Artificial intelligence in the service of sales
Artificial intelligence can be defined as the use of the power of computers to imitate human intelligence, which is essentially the application of mathematical models to increasingly sophisticated algorithms. The term was coined in 1956 by the American mathematician John McCarthy to describe the ability of an algorithm to learn from its past examples and make new decisions based on what it learns. Models and the software solutions they serve, can learn and improve over time, humans usually can't or won't. Systems that are designed to take advantage of artificial intelligence are more powerful than those designed for humans and are not subject to the emotions that are usually present when humans perform their jobs.
Artificial intelligence has huge potential for the FMCG industry: from smart devices that allow shoppers to remotely monitor spending patterns, to personal shopping assistants that offer recommendations based on past purchases, AI-based applications are poised to transform both physical and electronic commerce.
What is the implementation process?
Before implementing intelligent solutions in their processes, retailers must solve three critical questions:
determine if they have processes designed in such a way that they can benefit from the advantages and opportunities provided by technology,
choose systems that are capable of effectively implementing new services,
identify ways to integrate these intelligent tools into existing supply chains with mandatory process redesign to take full advantage of the new systems.
The implementation process begins with a snapshot of the state of existing data, processes and rules within the company, which collects historical data and business logic related to the ordering process. Sales models are created for each SKU based on historical data for each outlet. It is necessary to have as much information as possible which can affect sales, from promotion, secondary positions and external conditions such as weather.
The next step is integration with the existing business systems used in the organization. Based on this, modeling is done at the SKU level at each individual point of sale and we get a sales model, and then by applying business rules we create an order.
What are the use cases?
The possibilities for use are different and depend on the industry. The basis of everything is the creation of a sales model and the generation of orders based on business rules, the proposal of procurement and/or production based on several weeks' sales needs, and the reduction and optimization of returns in accordance with business rules.
Bakery industry has some specialized models, such as real-time orders for baking in convection ovens and real-time orders for assembly of sandwiches. Retail chains have specialized models that generate a proposal for purchasing of fruit and vegetables, a proposal for assortment lists by POS and shelf life reduction. Models for self-service restaurants (in supermarkets and hotels) suggest menus of ready-made meals, resulting in a reduction of waste, which has a positive effect on the environment. Models for distribution companies generate a forecast of deliveries in order to optimize capacities and future routes of the delivery fleet.
What future or even present brings?
Bearing all this in mind, the entire FMCG sector benefits from the implementation of artificial intelligence in all segments of work. The potential of technology in the FMCG area is great and growing month by month. No matter what your business area is focused on - processes based on artificial intelligence help to achieve better results. Applying predictive analytics helps you take advantage of all the data and find new opportunities for your business.
It is known that self-service has transformed the experience in the store, in the same way artificial intelligence will bring new views and ways of working to the processes that were then transformed that enable the development of personalized products, making optimal decisions in real time (such as determining the best selling price), efficient management of supply chains and finally improving the customer experience.