Development of Prototype Scheduling and Sequencing Software for Job Shop Manufacturing in Sheet Metal Fabrication

A software program has been developed to ease the process of scheduling and sequencing number of jobs to certain number of machines for job shop manufacturing in sheet metal fabrication. The program is designed based on the present operation at Technology Park Malaysia (TPM) - Production Engineering using available priority dispatching rules and multiple performance measures. For n by m job shop scheduling problems, where n is the number of jobs and m is the number machines, there are (n!)m possible schedules. In a typical job shop, hundreds of scheduling decisions must be made daily. Scheduling process, which is to organise, maintain, update and reschedule the job, is very tedious work and time consuming. For five jobs passing through one machine, there are 120 time charts just to show all possible sequence patterns. To plot the charts manually is not. a practical solution and ridiculous. Identifying the performance measures to be used in selecting the schedule is important. The schedule should reflect managerially acceptable performance measures. A logical strategy is thus to pursue methods that can consistently generate good schedules with quantifiable quality in a computationally efficient manner

Quick cost estimation model for early order stage

On time quotation and accurate cost estimate are two important factors influencing the ability of a company to sustain its business operation. Failure to fulfill requests for quotation can severely effect its business survival. Late and uncompetitive quotations will result in lost of customers. It is vital for manufacturing companies to generate cost estimates quickly and accurately otherwise they might either loose their business or not make sufficient profit. Cost estimation process requires good knowledge in the structure of the part and its manufacturing processes. Cost estimation can be done with a high degree of accuracy but it takes too long and if it is done in a short period of time the accuracy is usually quite poor. Generating detailed process plan requires a substantial amount of time and data. Unfortunately, not all necessary information is available during the costing stage. This paper shows the possibility of developing part cost estimate without having to construct its detail process plan. It presents a model for estimating part process cost which allows a cost estimate to be generated quickly with certain degree of accuracy by utilizing parametric techniques and part feature representation

Development of early order stage cost estimation models for sheet metal component

Timely submission of an accurate cost estimate is vital to the survival of manufacturing support group companies. Late submission of quotation and uncompetitive cost estimate result in lost of customers while an underestimate quotation may reduce company’s profit and usually leads to problems later in the production process. Short response time and lack of necessary technical information are the main difficulties frequently faced during the early product cost estimation. This study develops mathematical models to estimate process cost of sheet metal components at early ordering stage from collection of intuitive cost data and machine operation time. Multivariate statistical analysis is carried out to examine the relative importance of part feature representation extracted from engineering part drawing and to establish the significance relationship between the part features and the unit part process cost. This study introduces a systematic statistical framework for modeling the development of cost estimation for sheet metal components. An analytical classification approach in identifying critical factors is also established. The results of analysis illustrate that number of standard holes, bounding box perimeter, inner curved cut-out length, number of sharp edges, number of bend lines and inner straight cut-out length are among the most significant factors that influence the unit part processing cost of sheet metal components. The stability and reliability of the developed models are tested with an independent dataset using Mean Absolute Deviation and Mean Percent Absolute Deviation as main performance indicators. Cost model developed from machine operation time shows the ability to provide an estimation of part process cost within an acceptable range of 12% accuracy while cost models developed from quoted price shows a more narrowed results within 9.0% accuracy. Both the statistical time-based and price-based models provide a better estimation results compared to estimation cost produced by the company’s estimators generated based on their intuitive knowledge and experience. The statistical quality of the established mathematical models proves that the methodology developed in the study is capable to generate acceptable mathematical models for estimating early part process cost within certain degree of accuracy. The models which utilize parametric techniques provide an acceptable alternative solution to estimate process cost for manufacturing sheet metal components at early order stage in the absence of complete technical part information