Sanchoy K. Das

Modeling the flexibility of order quantities and lead-times in supply chains

Abstract The underlying assumption of a good supply chain is that buyers and suppliers are willing to accommodate the uncertainties and variations in each others businesses. We define supply chain flexibility as the robustness of the buyer–supplier relationship under changing supply conditions. This flexibility provides an effective parameter for characterizing the behavior of asynchronous supply chains. A highly flexible relationship is one in which there is little deterioration in the procurement price under different supply conditions. In this paper we introduce a measure for estimating supply chain flexibility as a function of varying order quantities and varying supply lead-times. Our survey indicates that order quantities and supply lead-times are the two most common changes which occur in supply chains, and are most often the cause of buyer–supplier grievance. Since buyers are not always able to predict downstream conditions, they will often issue procurement orders that are for a smaller quantity than normal, and/or shorter supply lead-time than normal. In an inflexible relationship a supplier will only accept these orders at a much higher unit price. Using the proposed model a buyer is able to estimate the flexibility of potential supply chain partners, and hence make a quantifiable choice. The measure itself can be specified in the supply chain contract. Further, in conjunction with a parametric representation of the buyers procurement behavior, the model is able to estimate the annual procurement cost of a given relationship.

An approach for estimating the end-of-life product disassembly effort and cost

Disassembly is the process of physically separating a product into its parts or subassembly pieces. The overall economics of the disassembly process, and in particular the cost to disassemble, is still not well understood. In this paper our goal is to introduce a methodology that will support and facilitate the economic analysis of the disassembly activity. We present a multi-factor model to compute the disassembly effort index (DEI) score, which is representative of the total operating cost to disassemble a product. The DEI score can then be compared against the projected market value of the disassembled parts and subassemblies to get an economic measure. To develop the DEI model we surveyed a variety of commercial disassembly facilities. Based on these surveys we propose a multifactor weighted estimation scheme. The seven factors are (i) time, (ii) tools, (iii) fixture, (iv) access, (v) instruct, (vi) hazard, and (vii) force requirements. The DEI scale is defined in the 0 to 100 range. This range is assigned on a weighted basis to each of the seven factors. For each factor, an independent utility scale is formulated, using the assigned range as anchors. Using a conversion scale the DEI score is used to derive an estimate of disassembly cost and the disassembly return on investment. An example is presented.

The measurement of flexibility in manufacturing systems

This article provides a theoretical basis for measuring the flexibility of manufacturing systems. The concept of multiple levels of measures (necessary, capability, actual, inflexibility, and optimality) for each flexibility type is introduced. Capability and actual measures are then developed for machine, routing, process, product, and volume flexibilities. For each of these flexibility types, a state defining variable is identified. A measure of flexibility is then derived by computing either, (i) the change effort expended in moving between states, (ii) the drop in system performance in moving between states, (iii) a general or physical scale of difference between two successive states, or (iv) a measure combining all three. The use of the developed measures is illustrated via a two-facility example.

A facility layout method for flexible manufacturing systems

Abstract This facility layout of a flexible manufacturing system (FMS) involves the positioning of cells within given boundaries, so as to minimize the total projected travel time between cells. Defining the layout includes specifying the spatial coordinates of each cell, its orientation in either a horizontal or vertical position, and the location of its load/unload point. We refer to this problem as the FMS facility layout problem (FLP). In this paper we present a four-step heuristic methodology for solving the FLP. This heuristic combines variable partitioning and integer programming methods to generate an open field type of layout.

Investigations into the impact of flexibility on manufacturing performance

This paper provides investigative insights into the impact of routeing flexibility, machine flexibility, and product-mix flexibility on the performance of a manufacturing plant. The study employs simulation modelling as the primary tool. The facility modelled is an automobile engine assembly plant consisting of a FMS (flexible manufacturing systems), job shop, and assembly line. A variety of experiments, with the FMS exhibiting one or more of the above three flexibilities at different levels, were simulated on the model. In each experiment the manufacturing performance as given by flow time and work-in-process inventory was tracked. The experiments focused first on the FMS itself, and then on the entire plant. Measures for the three flexibilities are introduced. The simulation results are analysed in detail. The results indicate significant performance benefits in context of the FMS, but little in context of the overall plant

Process planning for product disassembly

A disassembly plan is described by a disassembly bill of materials (DBOM), the sequence of processing steps, the type of disassembly action, the part or fastener worked on each step, the tools used, and the resulting material and part outputs. The disassembly process planning problem (DP3) involves the generation of a feasible plan and its implementation at a disassembly facility. In this paper, we introduce and describe the DP3 model. This model includes a structured format for creating, documenting, and evaluating a disassembly process plan. The resulting plan is intended to be a readily implemented solution and hence can be adopted by any reclamation facility. A key utility of this model is that it provides a format for transmitting product knowledge from the original product manufacturer to the consumer and the end-of-life disassembler, via the disassembly bill of materials (DBOM). The DP3 is a descriptive model, in that it describes a plan that can be readily developed by a manufacturer and efficiently distributed to the disassembly community. It is left to the user though, to determine the sequence of disassembly steps. The model introduces a variety of standards for identifying unfastening actions, destructive actions, and the required tools. The DP3 model also provides an economic evaluation of different plans.

A genetic algorithm based heuristic for scheduling of virtual manufacturing cells (VMCs)

We present a genetic algorithm (GA) based heuristic approach for job scheduling in virtual manufacturing cells (VMCs). In a VMC, machines are dedicated to a part as in a regular cell, but machines are not physically relocated in a contiguous area. Cell configurations are therefore temporary, and assignments are made to optimize the scheduling objective under changing demand conditions. We consider the case where there are multiple jobs with different processing routes. There are multiple machine types with several identical machines in each type and are located in different locations in the shop floor. Scheduling objective is weighted makespan and total traveling distance minimization. The scheduling decisions are the (i) assignment of jobs to the machines, and (ii) the job start time at each machine. To evaluate the effectiveness of the GA heuristic we compare it with a mixed integer programming (MIP) solution. This is done on a wide range of benchmark problem. Computational results show that GA is promising in finding good solutions in very shorter times and can be substituted in the place of MIP model.

Selection of routes in a flexible manufacturing facility

Abstract Routeing flexibility is defined as the ability to manufacture a product via several alternate routes in the same facility. A route is defined as the set of work centers or machines through which a product is processed, and the processing times at these work centers. Routeing flexibility permits the facility to adjust to changes in the production ratio. Typically, with every change in the production ratio, the FMS scheduler will assign routes to each product, so as to achieve a close to perfect load balance between work centers while minimizing the interference or setups between processing routes. The assigned routes will belong to the set of routes that have been implemented, and hence are available for scheduling. A route is described as implemented when the instructions, skills, tools, handling needs, and other auxiliaries associated with it have been positioned in the facility. The ability to change routes and optimize performance is therefore dependent on the routes which have been implemented. In this paper we assume that several good and feasible routes are generated by the process planner for each product. Since it is economically possible to implement only a few routes per product, the route selection problem is described as identifying the set of routes which should be implemented so as to optimize routeing flexibility related costs. The problem is formulated as a mixed integer program and rules for deriving tight initial solutions are proposed. Tests with sample problems were conducted, and model sensitivity to the scenario change factor and present worth factor were studied.

Design and implementation of flexible manufacturing solutions in agile enterprises

Flexibility is a key component in any agile manufacturing enterprise. A methodology that a firm may use to design, build and then implement a flexible manufacturing (FM) solution is presented. An FM solution is defined as an operational intervention that helps the company counter the changes in its internal and external environments. The methodology was developed in collaboration with several industrial partners, and is easy to use and readily applicable in an industrial setting. The FM solution design method is structured as a three‐phase execution. Phase I involves identifying the flexibility needs of the company. Phase II is the actual development of the FM solution and includes models for measuring the current and target flexibility levels. Phase III incorporates a process to address the economic viability of the proposed solutions. Also reported are the results of a survey on the relative importance of the flexibility types.

Evaluating the unfastening effort in design for disassembly and serviceability

Disassembly is the process of physically separating a product into its parts or subassemblies. Recently, product designers are being challenged to address the concept of ‘ease of disassembly’ while configuring new designs. This is driven by the need for new products to undergo a design for disassembly and serviceability (DfDS) analysis. DfDS promotes design features and attributes, which reduce the subsequent disassembly costs. The disassembly process commonly involves an unfastening action. In this paper we present the unfastening effort analysis (U-effort) model, which helps designers to evaluate and select their fastener options. The U-effort model was developed from an experimental investigation of the most common fastener types used in industry. For each fastener type, the U-effort model identifies several causal attributes, and uses these to derive the U-effort index for a given case. From our experiments, we found that the most significant causal attributes are usually related to fastener size, shape or operational characteristics. The U-effort model is easily integrated into DfDS analysis schemes. The disassembly times generated from the U-effort model can be used to perform economic analysis of product service and/or end-of-life disassembly operations.