State-of-the-art and adoption of artificial intelligence in retailing
| Pages | 264-279 |
| DOI | https://doi.org/10.1108/DPRG-09-2018-0050 |
| Published date | 13 May 2019 |
| Date | 13 May 2019 |
| Author | Felix Dominik Weber,Reinhard Schütte |
| Subject Matter | Information & knowledge management,Information management & governance,Information policy |
State-of-the-art and adoption of artificial
intelligence in retailing
Felix Dominik Weber and Reinhard Schütte
Abstract
Purpose –In the most abstract way, artificial intelligence (AI) allows human work to be shifted toward
technological systems that are currently not fully capable. Following this, the domain of retail can be
sketched as a naturalfit for the application of AI tools, whichare known for their high proportion of human
work and concurrent low profit margins. This paper aims to explore the current dissemination of the
application of AI within the industry. The value-added core tasks of retail companies are examined to
determine the possibleutilization and the market adoption within the globallylargest retail companies is
given.
Design/methodology/approach –The paper uses two different approaches to identify the scientific
state-of-the-art: a search on the major scientific databases and an empirical study of the ten largest
internationalretail companies and their adoptionof AI technologies in the domains of wholesaleand retail.
Findings –The application withinthe different value-added core tasks varies greatlydepending on the
area. In summary,there are numerous possible applications in all areas. Especially,in areas where future
forecasts are neededwithin the task areas (such as marketing or replenishment), the use of AI, today,is
both scientifically and practically highly developed. In contrast, the market adoption of AI is highly
variable. The pioneers have integrated extensive applications into everyday business, while the
challengers are investing heavily in new initiatives. Some others, however, show neither active use nor
any effortto adopt such technology.
Originality/value –To the best of the author’sknowledge, this is one of the first research contributionsto
analyzethe areas of application and the impact of AI structured alongthe value-added core processes of
retail companies.
Keywords Retail, Trade, Retailing, Artificial intelligence, Machine learning, Wholesaling
Paper type Research paper
1. Introduction
1.1 Wholesaling and retailing
In an economy based on the division of labor, trade has the task of balancing spatial,
temporal, qualitative and quantitative distances between production and consumption.
Trade comprises the activities of purchasing goods from various manufacturers or
suppliers; transporting, stocking and combining the goods to form an assortment; and
selling them to commercial (wholesaling) or non-commercial (retailing) customers without
the goods being significantly modified or processed. The different kinds of retail can be
differentiated genericallybetween brick-and-mortar retailing (selling from a fixed locationas
a department store, boutique or kiosk),mail and distance selling or online trading.
To reasonably structure the analysis of the purpose and potential relevance for the
wholesale and retail industry, thisarticle focuses on a reference model to structure the main
processes of a retailing company. Its overreaching structure will help to group and
structurally report the findings within a domain relevant structure. The framework proposed
as the reference model to describe a retail task is called the retail information system
architecture shell model (Schu
¨tte, 2017). It contains, from the inside out, the master dataas
Felix Dominik Weber and
Reinhard Schu
¨tte are both
based at Department of
Information Systems,
University of Duisburg-
Essen, Duisburg, NRW,
Germany.
Received 1 September 2018
Revised 3 January 2019
Accepted 8 January 2019
PAGE 264 jDIGITAL POLICY, REGULATION AND GOVERNANCE jVOL. 21 NO. 3 2019, pp. 264-279, ©Emerald Publishing Limited, ISSN 2398-5038 DOI 10.1108/DPRG-09-2018-0050
a core, the technically machine-oriented, value-adding and theadministrative and decision-
oriented tasks of the retail company (Figure1).
As the machine-oriented, administrative and decision-oriented tasks are rather generic and
not elementarily differentbetween retail companies, the following article concentrates on the
value-added core tasks. Following the reference model, the main value-added tasks of
retailing are summarized as managinggoods, ordering goods, serving customers, handing
out goods, transporting goods, making goods available and financial accounting activities
(combining billing goods, accounts payable and receivable and auditing). In accordance
with the initial architecture from Becker andSchu
¨tte (2004), the task areas can be recapped
as including the following components.
Firstly, the management of goods is located in the scientific domain of trade marketing,
which is defined as the processes of analysis, target formulation, strategy selection and
the composition and control of the marketing mix in a trading enterprise (Borden, 1964;
Haller, 2008). The central decisions that have to be made within the scope of trade
marketing encompass the four areas of the marketing mix. The basis is the central
concept of the 4Ps, introduced by McCarthy (1960), which structures the marketing into
four separable (but interlinked) components, namely, “product,” “price,” “place” and
“promotion.”
Figure 1 Retail informationsystem architecture shell model (Schütte, 2017)
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