Artificial Intelligence Adoption in QSR Industry

Abstract

According to research conducted by Allied Market Research (Kale and Deshmukh, 2020), the Quick-service restaurant (QSR) industry has been enjoying significant growth over the last several years and the trend is expected to continue at least over the next five years. This is due to the agility of most QSR brands in adapting to consumer needs, quality food products and service innovation. Offering the convenience of brands reach for their consumers such as a restaurant within the reach of the customer, as well as providing online ordering and food delivery convenience. The convenience of the omnichannel digital services offered by typical QSR restaurant brands to their customer provides convenience for service delivery and at the same time provides an opportunity to collect, collate, check, consume and analyse the data to examine, explore and provide hindsight, insight and foresight for the QSR industry to be ahead of the competition. Not only are these digital customer channels a great way of providing a better customer experience, but they also help QSR operators in capturing massive data such as sales transactions, customer details, product performance, and daypart (breakfast, lunch and dinner) sales insights. With the ability to analyse and perform analytics on the data and from a data strategy perspective, it is paramount to have a robust big data platform, data warehouse and ability to consolidate data from all the IT systems. This is to enable the development of BI/dashboards to provide better business insights, such as real-time and daypart product sales. Data could also be used to develop new digital products for more positive customer engagements such as upselling and loyalty programmes or for implementing of “smart kitchen” that is able to connect various datasets for maximum kitchen efficiency and cost optimisation. Due to a lack of data science approach and machine learning capability, most QSR brands are missing this sales uplifting opportunity. To develop an effective A.I. and machine learning in the QSR business, there is a need to evaluate and select the best machine learning techniques to address the business challenges. Our objective is to explore and recommend the various machine learning techniques that can be applied to the QSR industry to gain insights and provide the necessary analysis and analytics to ensure quality service and also address changing business challenges in the competitive and ever-changing market by deploying various machine learning models for prediction and pattern analysis through classification and association approaches.