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 there is pressure by consumers who want an immediate and varied product (size, taste, length, shape, brand, weight, etc.) and 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.
Customization and customer knowledge are often the way in which retailers tend to compete. However, when it comes to managing labor costs, inventory, compliance of suppliers and deliveries to give value to the consumer, there is a dependence on the information provided by systems linked to the administrative, commercial and logistical processes of the company, which are not usually integrated 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 create correlations and at the same time obtain recommendations from a previous analysis of patterns. These analyzes must contemplate the data stored over time, in order to provide a service of value to the consumer.
Taking into account the above, the technology applied by AI would allow minorites to have the ability to:
- Direct a customer in the store to find the item they are looking for, having confirmed their availability or 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
- Correctly choose suppliers, with deliveries on time, in the right amounts, at an optimal cost in store stores or end customer
- Reduce 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: “requesting that the ingredients for a recipe be packed and ready to be picked up at the store on the way home”
- Detect fraud in the supply chain and warehouses
- Set prices or promote an item, maximizing the margin and reaching the direct sales rate
Although the previous practices have traditionally been overcome by the staff through experience, studies and diverse practices, they have not stopped having a degree of intuition, giving space to improvements. Improvements that are being addressed by partner companies such as Infor and Cerca Technology, which take advantage of the AI and ML algorithms to optimize data quality and business analysis, fill these “gaps” in the information and 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.
Author: Bryan Buitrago, General Manager at Cerca Technology
