ted talk presentation slides
- Discuss if there is any potential to using Artificial Intelligence in your operations analytics plan in Cracker Barrel .
What do you recommend?
What ethical considerations would you consider with using AI in your analytics in regards to implications to Cracker Barrel stakeholders, specifically the customers.
A Proposed Operational Strategy for Cracker Barrel Old Country Store ®
Tracy Phipps, Ram Sharma Bastola, Bothwell Chavunduka, Karl Schneider, and Merina Shrestha
Amberton University
MGT 6470.E1: Operations Analytics
Dr. Matthew Reagan
November 17, 2020
The authors of this presentation are students and the members of “Group 1” and listed in order of presentation rather than alphabetically.
Overview of Organization
(Presented by Tracy Phipps)
Location: Over 660 stores across 45 states, strategically located along interstates and highways “so that travelers would always have a place to stop, relax, get a good country meal, and feel at home.”
Mission Statement: “Pleasing People ®”
Brand Promise: “Cracker Barrel provides a friendly home-away-from-home in our old country store and restaurant. Our guests are cared for like family while relaxing and enjoying real homestyle food and shopping that’s surprisingly unique, genuinely fun and reminiscent of America’s country heritage … all at a fair price”
(Cracker Barrel Old Country Store, n.d.)
From Cracker Barrel Old Country Store, 2020, https://www.crackerbarrel.com/newsroom/. In the public domain.
Overview of Organization
(Presented by Tracy Phipps)
Each store is approximately 10,000 square feet, including the front porch.
Customers enter in through the country store and can meet a hostess towards the back corner of the store to be seated in the restaurant.
Each restaurant seats around 177 to 207 patrons.
Customers can also shop online to purchase items from the country store.
(Cracker Barrel Old Country Store, n.d.)
From Cracker Barrel Old Country Store, 2020, https://www.crackerbarrel.com/newsroom/. In the public domain.
Proposed Operational Goals
(Presented by Tracy Phipps)
Develop four strategic operational goals for your organization. You can pick any organization or design your goals for a fictional organization.
Recommended Analytics Tools and Software (Presented by Tracy Phipps)
Question Prompt
Identify the analytics tools and software you recommend utilizing to meet those goals.
a. Discuss why you chose these tools and software.
i. This will require you to do additional research beyond your textbooks.
References
Cracker Barrel Old Country Store (n.d.). Get to know us. Retrieved November 10, 2020, from https://www.crackerbarrel.com/newsroom/get-to-know-us
Franks, B. (2014). The analytics revolution: How to improve your business by making analytics operational in the big data era. John Wiley & Sons.
Phillips, J. (2013). Building a digital analytics organization: Create value by integrating analytical processes, technology, and people into business operations. Pearson Education.
Recommended Statistical Analyses That Address The Strategic Goals
Presented By: Ram Sharma Bastola
Recommended Statistical Analyses And Why
Cracker Barrel can collect various data and calculate it using different statistical analyses.
Firms need to implement superlative decisions to smoothen their operations using different statistical analyses to achieve firms goal.
Data analytics can help the restaurant and retail industry to improve their menus, marketing policies, price, product, employees, place, promotion, sales, and forecasting.
Areas of operations are the important departments for any industries to focus to achieve their goals. Cracker Barrel is one of the restaurant and retail country store among many which serve their customers to eat and shop. Various factor plays role in their performance to satisfy the customer needs. The goal is to recommended different statistical analyses to achieve strategic operations goal. So, data analytics can help the restaurant and retail like Cracker Barrel to improve their menus, marketing policies, price, product, employees, place promotion, sales and forecasting. After collecting data and using various statistical analyses methods, the firm will also understand the best pricing structures to adopt which best for the customers demands.
Recommended Statistical Analyses and why Cont’d
Risk management is critical in any industry.
The industry deals with multiple risks in different time. It could be from their competitors, economic recession, locations and probably target market.
Some of them include huge capital outlays, rivalry from low-cost carriers, and volatility in customer demand (Ho, Zheng, Yildiz, & Talluri, 2015).
Cracker Barrel competitors includes Golden Corral, IHOP, Dennys, Darden Restaurants, Bob Evans and so on.
In regards to competitors and manage risk, data analytics can transform Cracker Barrel by improving its menus, price, promotion, customer services, locations, target people and market. For example: pandemic hits restaurant business to close dine-in which is huge loss to them. However, the scenario can be changed through data analytics. The approach will promote live forecasting, which will replace monthly or seasonal planning. That way, a firm can make accurate forecast regarding customers to prefer to dine-in replace by take out thru free delivery option. Data analytics will also help in comprehending customer behavior from dine-in eat to take out.
Recommended Statistical Analyses and why Cont’d
Data analysis is divided into several categories, thereby giving a restaurant and retail stores like Cracker Barrel the chance to choose ones that address its needs according to strategic operations goal.
Regarding the operations of focus, the types of data to be considered include Descriptive Analysis, Diagnostic Analysis, Predictive Analysis and Regression Analysis.
The categories are elaborated distinctively, but they are all connected to attain a common goal.
The first one tells what happened, the second analysis tells why it happened, third tells what is most likely to happen and last one tells what will happen in future when we compare two different data’s.
The basis of all data insight is comprised on descriptive data analysis. The assessment is straightforward; hence, widely used in the modern business environment. The main objective of descriptive analysis is to elaborate what happened in a situation by examining past data, mainly in the form of KPI dashboards (Nassaji, 2015). In the case of restaurant and retail business like Cracker Barrel the analysis will focus on sales, monthly revenue, number of customers in a month, product sales, price changes record. In such case, it will help to figure out differences on number from previous month to this month. It will let management know exactly what happens in sales and revenue after make some changes on promotion and coupons, price change on products, added new menus.
Recommended Statistical Analyses and Why Cont’d
Diagnostic analysis takes a deeper dive into a situation to explain why it happened in the first place.
It adopts the insights generated from descriptive analytics to make a thorough diagnosis.
Diagnostic analytics will help the Cracker Barrel to locate the root cause of problems. Using proper statistical data , it helps to establish more links between those data and recognize behavioral patterns and fix the root cause of problem.
Predictive analytics helps to understand what is most likely to happen in the future. So, it helps in forecasting the future price, product, promotion patterns, sales and so on.
Furthermore, Regression analytics will help to compare what if we make changes in one reflects the changes on another. Basically, relationship between two data (Phillips, 2013). For example: Menu prices change and total revenue collect. So, regression help to make the prediction on sales in this scenario.
One of the main features of diagnostic analytics is its ability to build detailed information. It explain why it happened in the first place and adopts data and information to make a thorough diagnosis. Therefore, Cracker Barrel needs to take it all and adopt diagnostic analytics to interconnect the underlying challenges to overcome to achieve strategic operations goals. That way, they become clear to implement effective solutions. Finally, through predictive analytics, the company can forecast its future. It will help to understand what will happen in future after the changes that might encounter the market (Moll, Berg, Ewers, & Schmidt, 2020). The Regression analytics will help to compare between two data. If management has decided to change one data, it helps to know the reflection on another (Phillips, 2013). Therefore, regression statistical analyses help to make the future prediction on sales and revenue.
Statistical Sales Data Analysis of Cracker Barrel
For the third quarter of fiscal 2020, when compared to the comparable period in 2019, comparable restaurant sales declined 41.7% and comparable store retail sales declined 45.5%.
All Cracker Barrel stores have remained open. However, all stores were operating in an off-premise-only model with no dine-in service from late March through late April, with incremental dine-in openings initiating thereafter.
For the week ending May 29, 2020, when compared to the comparable period in 2019, comparable store restaurant sales for stores with limited dine-in service decreased approximately 32% compared to approximately 76% for stores that were limited to an off-premise-only business model.
As of May 29, 2020, 505 stores had limited dine-in service, and the Company expects that substantially all stores will have limited dine-in service by the end of June.
In connection with its actions addressing the COVID-19 pandemic, the Company provided the above update on its sales. The restaurant sales has declined 41.7% in the third quarter of fiscal year 2020 com pared to 2019. And 45.5% sales declined on retail sales compared to 2019. This comparison is only possible through sales data using regression statistical analysis. Also, dine-in service decreased approximately 32% for the week ending May 29, 2002 compared to 2019. Therefore, the recommended statistical analyses such as descriptive analysis help us to know pandemic happened, diagnostic analysis further help us to know because of viruses it happened, predictive analysis help us to know all declined in sales and regression analysis helps us to compare between 2019 and 2020 fiscal year by what percentages sales has decline on those specific quarter and months. It is very important to do analyses using statistical data.
References
Ho, W., Zheng, T., Yildiz, H., & Talluri, S. (2015). Supply chain risk management: a literature review. International Journal of Production Research, 53(16), 5031-5069.
Moll, M., Berg, T., Ewers, S., & Schmidt, M. (2020). Predictive Analytics in Aviation Management: Passenger Arrival Prediction. In Operations Research Proceedings 2019 (pp. 667-674). Springer, Cham
Nassaji, H. (2015). Qualitative and descriptive research: Data type versus data analysis. DOI: 10.1177/1362168815572747
Phillips, J. (2013). Building a digital analytics organization: Create value by integrating analytical processes, technology, and people into business operations. Pearson Education.
Customer Demand and Needs
(Presented by Bothwell Chavunduka)
Customer Demand
Basis of decision making
Assessment of business trends
COVID-19
Social distancing
Contactless services
Generational
Younger consumers focused on brands (Generation Z & Millennials)
Brand Loyalty
Consumers are now more interested in what’s important to them, what a brand stands for, what it does for the community, sustainability etc.
Conduct price elasticity
Customers desire products less as they become expensive
Mark down pricing to sell out remaining inventory or products after sales season
Question Prompt
How will the strategic operational goals be tied to customer demand and needs?
What are some questions you would consider asking customers?
Why those questions?
Select the option that more appealing to you. (List two product images to choose from)
Product packaging
Were the associates friendly?
Shopping experience
How likely are you to return to this store?
We need to verify customer retention
Customer Demand and Needs (conti.)
(Presented by Bothwell Chavunduka)
Customer Demand
Basis of decision making (conti.)
Inventory replenishment
Impact of promotional activities on sales and brand recognition
Planning operations for management
Reducing operating costs
Reliable estimation for menu items
Revenue management system
Customer Demand and Needs (conti.)
(Presented by Bothwell Chavunduka)
Customer Needs (motivation to buy)
Generating higher sales
Choice
Cater to a wide range of customer needs
What exactly the customer is looking for or favorite products
Customer centric
Making customers feel known, understood, and valued when they interact with us
Feedback on social media
Convenience
E-commerce
Supports busy lifestyles
Customers are able to get what they want easily
Price
Affordable
Depends on targeted segment
Quality
High as possible (balanced with price)
References
Lee, Y., Lee, J. P., & Kim, S. (2019). Optimal timing of price change with strategic customers under demand uncertainty: A real option approach. Advances in Production Engineering & Management, 14(3), 379–390. https://doi.org/10.14743/apem2019.3.335
Artificial Intelligence (AI) Recommendations
[Presented by Karl Schneider]
Are Cracker Barrel‘s Nostalgic Customers Ready To Give Up Their Waitresses…
(Coley, 2020)
Question Prompt
Discuss if there is any potential to using Artificial Intelligence in your operations analytics plan.
a. What do you recommend?
… for Robots ??
(Shanghai Jinghong Robot Co., 2020)
These robot waiters from China start at just $4,000 each plus shipping. They cannot pour coffee.
NO WAY !
(Coley, 2020)
But that doesn’t mean AI is out of place in the restaurant industry. Restaurants are a subset of retail, and Chui et al (2018) identified retail as the industry which will benefit the most in absolute dollars from AI. Domino’s has had some success in trials of using AI to process telephone orders (Maze, 2019). But Cracker Barrel is a full-service restaurant where the human touch is part of the experience.
In my next slides I’ll recommend CB use AI to better understand customers. I’ll explain how AI can achieve this.
AI@Cracker Barrel : Hearing The Customer
SOCIAL MEDIA IS PUBLIC.
CRACKER BARREL IS FREE TO USE AI TO ANALYZE IT.
(Cracker Barrel [@CrackerBarrel]. 2020)
Customers are ready for Cracker Barrel robots to listen to their thinking. That’s why they post every day to Twitter or other social media.
A question you should have is this. Is AI ready? The short answer is yes. Cao et al (2020) reported that AI is capable of not only language recognition, but emotional recognition.
AI@Cracker Barrel : Hearing The Customer
Example of a real AI analysis of customer feelings, using social media data
(Monmousseau, 2020)
Not just hypothetical, it’s been done! The example above was done for the airline industry. Cracker Barrel may need to tune the parameters shown above for the restaurant industry, and of course the identity of their major competitors would differ as well.
AI@Cracker Barrel : Forecasting Sales
Technology exists. AI Neural Networks achieved rapid and accurate sales forecasts for restaurants by the year 2011 (Chen et al, 2011).
Cracker Barrel has the data. Neural Networks require large data sets and time spans for training but are tolerant of noisy data (Lasek et al, 2016, Table 3). Cracker Barrel would use data on past sales and promotions, as well as demographic data that led to past siting decisions. An AI vendor like Tenzo (Norton, n.d.) claimed they can work with as few as 2 years data. Other important data, such as holiday dates and weather data, is easily obtained.
An incumbent like Cracker Barrel has a competitive advantage of decades of data.
(ClipArtMax, 2019)
Note, Chen et al was published in 2011, so AI technology will have improved even further in the decade since.
Lasek et al (2016) suggested in their Table 1 that the most likely predictive variables for restaurant sales and customer demand forecasting are time, weather, holidays, promotions, events, historical trend data, macroeconomic indicators, location type, and demographics of the location. The model will also want to draw on the AI social media analysis mentioned in the previous slide.
AI@Cracker Barrel : Forecasting Benefits
Real-time. Needed due to the dependency on weather.
Efficient labor. Understaffing is the #1 cause of negative customer reviews (Norton, n.d.) in restaurants, but overstaffing increases costs. AI vendor Tenzo promises to reduce labor costs by at least 5%.
Efficient inventory. Less food waste, without running out of menu items. AI vendor Tenzo promises to reduce food costs by at least 2%.
More accurate. AI vendor Tenzo promises to forecasting error by at least 29% over traditional statistical models, while providing item-level forecasting, all at a granularity of one hour (Norton, n.d.).
(ClipArtMax, 2019)
.
AI@Cracker Barrel : Suggestive Selling
AI suggestive selling leads to a “higher average check” (Maze, 2019). AI vendor TabSquare promises a 5% boost in average order value (Bawazer, 2019).
(Woods 2019)
Would you like some pie for dessert? Cross selling or upselling has been a part of restaurants for a long time.
Human waiters and waitresses find selling a burden, with only 59% doing so in one restaurant mystery shopper survey (Bawazer, 2019).
AI@Cracker Barrel : Suggestive Selling
Personal Touch. To keep the old fashioned “personal touch” that a full-service restaurant offers, I recommend Cracker Barrel NOT use kiosks to take orders and suggestive sell. Instead, have the human server enter orders on a tablet. The tablet AI should discreetly display an menu suggestion to the human server, who presents it to a customer only if the customer seems in a mood to hear it.
For this to work best, the AI must perform Analytics prescriptive and real-time (easy), and ideally the AI would identify the customers upon their arrival (hard).
(Coley, 2020)
Identifying the customer would allow the AI to improve its selling suggestions with past purchase data, even dietary and allergy data. Customers might be induced into identification with a fixed-price, pay-up-front approach that some buffet restaurants utilize. Alternatively, it may be possible to identify customers with video data.
For the most profitable results, the selling would take into account not just customer preference, but inventory issues and sales forecasts.
Artificial Intelligence : Monitoring Operations
(Splitter, 2020)
Hungry customers aren’t the only reason restaurant inventory can shrink. I recommend Cracker Barrel use AI to detect unusual patterns in shrinkage that may signal a problem. The system shown was purchased from DragonTail Systems by Domino’s Pizza (Splitter, 2020)..
(Splitter, 2020)
AI@Cracker Barrel : Monitoring Operations
AI Can Analyze Video, Audio, and Transaction Data To Verify
Recipe conformance
Food freshness conformance
Safety procedural conformance
Employee engagement
Inventory theft or other theft
In 2015 AI surpassed human capability at
image recognition (Chui et al, 2018).
From Mr. Ido Levanon, as cited by Splitter (2020), an example of recipe conformance was the correct number of pepperoni slices. Example safety conformance was hand-washing, glove-wearing, surface-cleaning, and in this day and age, mask-wearing. Levanon did not mention food freshness but it could be easily calculated by AI based on the time food was taken away for delivery (whether by car or waiter) minus the time food was prepared. DTIQ (2020) reported their system can cut theft losses by 50% in some cases.
(Splitter, 2020)
AI@Cracker Barrel : Monitoring Operations
Limitations And Recommendation
For Cracker Barrel
While AI is becoming better and better at detecting issues, today it still requires a human operator to act on issues the AI identifies and presents in its dashboard, such as an identified need to terminate an employee for theft. Cracker Barrel would be advantaged to install an AI monitor because while human store managers make some efforts to improve operational performance, no human can equal the tirelessness and tenacity of AI. The mere knowledge that an AI system is installed may influence employees. The DTIQ (2020) website lists a number of well-known restaurant brands amongst their customers.
AI@Cracker Barrel : Conclusion
Greatest strengths of AI:
Ability to ingest myriad sources of data, a.k.a. Big Data (Franks, 2014) or Omnichannel Data (Phillips, 2013).
Ability to machine learn and adapt, escaping the programmatic straitjacket of conventional software.
AI never sleeps!
.
AI
Caveats
(Pershan, 2019)
One caveat is that AI may need to be implemented slowly due to the well known revenue starvation the restaurant industry is undergoing currently due to COVID-19, which impacts the ability to raise capital for an AI project.
The second caveat is that I recommend Cracker Barrel avoid AI for the sake of AI, as I illustrated earlier with my “robot waiters” slide. Cracker Barrel does not want to become the next Eatsa. Eatsa was a failed fully self-serve restaurant that is now closed permanently (Pershan, 2019). Amongst the problems it encountered was being sued by blind people for failure to accommodate them.
Dominos is experimenting with AI to take telephone voice orders (Maze, 2019) in frustration with customers who refuse to enter their orders online via the website. I am not sure if that battle with the customer will end well, though it could if it cuts costs enough. The uncertainty highlights that one potential use of AI could be to self-evaluate its own effectiveness.
The third and final caveat of AI is ethical issues, which our next speaker will address.
References
Bawazer, N. (2019, April 1). How AI-powered ordering can boost your upsell success. TabSquare AI Blog. https://blog.tabsquare.ai/2019/04/01/how-ai-powered-ordering-can-boost-your-upsell-success
Cao, S. S., Jiang, W., Yang, B., & Zhang, A. (2020). How to talk when a machine is listening: Corporate disclosure in the age of AI. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3683802
Chen, P. C., Lo, C. Y., Chang, H. T., & Locho, Y. (2011). A study of applying artificial neural network and genetic algorithm in sales forecasting model. Journal of Convergence Information Technology, 6(9), 352-362. https://doi.org/10.4156/jcit.vol6.issue9.41
Chui, M., Manyika, J., Miremadi, M., Henke, N., Chung, R., Nel, P., & Malhotra, S. (2018). Notes from the AI frontier – Insights from hundreds of use cases. McKinsey Global Institute. https://www.mckinsey.com/~/media/mckinsey/featured%20insights/artificial%20intelligence/notes%20from%20the%20ai%20frontier%20applications%20and%20value%20of%20deep%20learning/notes-from-the-ai-frontier-insights-from-hundreds-of-use-cases-discussion-paper.ashx
References, continued
ClipArtMax. (2019). Observation vector [Clip Art]. https://www.clipartmax.com/max/m2i8G6m2G6Z5Z5b1/
Coley, B. (2020, June). At Cracker Barrel, Guests are coming back and so are sales. Full Service Restaurant News. https://www.fsrmagazine.com/casual-dining/cracker-barrel-guests-are-coming-back-and-so-are-sales
Cracker Barrel [@CrackerBarrel]. (2020). Tweets & replies[Tweets]. Twitter. Retrieved November 12, 2020, from https://twitter.com/CrackerBarrel/with_replies?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor
DTIQ. (2020, October 30). Business analytics solutions. https://www.dtiq.com/products/advanced-analytics/
Franks, B. (2014). The analytics revolution: How to improve your business by making analytics operational in the big data era. John Wiley & Sons.
Lasek, A., Cercone, N., & Saunders, J. (2016). Restaurant sales and customer demand forecasting: Literature survey and categorization of methods. Smart City 360°, 479-491. https://doi.org/10.1007/978-3-319-33681-7_40
References, continued
Maze, J. (2019, November 1). Artificial intelligence takes hold at U.S. restaurants. Restaurant Business. https://www.restaurantbusinessonline.com/financing/artificial-intelligence-takes-hold-us-restaurants
Monmousseau, P., Marzuoli, A., Feron, E., & Delahaye, D. (2020). Impact of COVID-19 on passengers and airlines from passenger measurements: Managing customer satisfaction while putting the US air transportation system to sleep. Transportation Research Interdisciplinary Perspectives, 7, 100179. https://doi.org/10.1016/j.trip.2020.100179
Norton, E. (n.d.). Sales forecasting methods: A comprehensive guide for restaurants. Tenzo Blog. https://blog.gotenzo.com/sales-forecasting-methods-guide-for-restaurants
Pershan, C. (2019, July 23). Automated quinoa shop Eatsa is now a tech company married to Starbucks. Eater San Francisco. https://sf.eater.com/2019/7/23/20706270/eatsa-closed-tech-company-starbucks-investment-brightloom
Phillips, J. (2013). Building a digital analytics organization: Create value by integrating analytical processes, technology, and people into business operations. Pearson Education.
References, continued
Shanghai Jinghong Robot Co. (2020). 1st generation-high quality humanoid robot waiter intelligent. www.alibaba.com. Retrieved November 12, 2020, from https://www.alibaba.com/product-detail/1st-generation-High-quality-Humanoid-Robot_60693612081.html
Splitter, J. (2020, April 23). This AI camera can help restaurants show that their food is safe from coronavirus. Forbes. https://www.forbes.com/sites/jennysplitter/2020/04/23/this-ai-camera-can-help-restaurants-show-that-their-food-is-safe-from-covid-19/
Woods, S. (2019, November 11). How trade promotions can dramatically increase sales. Pepperi. https://www.pepperi.com/how-upselling-and-cross-selling-can-dramatically-increase-field-reps-orders/
Artificial Intelligence and Ethical Considerations
(Presented by Merina Shrestha)
Question Prompt
What ethical considerations would you consider with using AI in your analytics in regards to implications to your stakeholders, specifically the customers.
ETHICAL Considerations
Generally, emerging technologies have been associated with a number of ethical issues.