Modelling wholesale distribution operations: an artificial intelligence framework
| Pages | 698-718 |
| DOI | https://doi.org/10.1108/IMDS-04-2018-0164 |
| Published date | 13 May 2019 |
| Date | 13 May 2019 |
| Author | Eleonora Bottani,Piera Centobelli,Mosé Gallo,Mohamad Amin Kaviani,Vipul Jain,Teresa Murino |
| Subject Matter | Information & knowledge management,Information systems,Data management systems,Knowledge management,Knowledge sharing,Management science & operations,Supply chain management,Supply chain information systems,Logistics,Quality management/systems |
Modelling wholesale distribution
operations: an artificial
intelligence framework
Eleonora Bottani
Department of Engineering and Architecture, University of Parma, Parma, Italy
Piera Centobelli
Department of Industrial Engineering, University of Naples Federico II, Naples, Italy
Mosé Gallo
Dipartimento di Ingegneria Chimica, dei Materiali e della Produzione Industriale,
Universitá degli Studi di Napoli Federico II, Napoli, Italy
Mohamad Amin Kaviani
Young Researchers and Elite Club, Shiraz Branch,
Islamic Azad University, Shiraz, Iran
Vipul Jain
Victoria Business School,
Victoria University of Wellington, Wellington, New Zealand, and
Teresa Murino
Department of Chemical, Materials and Industrial Production Engineering,
University of Naples Federico II, Naples, Italy
Abstract
Purpose –The purpose of this paper is to propose an artificial intelligence-based framework to support
decision making in wholesale distribution, with the aim to limit wholesaler out-of-stocks (OOSs) by jointly
formulating price policies and forecasting retailer’s demand.
Design/methodology/approach –The framework is based on the cascade implementation of two artificial
neural networks (ANNs) connected in series. The first ANN is used to derive the selling price of the products
offered by the wholesaler. This represents one of the inputs of the second ANN that is used to anticipate the
retailer’s demand. Both the ANNs make use of several other input parameters and are trained and tested on a
real wholesale supply chain.
Findings –The application of the ANN framework to a real wholesale supply chain shows that the proposed
methodology has the potential to decrease economic loss due to OOS occurrence by more than 56 percent.
Originality/value –The combined use of ANNs is a novelty in supply chain operation management.
Moreover, this approach provides wholesalers with an effective tool to issue purchase orders according to
more dependable demand forecasts.
Keywords Demand forecasting, Supply chain management, Artificial neural networks (ANNs),
Multiple neural networks (MNNs), Price determination, Wholesale distribution
Paper type Research paper
1. Introduction
In the wholesale distribution of consumer goods, the role of a wholesaler is to provide its
customers with a broad assortment of products from different suppliers, which in most
cases are large companies managing business relationships exclusively with their key
distributors (Figure 1). In such a system, the wholesaler renders the service of making
the desired products available for purchase at a specific time, which creates value for the
customer (Ehrenthal et al., 2014). The primary focus of the wholesaler is to maximize
the difference between what retailers will pay and what vendors will accept as payment for
Industrial Management & Data
Systems
Vol. 119 No. 4, 2019
pp. 698-718
© Emerald PublishingLimited
0263-5577
DOI 10.1108/IMDS-04-2018-0164
Received 18 April 2018
Revised 13 August 2018
10 November 2018
Accepted 7 December 2018
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0263-5577.htm
698
IMDS
119,4
the products they offer. The distributors typically have a strong bargaining power, which
enables them to obtain good contractual conditions with industries, with profit margins up
to 6 percent (Holweg et al., 2016; Wyman, 2014).
Defining the amount of product to be kept in stock is a crucial issue for wholesalers, as it
leads to inventory management and purchasing decisions, which, if not managed properly,
can involve dramatic consequences (Fisher, 2009). Inventory management inaccuracies have
a significant negative impact on the performance of all the actors in the supply chain (Sari,
2008). In some cases, buyers order more than the actual demand due to poor order
forecasting, thus issues related to unsaleable products arise (Lee et al., 2006; Holweg et al.,
2016). In other cases, incorrect purchasing decisions can lead to out-of-stock (OOS)
occurrence, which is a serious issue in supply chains (Roland Berger Consultants, 2003;
Supermarket Guru Consumer Panel, 2011). Indeed, although OOS is mainly observed at the
retail stores, supplier related issues contribute to approximately 10–30 percent of OOS
situations (Aastrup and Kotzab, 2009). From an economic perspective, OOS cause lost sales
as an immediate consequence, but in the long-term dissatisfy shoppers, diminish store
loyalty and jeopardize marketing efforts. Therefore, reducing OOSs provides retailers with
the opportunity of increasing sales and reducing cost (Fernie et al., 2010).
Demand forecasting issues were found to cause approximately 47 percent of OOS
situations in retail (Gruen et al., 2002). An accurate forecasting of retailers’demand largely
affects the successful inventory management of the wholesaler, while the inability to match
supply with demand is one of the biggest obstacles to supply chain excellence (Fliedner,
2001; Esper et al., 2010; Stank et al., 2011). The demand forecast is often grounded on
incomplete information and thus tends to underestimate the real demand generating OOSs.
In addition, the demand and sales forecasts invariably differ due to sales variances caused
by OOS occurrence; this means that OOSs, in turn, contribute to an inaccurate demand
forecasting process (Gruen and Corsten, 2007). Conversely, improved demand forecasting
accuracy results in monetary savings, greate r competitiveness, enhanced channel
relationships and customer satisfaction (Moon et al., 2003).
Wholesalers manage two crucial activities that determine the flow of purchased products
from the supplier(s) to the wholesaler and of soldproducts from the wholesalerto the retailer(s),
and are, therefore, closely related to the demand forecasting process and to the occurrence of
OOS situations. These activities are:
(1) the formulation of the price list of products to be sold to the retailers; and
(2) the issuance of purchase orders to its suppliers.
With respectto point (1), when planningtheir product range, wholesalers shoulddecide which
products to offer in each store and at each time, as well as products to be promoted weekly,
including price discounts or special promotions(e.g. a three for two promotion) (Kumar et al.,
2016; Holweg et al., 2016; Willart,2015). The definitionof the selling prices, as well as the choice
of the products to be sold through sales promotions, typically depends on several factors,
includingthe available inventory,purchasing price and product’s turnover rate.As the selling
WholesalerSupplier Retailer
Flow of product
Flow of orders
Final customer
Retailer’s demand
Selling price
Figure 1.
Scheme of
the wholesale
supply chain
699
Modelling
wholesale
distribution
operations
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