Tom van Woensel, Karel van Donselaar, Rob Broekmeulen and Jan Fransoo
This paper aims to identify customer behavior with regard to out‐of‐stocks (OOS) of perishable products (focused on bakery bread) and the resulting inventory performance for these…
Abstract
Purpose
This paper aims to identify customer behavior with regard to out‐of‐stocks (OOS) of perishable products (focused on bakery bread) and the resulting inventory performance for these perishable products.
Design/methodology/approach
Insights on how consumers behave when their preferred bread product is OOS are derived based on 3,800 customer interviews performed in three stores of a large Dutch grocery retail chain. Next to this, additional logistical information was measured on regular moments with respect to the shelf availability per stock‐keeping unit during the day and to waste at the end of the day.
Findings
The customer behavior with regard to perishables is observed to be different from that for the non‐perishable items. The key observation is that customers have a high willingness to substitute. The incorporation of the obtained knowledge of the observed consumer buying behavior into the existing automated store ordering (ASO) systems is discussed. In the current ASO systems, no distinction is made between perishable and non‐perishable products, as it is primarily designed and used for the non‐perishables. The authors show that the current ASO can be enriched and extended by taking into account some extra crucial parameters which are based on the observed consumer behavior.
Originality/value
One common factor in the research papers published so far is that they primarily looked into the customer behavior for non‐perishable items. The current paper on‐hand extends these works towards perishable items with a focus on bakery bread.
Details
Keywords
The rapid development of e-commerce has brought not only great convenience to people but a great challenge to online stores. Phenomenon such as out of stock and slow sales has…
Abstract
Purpose
The rapid development of e-commerce has brought not only great convenience to people but a great challenge to online stores. Phenomenon such as out of stock and slow sales has been common in recent years. These issues can be managed only when the occurrence of the sales volume is predicted in advance, and sufficient warnings can be executed in time. Thus, keeping in mind the importance of the sales prediction system, the purpose of this paper is to propose an effective sales prediction model and make digital marketing strategies with the machine learning model.
Design/methodology/approach
Based on the consumer purchasing behavior decision theory, we discuss the factors affecting product sales, including external factors, consumer perception, consumer potential purchase behavior and consumer traffic. Then we propose a sales prediction model, M-GNA-XGBOOST, using the time-series prediction that ensures the effective prediction of sales about each product in a short time on online stores based on the sales data in the previous term or month or year. The proposed M-GNA-XGBOOST model serves as an adaptive prediction model, for which the instant factors and the sales data of the previous period are the input, and the optimal computation is based on the proposed methodology. The adaptive prediction using the proposed model is developed based on the LSTM (Long Short-Term Memory), GAN (Generative Adversarial Networks) and XGBOOST (eXtreme Gradient Boosting). The model inherits the advantages among the algorithms with better accuracy and forecasts the sales of each product in the store with instant data characteristics for the first time.
Findings
The analysis using Jingdong dataset proves the effectiveness of the proposed prediction method. The effectiveness of the proposed method is enhanced and the accuracy that instant data as input is found to be better compared with the model that lagged data as input. The root means squared error and mean absolute error of the proposed model are found to be around 11.9 and 8.23. According to the sales prediction of each product, the resource can be arranged in advance, and the marketing strategy of product positioning, product display optimization, inventory management and product promotion is designed for online stores.
Originality/value
The paper proposes and implements a new model, M-GNA-XGBOOST, to predict sales of each product for online stores. Our work provides reference and enlightenment for the establishment of accurate sales-based digital marketing strategies for online stores.
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Keywords
Sebastian Brockhaus, Daniel Taylor, A. Michael Knemeyer and Paul R. Murphy
This research explores the concept of omnichannel fulfillment steering (OFS) and demonstrates how retailers can influence a consumer’s fulfillment decisions through commonly used…
Abstract
Purpose
This research explores the concept of omnichannel fulfillment steering (OFS) and demonstrates how retailers can influence a consumer’s fulfillment decisions through commonly used financial incentives such as discounts, credits and the opportunity to avoid home delivery fees.
Design/methodology/approach
We present insights from two theoretically grounded experiments to examine how different types of financial incentives can be used by omnichannel retailers to steer consumers from home delivery toward three alternative order fulfillment methods (AOFM) – buy-online-pickup-in-store, curbside-pickup and ship-to-locker.
Findings
Our analysis suggests that an opportunity to avoid shipping fees (penalty-avoidance) is a more effective OFS nudge than offering discounts or store credits (rewards). No difference was observed between offering discounts or credits as steering mechanisms; further, no omnichannel steering benefits were observed among the tested AOFMs. Collectively, these findings provide possible justification for retailers’ prioritization of credits to foster customer in-store visits, thus encouraging greater customer engagement and facilitating cross-selling opportunities. Finally, we uncover a penalty-avoidance endowment effect for “free shipping” of purchases over the current industry-standard free shipping threshold.
Practical implications
Retailers might prioritize store credits over discounts as nudges to steer customers toward an AOFM, with buy-online-pickup-in-store offering the greatest benefits for most retailers. Furthermore, using penalty-avoidance OFS incentives over a typical free shipping threshold may increase AOFM selection rates but engender adverse customer reactions.
Originality/value
Advancing the concept of OFS, this study directly informs retailers’ omnichannel incentive programs to nudge customers back into the store. Countering intertemporal choice theory, we could not demonstrate that delayed incentives are less effective than immediate ones. Based on prospect theory, we identify a free shipping endowment effect at a specific reference point along a purchase amount continuum.
Details
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Matias Escuder, Martin Tanco, Andres Muñoz-Villamizar and Javier Santos
Urban logistics presents a series of challenges, as the interests of the different stakeholders are not always aligned. The aim of this research is to explore the potential of…
Abstract
Purpose
Urban logistics presents a series of challenges, as the interests of the different stakeholders are not always aligned. The aim of this research is to explore the potential of applying Lean principles to reduce waste in urban logistics.
Design/methodology/approach
As a structure for “going to gemba,” the authors implemented the shadowing technique to better understand the perspective of companies distributing products in the city of Montevideo, Uruguay. Then, meetings were conducted to validate the observations by the people shadowed.
Findings
The results show that most of shipper's time is dedicated to waiting (59%), which is followed by driving (22%), and only a small section of time was dedicated to unloading and verification activities (19%). Although collaborative solutions are needed along with the different stakeholders, this research highlights how deploying Lean thinking can improve significantly urban logistics achieving up to 25% improvement in the number of stores served per shift.
Practical implications
From an academic point of view, this study emphasizes the importance of continue applying and evaluating the Lean practices into transportation contexts. From a company's perspective, the authors have presented a list of propositions that can be implemented for carriers in order to reduce waste and/or improve the efficiency of the urban transportation process.
Originality/value
Based on the literature review carried out, the subject study of Lean and its application to urban logistics remains mostly unexplored in the scientific literature.