Demand forecasts with judgement bias in a newsvendor problem

Abstract

We explore the impact of judgement bias on demand forecast accuracy and profit to identify the driving force of decision-makers to be biased. We study the accuracy-maximising forecasts and the profit-maximising forecasts, which results in the least error of demand forecasts and minimum deviation from the optimal order decision, respectively. Under the assumption that period-to-period demand is independent over time, we find that both types of forecasts are biased. It implies that a newsvendor has the motivation to be biased to obtain either a more accurate demand forecast or a higher profit. Moreover, the decision error under the accuracy-maximising forecasts can be lower than that under unbiased demand forecasts and be bounded by twice of the error under profit-maximising forecasts. It suggests that the biased accuracy-maximising forecasts can perform satisfactorily in both forecasts and decisions. We further relax the assumption of independent demand by considering correlated and trended demand processes, and show the robustness of the positive impact of judgement bias. We then propose a method to solve pure data-driven newsvendor problem and examine its performance with empirical evidence. Our paper contributes to the literature on behavioural operations management by investigating the rationality of judgement bias and its implications.

KEYWORDS:

• Judgement bias
• demand forecasts
• newsvendor problem
• forecast accuracy
• decision error

Acknowledgments

The authors are grateful to the associate editor and two anonymous referees for their constructive comments and suggestions that have led to significantly improvements on the presentation and content of the paper.

Data availability statement

The authors confirm that the data supporting the findings of this study are available within the article or its supplementary materials.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 Here, we use a stable bias $\lambda =0.9$, $\gamma =0.9$ instead of the optimal biased indexes shown in Sections 3 and 4 to deliver a concise calculation. The results of simulation show that even if not applied the optimal bias, the performance of order decision can still be improved by judgement bias.

2 If the demand process follows the mode $D_t=\text{β}t+\tau +{ϵ}_t$, the newsvendor problem is similar with that in Section 5.2. Thus, we omit the discussion and analyse this common-seeing trend.

3 Data source: https://tianchi.aliyun.com/dataset/dataDetail?dataId=45#1.

4 The data from Ali Tianchi does not provide the prices and costs of baby products. Since baby products typically belong to high-profit margin products, we assume that an average price of 150 and cost of 50 are set for the aggregate sales. Then, the safety stock factor is $z={\mathrm{\Phi }}^{-1}\left(2/3\right)=0.4307$.

Additional information

Funding

The research of Qi Fu is supported in part by the University of Macau [grant number MYRG2020-00256-FBA]. The research of Juan Li is supported in part by the National Natural Science Foundation of China under [grant number #72171113]. The research of Lianmin Zhang is supported in part by the National Natural Science Foundation of China under [grant number #72171156].

Notes on contributors

Yini Zheng

Yini Zheng is a PhD student in School of Management and Engineering, Nanjing University. She holds a bachelor degree from Northwest University and a master degree from Nanjing University. Her research interests are supply china management, behaviour operations management, and the interface between Marketing and OM.

Qi Fu

Qi Fu is an associate professor in Faculty of Business Administration, University of Macau. She received her PhD from Hong Kong University of Science and Technology. Her research interests include operations and supply chain management, robust inventory planning, marketing-operations interface. Her work has been published in journals such as Operations Research, Production and Operations Management, European Journal of Operational Research, International Journal of Production Economics.

Juan Li

Juan Li is a professor in the School of Management and Engineering, Nanjing University. She holds a Ph.D. from Shanghai Jiaotong University of China. Her primary areas of research interest include 2021 supply china management, behaviour operations management, and the interface between Marketing and OM. She has published in European Journal of Operational Research, International Journal of Production Economics, Annals of Operations Research, and other journals.

Lianmin Zhang

Lianmin Zhang is a Senior Research Scientist in Shenzhen Research Institute of Big Data. He received his PhD in System Engineering and Engineering Management Science from The Chinese University of Hong Kong. His current research interests include supply chain management and operations research. He has published more than 30 papers in leading journals, such as Production and Operations Management, Transportation Research Part B, Naval Research Logistic, Decision Science and European Journal of Operations Research, and other journals.