Demand forecasting under fill rate constraints—The case of re-order points

Bruzda, Joanna

doi:10.1016/j.ijforecast.2020.01.007

Cited by 8 publications

(5 citation statements)

References 47 publications

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“…Though some research highlighted the importance of measuring forecast utility related to inventory performance (see, e.g., in [69,70]), this remains out of the scope of this work.…”

Section: Discussionmentioning

confidence: 99%

Automotive OEM Demand Forecasting: A Comparative Study of Forecasting Algorithms and Strategies

et al. 2021

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Demand forecasting is a crucial component of demand management, directly impacting manufacturing companies’ planning, revenues, and actors through the supply chain. We evaluate 21 baseline, statistical, and machine learning algorithms to forecast smooth and erratic demand on a real-world use case scenario. The products’ data were obtained from a European original equipment manufacturer targeting the global automotive industry market. Our research shows that global machine learning models achieve superior performance than local models. We show that forecast errors from global models can be constrained by pooling product data based on the past demand magnitude. We also propose a set of metrics and criteria for a comprehensive understanding of demand forecasting models’ performance.

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“…Though some research highlighted the importance of measuring forecast utility related to inventory performance (see, e.g., in [69,70]), this remains out of the scope of this work.…”

Section: Discussionmentioning

confidence: 99%

Automotive OEM Demand Forecasting: A Comparative Study of Forecasting Algorithms and Strategies

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Demand forecasting is the best decision to provide a company's future needs [6]. Forecasting, which is one of the fundamental goals of statistical modeling [5], is still imperative in many areas of research and application, including the problem of statistical forecasting of future orders [7], prediction of the performance of hot presses in a multi-effect distillation unit [8], energy demand prediction [9], or tourism demand forecasting [10].…”

Section: Literature Reviewmentioning

confidence: 99%

On the Application of ARIMA and LSTM to Predict Order Demand Based on Short Lead Time and On-Time Delivery Requirements

2021

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Suppliers are adjusting from the order-to-order manufacturing production mode toward demand forecasting. In the meantime, customers have increased demand uncertainty due to their own considerations, such as end-product demand frustration, which leads to suppliers’ inaccurate demand forecasting and inventory wastes. Our research applies ARIMA and LSTM techniques to establish rolling forecast models, which greatly improve accuracy and efficiency of demand and inventory forecasting. The forecast models, developed through historical data, are evaluated and verified by the root mean squares and average absolute error percentages in the actual case application, i.e., the orders of IC trays for semiconductor production plants. The proposed ARIMA and LSTM are superior to the manufacturer’s empirical model prediction results, with LSTM exhibiting enhanced performance in terms of short-term forecasting. The inventory continued to decline significantly after two months of model implementation and application.

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“…We fitted the nine time series models for each of the 426 item using the 52 weeks' observations (t = 1, …, 52). Since procurement decisions on safety stock are entirely contingent on aggregate demand over lead time L (Bruzda, 2020;Cobb, Johnson, Rumi, & Salmeron, 2015;Trapero, Cardos, & Kourentzes, 2019b), Alpha's goal is to predict total demand over lead time (i.e., L = 10 weeks) and ensure that orders placed at the current week would be able to cover the supply deficit after the lead time.…”

Section: Initial Interventionmentioning

confidence: 99%

“…Since the goal of procurement managers is to predict total demand over lead time (L = 10 weeks), the target forecast should be in line with the operational requirement. Week-to-week differences in forecast accuracy within the lead time are not considered as crucial for stocking decisions (Bruzda, 2020;Cobb et al, 2015;Trapero et al, 2019b). The cumulative demand and errors over lead time remains the focal issue for up-to-date forecasting research (Kourentzes, Trapero, & Barrow, 2020;Prak & Teunter, 2019;Trapero et al, 2019a).…”

Section: Problem Reframing-data Aggregationmentioning

confidence: 99%

Cross‐item learning for volatile demand forecasting: An intervention with predictive analytics

Chuang

Chou

Oliva

2021

J of Ops Management

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Despite its importance to OM, demand forecasting has been perceived as a "problem-solving" exercise; most empirical work in the field has focused on explanatory models but neglected prediction problems that are part of empirical science. The present study, involving one of the leading electronics distributors in the world, aims to improve prediction accuracy under high demand volatility for procurement managers to make better inventory decisions. In response to requests for an integrated forecasting methodology, we undertook an iterative process based on three guiding principles-data pooling, theoryinformed feature engineering, and ensemble-based machine learning. The resulting framework managed to improve forecast accuracy significantly and is applicable to a broad range of situations. We present reflections and insights derived abductively through engagement with managers in this problem situation. This "problem-driven" process corresponds to intervention as a research strategy that can foster theoretical and methodological innovations in OM. Our contribution goes beyond the development of the prediction framework as it elucidates ways OM researchers could leverage theoretical foundations to inform feature derivation and model construction. We posit that this work points to a way forward to the combination of OM principles with the emerging innovations in data science and artificial intelligence.

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Demand forecasting under fill rate constraints—The case of re-order points

Cited by 8 publications

References 47 publications

Automotive OEM Demand Forecasting: A Comparative Study of Forecasting Algorithms and Strategies

Automotive OEM Demand Forecasting: A Comparative Study of Forecasting Algorithms and Strategies

On the Application of ARIMA and LSTM to Predict Order Demand Based on Short Lead Time and On-Time Delivery Requirements

Cross‐item learning for volatile demand forecasting: An intervention with predictive analytics

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