initial modelling steps will be presented in this paper, especially the impact of kanban lot size and demand variability on the output and stability of the production system, from which a set of future work is proposed.

Application of co-operative agents in handling fluctuations in a pull production system

This paper presents the general details of the structure and strategy of a multi-agent system that is being developed to improve the performance of pull (kanban) production control to handle large fluctuations in product demand. Employing a set of generic, heterarchial agents each controlling a single product and co-operating together to ensure that all components, regardless of demand fluctuation, are manufactured on time as per basic kanban principles. Preliminary results indicate that the basic kanban model does not cater for large demand fluctuations and the application of this multi-agent strategy may be beneficial to improving the overall system performance and increase the likelihood that all products will be manufactured on time.

Agent-based heuristics to solve NP-hard scheduling problems

A novel agent-driven heuristic approach was developed to control the operational scheduling for a local manufacturer. This approach outperformed the traditional kanban control mechanism under numerous simulated benchmarking tests. Using this approach, the individual machine loading was reduced by, on average, 28%, with the loading spread reduced by 85%