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 non-linearity 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.
Highlights
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)
Performances are measured in terms of percentage of tardy jobs and percentage of negotiated due dates; the results demonstrate the superiority of our approach, compared with a set of Workload control (WLC) configurations taken as benchmarks
To estimate the Gross Throughput Time (GTT), we recall that this quantity is defined as the time between order acceptance and order delivery and, when WLC is used to regulate the system, it can be decomposed in the time spent by job j∗ in the PSP and, in the shop floor
Summary
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). Perhaps more important, we stress the importance of the negotiation strategy, which is extended and made more coherent in the present study with a real operating scenario In this regard, we (i) introduce the possibility of negotiating due dates, considering three different scenarios: namely balanced market power between the manufacturer and the customer, manufacturer has more market power, and customer has more market power; (ii) provide both the Standard Push and the Standard WLC systems with two alternative negotiation methods, namely the blind and the selective negotiation, to assess whether the observed benefits are merely due to the negotiation method, or if they are enhanced by a precise estimation of the Gross Throughput Time; (iii) assess performance even when the production capacity of the manufacturing system is almost saturated, a condition that is rather frequent in case of make to order job shops and (iv) introduce the reverse negotiation procedure, to provide the manufacturer with a system capable of reducing the due dates that are very late, if compared with the forecasted Gross Throughput Time.
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