Trade is responsible for balancing the spatial, temporal, qualitative and quantitative distances between production and consumption in every economy based on the division of labor. Trade includes the activities of purchasing goods from different manufacturers or suppliers, transporting, storing and combining the goods to form an assortment, and selling them to commercial (wholesale) or non-commercial (retail) customers without significant modification or processing of the goods. The various types of retail can generally be differentiated between brick and mortar retailing (selling from a fixed location such as a department store, shop or kiosk), distance selling (and mailing) or online retail. In order to structure the analysis of the purpose and potential relevance for the wholesale and retail industries, this article focuses on a reference model to structure a retailer’s main processes. This overreaching structure will help to group and report the findings structurally within a domain-relevant model. The framework proposed as a reference model to describe a retail task is called the shell model of retail information systems [1
]. It contains the master data as a core, the technically engineered, value-adding core and the administrative and decision-making tasks of the retail company from the inside (see Figure 1
Since machine-oriented, administrative and decision-making tasks are generic and not primarily different among retail companies, the following article focuses on the core value-added tasks. The reference model summarizes the main value-added retailing tasks according to the shell model as managing goods, ordering goods, serving customers, distributing goods, transporting goods, distributing goods and financial accounting (combining billing goods, accounts payable/receivable and auditing). In accordance with the original architecture [2
], the task areas can be recapped as the following components:
First, the management of goods is located in the scientific field of trade marketing, which is defined as analytical processes, target formulation, strategy selection and the composition and control of the marketing mix in a trading company [3
]. The four areas of the marketing mix are central to the decisions to be taken within the scope of trade marketing. The basic concept of the 4Ps, introduced by McCarthy [5
], structures the marketing into four separate components: product, price, location and promotion.
The ordering of goods includes all activities relating to the replenishment of the shops, the filling of the shelves and the reaction to customer requirements. This includes the processes between central warehouses and stores, between suppliers and warehouses, and between suppliers and stores (if directly supplied) depending on the type of trading company. Serving customers includes tasks that are intuitively attributed to trade, such as sales advice and the actual core activities at the cash desks in the store. The operational tasks include the initiation, execution and downstream processes of a transaction (customer service and complaint management).
The transport of goods and logistics includes all tasks related to the storage of goods. This includes any functions related to the creation or management of the warehouse structure for the transfer and management of the warehouse in general—like the management of storage locations and optimization of shelf space. It also covers the operational tasks between central warehouses in the individual stores, from the manufacturer to the direct goods stores, and the coordination of these activities. The delivery of goods includes tasks involving the fulfillment of the order according to the agreed quantity, quality and time.
Making goods available and the receipt of goods is the functional field of the planning, acceptance, control, return and physical storage of goods and the parallel execution of these processes.
Financial accounting activities cover all operational activities, such as invoice entry, invoice verification, deviation control, post-processing invoices and subsequent remuneration settlement.
1.2. Artificial Intelligence (AI) and Machine Learning (ML)
All efforts within the domain of information technologies, independently of an academically motivated and enforced separation of the research fields, have followed the assumption and goal of the transfer of task to be overtaken by machines in the last decades. Artificial intelligence (AI) was born out of the considerations regarding the extent to which the machine can partially or completely replace humans in the performance of tasks. Following McCarthy et al. [6
], artificial intelligence tries to figure out how to get machines to use language, to form abstractions and concepts, to solve types of problems that are currently reserved for humans, and to improve themselves. In addition, it is pointed out here that the ideas of [6
] are pursued in order to evaluate the use of in retail: AI is the science that enables machines problem types and tasks that cannot yet be performed by computers and in which people are currently better [9
]. In this paper it will neither be claimed nor necessary to fully discuss the concept of AI or to deal with philosophical thoughts about intelligence itself [7
]. Machine learning (ML), as a subdiscipline of the field of AI, uses techniques for learning from examples, test data, or large data sets to make predictions afterwards. This means that the examples are not simply emulated, but patterns and laws from the data are recognized. After this short introduction to AI and ML, the next step is to evaluate the areas of application and the possible impact especially of ML on the retail sector. Here, we mainly concentrate on the underling business task that are subject to the transfor towards the machine. The retail sector is characterized by an oligopolistic market with strong intra-competition between existing retailers and rising inter-competition between traditional and new “pure” digital players in many countries around the world [1
]. With Amazon Fresh about to enter the grocery market, this competition is intensifying. The increased competition, a fading scope for differentiation between operating types [10
], increased costs, the overall increased in price knowledge on the customer’s side [11
] and strong influence of the company’s price image on the customer ’s choice for a retailer has forced retail companies to find a way to stay competitive.
Due to the nature of stationary trade (bricks-and-mortar stores) in particular, the work areas can be described as focused on manual human activities. This is reflected above all in the high personnel costs of between 12 percent (food) and 40 percent (bakery) of total sales [12
]. However, this is not only true for operational activities in direct or indirect customer contexts, but the use of technologies and analyses in the retail trade is traditionally low. Here the core point of the potential impact of the application of ML can be considered.
Also, the operating margins are very low with an average of 0.1% and a maximum of 3% [13
]. Both aspects together, the relatively high personnel costs on the one hand and the low operating margins on the other, make the retail sector an ideal industry for the application of machine learning. Overall, there is an enormous potential for the transfer of human activities, mainly automated decision and reasoning, to machines.