In reviewing the retail landscape of 2018, there have certainly been challenges and opportunities that have driven the evolution of the sector to meet the demand of a changing consumer. Mobile technology, speed of service / delivery and low prices have been just the tip of the iceberg.
For companies in the sector, the pressure begins with consumers who want their product now: in its size, flavor, length, shape, brand, weight, etc.; and is followed by the rapid expansion of markets (globalization). With Amazon investing in R & D at a rate of US $ 22.6 billion in 2017 (source: Bloomberg), and Alibaba with an investment plan of US $ 15 billion over the next 3 years (source: CNBC), both with A growth approach in AI, it is not commercially sustainable for retailers to compete against a seemingly endless variety of items in inventories and with delivery policies of between 0 and 3 days.
However, personalization and customer knowledge is often the way in which retailers tend to compete, but when it comes to managing labor costs, inventory, supplier compliance and deliveries to give value to the consumer, there is a dependence on the information provided by the systems linked to the administrative, commercial and logistic processes of the company, which are not usually systematic when offering multichannel customer service.
Artificial Intelligence (AI) and Machine Learning (ML) could be the answer
In the retail market, the headache is the administration of large amounts of data that allow to draw correlations between a datum (x) and in turn allow to obtain recommendations according to a previous analysis of patterns; all this after having examined the data stored over time, in order to provide a positive service to the consumer.
Taking into account the above, the technology applied by AI would allow minorities to have the ability to:
- Direct a customer in the store to find the item they are looking for, having confirmed their availability or having identified a substitute product, always in order to comply with the request.
- Increase efficiency in the supply chain based on:
- Improve the accuracy of the demand for each product in each location, taking into account promotions, events, cannibalization, demographic data and weather forecasts.
- Correct selection of suppliers, with deliveries on time, in the right amounts, at an optimal cost, in store stores or to final customers.
- Reduction of labor costs of personnel in stores and warehouses, optimizing the hours of the workers according to the demand, calculated in sales flow and inventory.
- Provide relevant analytical information to business users, alerting them to possible deviations and taking actions on their behalf.
- Provide chatbots, for the consumer and staff that allows easy access to information and actions such as: “I want to cook roast duck tonight, ask for the ingredients to pick them up on my way home from work”.
- Detection of fraud identified in the supply chain, warehouses, registry.
- Set prices or promote an item, maximizing the margin and reaching direct sales rates.
The above points, although traditionally overcome by the staff through experience, studies and various practices, have not stopped having a degree of intuition or hunch; giving space to improvements. Improvements that are being addressed by companies such as Infor, which take advantage of the AI and ML algorithms to improve data quality and business analysis, “filling gaps” in the information in order to increase the relevance and precision of the tools of support in decision making.
It is the 21st century! Technological changes have been prominent and technological solutions and tools have also been prominent. There are no excuses for companies to continue delaying the digital transformation process.