By Lacie Blankenship
In a forthcoming special issue of Production and Operations Management on business analytics, Kejia Hu, Brownlee O. Currey Jr. Dean’s Faculty Fellow and Assistant Professor of Operations Management, along with Morgan Swink of Texas Christian University and Xiande Zhao of China Europe International Business School, discuss the current state of business analytics (current usage and challenges) and goals for future data analysis based on interviews with several c-suite executives at major American and Chinese restaurants and food supply chain firms. Below are 4 takeaways from their research:
Restaurants collect a wide variety of data across their supply chain. Restaurant chains’ data analytics capabilities have historically lagged behind other industries, but they are catching up. Restaurant chains collect data from a large number of touchpoints from farm to table (i.e.: farming data, shelf life data, inventory data, kitchen efficiency data, sales data, and so much more). The data doesn’t end on the logistics side. There is a significant amount of data collected from customers categorized as basic data (i.e., demographics), engagement data (i.e., visit frequency), or behavioral data (i.e., purchase history).
This data is used in ways customers can and can’t see. The authors describe several instances in which restaurants utilize their data to learn, make strategic decisions, solve problems, identify new revelations, and plan for the future. Many restaurants build customer profiles where survey feedback can help with marketing and customer service efforts and can even help with engagement. Starbucks, for instance, builds customer profiles by offering free internet to customers who provide their basic information: full name, email address, and zip code. McDonald’s and Chick-fil-A collect data from customers by offering free food in exchange for answering a survey. In these contexts, customers understand how their inputs can help with marketing and customer service efforts… but that isn’t the case for all data collection. Personal profiles that collect demographic information can help restaurant operators “adjust food offerings to engage customers,” the authors write. “McDonald’s recently purchased an AI development company to generate applications that will customize menu offerings based on weather data to increase revenue.” Distributors gather internal geographical sales pattern data to determine where to send specific food products based on consumption patterns and inventory needs. Likewise, it is common practice for restaurants to track delivery driver data to ensure the maintenance of, “food quality upon arrival to the customer.” Supply chain-related data collection and analysis is also used to understand and plan for ingredient shelf-life/freshness, inventory quality, employee scheduling, employee satisfaction, menu design, menu offerings, expansion (new locations), and much more.
The restaurant industry has blooming potential. Despite the numerous systems that collect and analyze the industry’s massive amount of data, the study notes that these activities are often not well integrated. Data technology and structure vary throughout the supply chain, necessitating significant ongoing audit work to ensure proper data quality. “The high level of vertical disintegration in most supply chains results in clusters of data and analytics activity,” the authors write, offering significant opportunity for parties to integrate and consolidate their data to improve technology for better data flow and real-time insight for decision-makers across the supply chain.
One of the interviewed restaurant industry leaders points out the lack of standardized data- “a distributor operating in a region may have a completely different product numbering and protocol system than a distributor in another region, even if the distributors are owned by the same overall company,” they explained.
Another executive voiced challenges with advancing their analytics capabilities when stores are owned by franchisees and a corporation. A franchisee with multiple stores under different brands might use a system incompatible to other franchise owners. “Large franchise owners often make decisions that optimize their own operations while making their larger supply chains inefficient,” they explain.
These challenges, among others referenced, translate to opportunities for combative action. A strong first step would be implementing industry-wide regulations and standards, as noted in the study.
The industry presents opportunities for food industry workers and researchers. A significant component of the manuscript was the inclusion of various opportunities within the industry. In terms of industry workers, the study mentions the opportunity to use social media data to identify food quality issues. “Monitoring social data could have helped Chipotle to swiftly shut down stores in appropriate regions during their e-coli breakout a few years ago,” the authors write. The study also discusses opportunities for managers to use data to efficiently manage their staff through analysis of scheduling factors like employee commute times and quality of life.
The authors detail a significant runway for researchers to leverage; they note “opportunities for research on business analytics applications in the restaurant industry can be grouped into 2 broad agendas: development research and implementation research.” Development research is related to technical challenges like the use of machine learning, and implementation research is more so linked to an exploratory approach with improvement opportunities being used as a starting point for trial-and-error. Below are 6 briefs on potential research opportunities the authors included in the manuscript:
- The need for restaurants to share data to improve food quality and lower costs
- The developing roles of data standards and inter-organizational systems, along with the incentives/disincentives for standardization
- The pros and cons of formalized data literacy training versus department specialization
- The use of new analytic technology to develop predictive models
- The analysis of responsive distribution models supporting dynamic menus and smart pricing
- The developing role of customer self-service/engagement with customized foodservice
To review the complete study – Analytics Applications, Limitations, and Opportunities in Restaurant Supply Chains- click here.
If you want to know more about business analytics in the restaurant and food supply chain industry, you can reach out to Professor Hu at her personal website: www.kejiahu.com or email her at email@example.com.