Defining accurate delivery dates in make to order job-shops managed by workload control
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Defining accurate delivery dates in make to order job‑shops managed by workload control Davide Mezzogori1 · Giovanni Romagnoli1 · Francesco Zammori1 Accepted: 28 September 2020 © The Author(s) 2020
Abstract Workload control (WLC) is a lean oriented system that reduces queues and waiting times, by imposing a cap to the workload released to the shop floor. Unfortunately, WLC performance does not systematically outperform that of push operating systems, with undersaturated utilizations levels and optimized dispatching rules. To address this issue, many scientific works made use of complex job-release mechanisms and sophisticated dispatching rules, but this makes WLC too complicated for industrial applications. So, in this study, we propose a complementary approach. At first, to reduce queuing time variability, we introduce a simple WLC system; next we integrate it with a predictive tool that, based on the system state, can accurately forecast the total time needed to manufacture and deliver a job. Due to the nonlinearity among dependent and independent variables, forecasts are made using a multi-layer-perceptron; yet, to have a comparison, the effectiveness of both linear and non-linear multi regression model has been tested too. Anyhow, if due dates are endogenous (i.e. set by the manufacturer), they can be directly bound to this internal estimate. Conversely, if they are exogenous (i.e. set by the customer), this approach may not be enough to minimize the percentage of tardy jobs. So, we also propose a negotiation scheme, which can be used to extend exogenous due dates considered too tight, with respect to the internal estimate. This is the main contribution of the paper, as it makes the forecasting approach truly useful in many industrial applications. To test our approach, we simulated a 6-machines job-shop controlled with WLC and equipped with the proposed forecasting system. Obtained performances, namely WIP levels, percentage of tardy jobs and negotiated due dates, were compared with those of a set classical benchmark, and demonstrated the robustness and the quality of our approach, which ensures minimal delays. Keywords Delivery dates · Discrete event simulation · Job-shop · Workload control · Regression · Neural network * Francesco Zammori [email protected] 1
Department of Engineering and Architecture, University of Parma, Viale G.P. Usberti, 181/A, 43124 Parma, Italy
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1 Introduction Nowadays, the successful application of lean manufacturing across industries of various sectors and with different characteristics, has reinforced the claim that lean is a universal production system that can bring a permanent competitive edge (Yadav et al. 2019). Especially in manufacturing and logistics, lean can help industrial practitioners to increase operational performance by developing a waste-free value stream, where jobs flow continuously from a value-added activity to the following one. The focus is on waste identification and removal and, in this regar
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