A knowledge based approach for automatic process plan generation in an electronic assembly environment

In this paper important issues in the automatic generation of process plans are addressed. Since the conventional method of electronic assembly planning is subjective and depends greatly on the skills, memory, knowledge, and experience of the planners, inconsistencies in the plan arise, which in turn create problems in the manufacturing environment. An automatic electronic assembly planning system that would offer advantages over manual systems has been implemented. Due to frequent advances in technology, the electronic industry is a very dynamic one. This affects the effectiveness of the current process plans and the applicability of the system that generates them. The framework of a system which takes into account the dynamic nature of the environment is presented as an example. A network representation of plans that allows for flexibility and modularity in the planning system is being used. A hybrid architecture consisting of production rules, procedural algorithms and a hierarchical database has been implemented using an expert system shell.     

Optimal integrated maintenance/production policy for randomly failing systems with variable failure rate

This article deals with the combined production and maintenance plans for a manufacturing system satisfying a random demand. We first establish an optimal production plan which minimises the average total inventory and production cost. Second, using this optimal production plan, and taking into account the deterioration of the machine according to its production rate, we derive an optimal maintenance schedule which minimises the maintenance cost. A numerical example illustrates the proposed approach, this analytical approach, based on a stochastic optimisation model and using the operational age concept, reveals the significant influence of the production rate on the deterioration of the manufacturing system and consequently on the integrated production/maintenance policy.     

Integration of process planning and scheduling by exploring the flexibility of process planning

A unique method for the integration of process planning and scheduling in a batch-manufacturing environment is reported. This integration is essential for the optimum use of production resources and for the generation of realistic process plans that can be readily executed with little modification. The integration problem is modelled at two levels: process planning and scheduling, which are linked by an intelligent facilitator. The process-planning module employs an optimization approach in which the whole plan solution space in terms of available machines, tools, tool accessibility and precedence constraints is first generated and a search algorithm is then used to find the optimal plan. For a given set of jobs, the scheduling module takes the optimal plans for each job and generates a schedule based on a given criterion, as well as the performance parameters (machine utilization and number of tardy jobs). An unsatisfied performance parameter is fed back to the facilitator, which then identifies a particular job and issues a change to its process plan solution space. The iteration of process Planning -scheduling-solution space modification continues until a schedule is satisfactory or until no further improvement can be made. The uniqueness of this approach is characterized by the flexibility of the process-planning strategy and the intelligent facilitator, which makes full use of the plan solution space intuitively to reach a satisfactory schedule. (It may not be the optimal, though.) The integrated system was implemented in the manufacturing of prismatic parts. The testing results show that the developed integration method can achieve satisfactory process plans and a schedule in an effective and efficient manner.     

Strategies for planning experiments using orthogonal arrays and confounding tables

Genichi Taguchi has popularized a robust design method which employs experimental design techniques to help identify the levels of design factors to improve the quality of products and manufacturing processes. Experimental design techniques are extremely effective for identifying improved factor levels in problems that involve a large number of factors. Taguchi's success in getting engineers to use experimental design techniques is due, at least in large part, to his use of tools and techniques that simplify the experiment planning process. Recognizing the advantages of this approach, this paper proposes a new set of tools, confounding tables, which offer more guidance to experimenters. Confounding tables provide a clear and systematic representation of confounding relationships. They are simple and useful tools for constructing experiment plans, and they enable users easily to evaluate the confounding patterns of a completed plan. We show how confounding tables provide more information than Taguchi's linear graphs, and are useful for a large class of experiment plans.     

A three-stage simulation based approach to inventory management with discrete demand

This paper proposes a three-stage simulation-based approach to determine target inventory level for a plastic moulding company. The classical formula of a periodic review inventory system requires continuous demand with a normal distribution. This is often not the case in real world companies. For this reason, a three-stage approach based on a SLAM II simulation model is proposed. Our model allows for a statistically based determination of target inventory level for both normal and non-normal distributed demand. This model includes inventory management, production planning and control, finished goods shipment, and functional area of manufacturing. Analysis of simulation results indicates an inventory reduction plan and the feasibility of a layout design to relocate equipment for the plant.     

Optimising replenishment policy for an integrated production inventory deteriorating model considering green component-value design and remanufacturing

This paper develops an integrated deteriorating production inventory model with green component design, remanufacturing and JIT deliveries. We provide a rigorous analysis to derive the number of deliveries, the optimal cycle time of deliveries, and the delivery sise for the integrated buyer-supplier inventory deteriorating model. Distinct from the former concept of average inventory level, our paper proposes a significantly different approach to deal with the first production batch and uses a revised method to approximate the relationship between the supplier's production and storage time. A manufacturing case example of Taiwan computer power-supply component producers is presented to illustrate the theory. It is shown that the parameters of component-value design and unit holding cost are the critical factors affecting the deteriorating inventory planning.     

Applicability of Demand-Driven MRP in a complex manufacturing environment

Push and pull methods have been adopted for specific volume production and uncertainty scenarios in order to plan and control production. The further development of hybrid or integrated methods allows benefit to be drawn from opposing approaches. The literature concerning Demand-Driven Material Requirements Planning (DDMRP) proves its superiority under conditions of internal and external uncertainty for high-volume production compared to the most implemented push method (manufacturing requirements planning MRPII). Companies that have adopted this method, manufacture on average 10–15 parts per product, with 2 or 3 levels of bills of materials. In this paper, we evaluate the applicability of DDMRP in a complex manufacturing environment (e.g. products of four levels of bill of materials) in terms of customer satisfaction and stock levels. Buffered and non-buffered items clustered in seven types of decoupling structures contributed to this complexity. We developed a DDMRP model for planning and execution purposes, which was simulated in ARENA's discrete events software. We analysed the model's on-hand stock and delayed orders. DDMRP works effectively under the manufacturing conditions considered. It is found to prevent inventory stockouts and overstocks, reduce lead time by 41% and reduce stock levels by 18%. The success of this method; however, depends on the strategic positioning of the buffers.     

Manufacturing responsiveness through integrated process planning and scheduling

This paper describes a novel approach to improve manufacturing responsiveness, defined as the ability of a manufacturing system to make a rapid and balanced response to the predictable and unpredictable changes characterizing today's manufacturing environments. The approach aims at creating a dynamic environment that is capable of reacting to factory conditions and which has a significant impact on the reduction of cost, lead time and inventory. The concurrent engineering concept of information sharing to support all the product development phases is employed here. Techniques such as design for manufacture have been applied to the design-process planning interface. Efforts have been made towards eliminating the divisions between process and scheduling activities using the concept of resource elements, which are the exclusive and shared capability boundaries between processing elements in the manufacturing facility. A prototype integration system has been implemented consisting of a knowledge base with facility modelling functions, an intelligent feature-based design system, a featurebased process planning system and a simulation-based scheduling module.     

Dynamic planned safety stocks in supply networks

Safety stocks are commonly used in inventory management for tactically planning against uncertainty in demand and/or supply. The usual approach is to plan a single safety stock value for the entire planning horizon. More advanced methods allow for dynamically updating this value. We introduce a new line of research in inventory management: the notion of planning time-phased safety stocks. We assert that planning a time-phased set of safety stocks over a planning horizon makes sense because larger safety stocks are appropriate in times of greater uncertainty while lower safety stocks are more appropriate when demand and/or supply are more predictable. Projecting a vector of safety stock values is necessary to assure upstream members in the supply network have advanced warning of changes. We perform an empirical study of U.S. industry, which demonstrates that significant savings can be achieved by employing dynamic planned safety stocks, confirming recent case study reports. We provide a simple optimisation model for the problem of minimising inventory given a vector of safety stock targets. We propose a computationally efficient solution procedure and demonstrate its implementation in an MRP/ERP system. We then illustrate an MRP/ERP planning system feature, which employs a dynamic planned safety stock module that supports a production planner by showing the inventory implications of safety stock plans.     

Robust production planning and control for multi-stage systems with flexible final assembly lines

Production planning of final assembly systems is a challenging task, as the often fluctuating order volumes require flexible solutions. Besides, the calculated plans need to be robust against the process-level disturbances and stochastic nature of some parameters like manual processing times or machine availability. In the paper, a simulation-based optimisation method is proposed that utilises lower level shop floor data to calculate robust production plans for final assembly lines of a flexible, multi-stage production system. In order to minimise the idle times when executing the plans, the capacity control that specifies the proper operator–task assignments is also determined. The analysed multi-stage system is operated with a pull strategy, which means that the production at the final assembly lines generates demands for the preceding stages providing the assembled components. In order to guarantee the feasibility of the plans calculated for the final assembly lines, a decomposition approach is proposed to optimise the production plan of preceding stages. By this way, the robust production can be ensured resulting in reduced losses and overall production costs even though the system is exposed to changes and disturbances.     

Aggregate versus disaggregate production planning: a simulated experiment using LDR and MRP

Within the past several years, considerable research has been devoted to the aggregate production planning problem starting with the pioneering work of Holt el al, (1955) and the resulting Linear Decision Rule (LDR). However, researchers have also recognized that developing optimal aggregate production plans, per se, is not sufficient for solving real problems; these plans have to be disaggregated into specific schedules for specific products. Consequently, the thrust of current research is on the ‘disaggregation’ problem. Simultaneously a number of companies have been installing MRP systems with reports of significant improvements in inventory control, production planning, work force scheduling and production costs. This paper reports on an experiment which was designed and conducted to compare the effectiveness of the ‘aggregate-disaggregate’ and MRP approaches to production planning in a simulation environment. LDR was used as the optimal aggregate technique in the aggregate-disaggregate approach. The results appeared to favour the MRP approach     

Hybrid (re)manufacturing: manufacturing and operational implications

Remanufacturing presents an option to recover value from used products. However, hybrid (re)manufacturing (i.e. simultaneous manufacturing and remanufacturing) is a challenge, owing to non-uniform availability and heterogeneous quality of returns. In this paper, we examine how heterogeneous quality and non-uniform quantity of returns influence the optimal production rates and inventory levels in a hybrid (re)manufacturing system that incurs costs to readjust manufacturing and remanufacturing capacities on a temporary basis. Specifically, we propose a mixed integer linear programming (MILP) based approach to obtain the optimal production plan for a specified planning horizon. We apply the proposed model to the (representative) operational data of an office equipment manufacturer to evaluate the impacts of quality of returns, quality-based segregation of returns, and capacity readjustment costs. The study provides insights into the effects of trade-offs among different operational costs in a hybrid (re)manufacturing system.     

Selection of management accounting systems in Just-In-Time and Theory of Constraints-based manufacturing

In today's competitive business environments, a firm's long-term survival rests heavily on its ability to sustain manufacturing superiority over its competitors. To provide the firm with detailed guidelines for sustaining manufacturing superiority, this paper examines the impact of different management accounting systems, manufacturing control systems and time horizon on manufacturing performance in an enterprise resource planning integrated environment. These management accounting systems include traditional costing, activity-based costing and throughput accounting. The manufacturing control systems include Just-In-Time- and Theory of Constraints-based manufacturing. Through a series of simulation experiments, it was found that activity-based costing provided higher short- and long-term profit, better customer service and lower work-in-process inventory than traditional costing and throughput accounting in situations where firms have high overhead costs and relatively low labour and material costs, while carrying ending inventories because of demand uncertainty. Traditional costing also outperformed throughput accounting by exploiting the real-time information sharing capabilities of an enterprise resource planning system. Just-In-Time manufacturing outperformed Theory of Constraints with respect to short- and long-term profitability, customer service, and work-in-process inventory because of differences in buffer inventory policies and sequencing rules. However, time horizon and its interaction with management accounting systems had no impact on the manufacturing performance. In addition, the results suggest that a management accounting system that depicts the manufacturing process tended to provide more accurate product cost information and resulted in a better system performance than the others.     

Dynamic mutual adjustment search for supply chain operations planning co-ordination

Operational planning is an activity carried out by all manufacturing and logistical companies. Its co-ordination with supply chain partners aims at synchronising resources utilisation in order to minimise inefficiencies, such as unnecessary inventory holding, or in order to improve revenue through better resource utilisation. It is a rather complex process as partners have different objectives and information asymmetry is part of any effort to find good co-ordination solutions. Furthermore, because supply chains evolve in a dynamic and uncertain environment, once a co-ordination of operations plans is achieved, input data, such as forecasts or resources’ status, can change and affect on hand plans. These dynamic changes not only require updating the plan that is directly affected by the changes, but it also requires the adjustment of all plans that are part of the same co-ordination solution (Stadtler, H. 2009. A framework for collaborative planning and state-of-the-art. OR Spectrum, 31 (1), 5–30). Therefore, the development of a practical co-ordination approach should be capable of dealing with these dynamic changes. This paper proposes a dynamic mutual adjustment search heuristic, which can be used to co-ordinate the operations plans of two independent supply chain partners, linked by material and non-strategic information flows. Computational analysis shows that the proposed approach produces a win-win strategy in the context of two supply chain partners, and improves the results of upstream planning in each planning cycle, and also improves the fairness of revenue sharing when compared to optimal centralised planning.     

A fuzzy approach to process plan selection

Process planning is the systematic determination of the detailed methods by which parts can be manufactured from raw material to finished product. In a real manufacturing environment, usually several different parts need to be manufactured in a single facility sharing constrained resources. The existence of alternative process plans for each part makes the selection of process plan a very important issue in manufacturing. The objectives in process plan selection might be imprecise and conflicting. In this paper, a fuzzy approach is used to deal quantitatively with the imprecision of the process plan selection problem. Each process plan is evaluated and its contribution to shopfloor performance is calculated using fuzzy set theory. A progressive refinement approach is used to first identify the set of process plans that maximize the contributions, and then consolidate the set to reduce the manufacturing resources needed.     

A hybrid GA-simulation approach to improve JIT systems

This study presents a hybrid approach involving genetic algorithms (GAs) as an optimisation search technique and a simulation model, representing the dynamic behaviour of a system and its limitations, to improve an existing just-in-time (JIT) manufacturing system. To achieve the objective, first, the existing system is modelled and simulated (by considering the system's limitations and its dynamic behaviour). Second, the integrated simulation model is tested and validated by analysis of variance. Third, the hybrid GA-simulation approach is used in an interactive manner to determine the optimal/near-optimal number of kanban cards in different stations of the existing JIT system. The presented hybrid approach is tested and applied to an auto industry production line. Furthermore, it is compared with the practical JIT through analysis of variance (ANOVA) and the results show improvements in the average daily production rate, the average resource utilisation and the average cycle time but some deterioration in the average queue length and in-process inventory is inevitable.     

Modelling and analysis for sequentially optimising production,maintenance and delivery activities taking into account product returns

This paper develops and analyses a stochastic optimisation problem with a service level constraint for generating a sequentially optimal plan of production, maintenance and delivery activities in a deteriorating manufacturing system. Stochastic demand along with product returns are both assumed the latter of which allows for restocking products returned by the customer which are still new and thus in saleable condition. A constrained production/maintenance/delivery problem with service level, stochastic demand, delivery time, failure rate and product returned is formulated based on quadratic model. This quadratic formulation is adapted to provide an inventory, delivery, production and maintenance policies. The objective of this paper is to study the delivery time influence on the planning of the production, maintenance and delivery activities. Finally, we present simulation results to illustrate the exploitation of the proposed approach.     

Revolutionizing supply chain management the theory of constraints way: a case study

This research describes in detail an application of theory of constraints (TOC) and its resulting benefits on the supply chain performance of India's largest lock manufacturing company over a period of seven years. Using TOC's thinking process, the core constraints that had limited the company's performance in the areas of production, distribution, supply group and projects were identified and eliminated. TOC's unique approach helped the company achieve a significant reduction in its finished goods, raw material and work-in-process inventories at various levels across the supply chain. The stock-outs and excess in the distribution system nearly disappeared. The existing lead times saw a drastic reduction while the availability of items increased to nearly 100% despite significant decrease in inventory levels in the supply chain. The inventory turns of the distributors and retailers more than tripled and their profitability increased significantly. The overall sales of the company grew nearly three times during the six years post TOC implementation. TOC's holistic approach helped the company to double its profits and improve its cash position during the Great Recession.     

A function block based approach for increasing adaptability of assembly planning and control

Today's market turbulences cause frequent changes in manufacturing environments. Products diversity, small batch sizes and short life cycles have increased production uncertainties and created a highly dynamic shop floor environment. One essential requirement of such an environment is an adaptive planning and control system that is sufficiently agile to respond to the variety of production requirements and enable easy system reconfiguration at run-time. When developing a product, assembly is a key area that impacts the manufacturing system's responsiveness to the changes. In this research, a framework and a new methodology are introduced to increase the adaptability and autonomy of job-shop assembly process planning and control using function blocks (FBs). A function block is a reusable functional module with an explicit event-driven model, and provides for data flow and finite state automata based control. Event-driven and FB-enabled decision-making is unique in adaptive assembly planning and control. It is explained through an example of a two-robot assembly work cell, where the result of the adaptive planning is wrapped in FBs for execution. The proposed approach has been implemented and simulated using Matlab Simulink in the case study. The simulation demonstrates how this approach would increase the adaptability and responsiveness to changes that may occur regularly in dynamic job-shop assembly operations.     

Aggregate model and decomposition method for mid-term production planning

In this paper a hierarchical decomposition-based approach to the planning of an electronic tube production unit is presented. A mid-term planning level computes a manufacturing plan over a discretized horizon by using a decomposition method for solving large linear programming problems. Then a short-term level schedules the first week objective previously calculated. Special attention is paid to the planning level