Leveraging AI to Predict Demand and Drive Just-in-Time Operations
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This document discusses how leveraging artificial intelligence (AI) can help companies predict demand and enable just-in-time operations. It highlights how DataRobot's automated machine learning platform can help companies build hundreds of predictive models to optimize business processes across various functions like marketing, supply chain, and customer service. The document also notes that while AI has great potential, many companies struggle due to lack of data science expertise and resources to develop models at scale. Automated machine learning helps address this challenge by accelerating AI adoption and increasing a company's modeling capacity.
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Leveraging AI to Predict Demand and Drive Just-in-Time Operations
- 1. Leveraging AI to Predict Demand and Drive Just-in-Time Operations
- 2. The World’s Most Advanced Enterprise-Grade Automated Machine Learning Platform 2012 Founded, HQ in Boston, MA $224M In funding 800,000,000+ Models built on DataRobot Cloud 200+ Data Scientists & Engineers (of 500+) 4 #1 ranked Data Scientists 50+ Top 3 finishes I N S U R A N C E F I N T E C H H E A L T H C A R E R E T A I L M A R K E T I N G B A N K I N G M A N Y M O R E
- 3. $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ Automate $ Optimize $ Produce actionable insights Machine Learning Deep Learning Machine Learning: Learning from the past to predict the future AI: Systems able to perform tasks that ordinarily require human intelligence Boring Stuff Deep Learning Machine Learning Definitions
- 4. Those who rule data will rule the entire world.” Masayoshi Son CEO | Softbank “
- 5. If your competitor is racing to build AI and you don’t, they will crush you.” Elon Musk CEO | Tesla “
- 6. AI will generate $2.9 TRILLION in business value and recover 6.2 BILLION hours of worker productivity by 2021. The Impending AI Divide Gartner Predictions (Forbes)
- 7. AI-Driven companies will take $1.2 TRILLION from competitors by 2020. The Impending AI Divide Forrester Predictions 2017 Artificial Intelligence Will Drive The Insights Revolution
- 8. What does this mean for Retailers?
- 9. AI leverages data and human expertise to make retailers more profitable and efficient Historical data captures the collective experience of the retailer Skilled retailers understand the marketplace and practical constraints Improved Demand Forecasts Increase Revenue and Efficiency
- 10. IT Services Dynamic provisioning of servers Dynamic health monitoring Marketing Upsell / cross-sell opportunities Targeted marketing Rewards program planning Human Services Employment/hiring trends Employee Injury avoidance Production Planning Existing SKU production New SKU production Materials sourcing Customer Service Call center staffing Complaint management Finance & Accounting Working capital requirements Accounts receivable Sales Pricing elasticity Distribution / Fulfillment Centers Staffing optimization Capacity planning Shipping Operations Store staffing Capacity planning, allocation, and replenishment Lifetime value Demand forecasting is central to all use cases There are Hundreds of Opportunities for Retailers to Optimize Operations
- 11. What are companies doing?
- 12. Army of Data Scientists Path to AI Success?
- 13. The AI Bottleneck: Data Scientists Domain Expertise Programming Skills Math & Stats PREREQUISITES: Domain Expertise Knowledge of the overall & specific missions Knowledge of the data Programming Skills Ability to write code to gather data Ability to write code to explore/inspect data Ability to write code to manipulate data Ability to write code to extract actionable intel Ability to write code to build models Ability to write code to implement models Math & Stats Foundational statistics Internals of algorithms Practical knowledge and experience Knowing how to interpret and explain models
- 14. In order to recognize the potential of AI, organizations need to utilize tools that will accelerate adoption Time NumberofOpportunities 100 50 0 150 Hand Coding Every Model Cannot Possibly Meet the Demand
- 15. Where AI is applied in every business process to predict outcomes, offering unprecedented scale, speed, and efficiency to compete in an ever-changing marketscape. Vision AI-Driven Enterprise
- 16. 1. It enables your data science teams to be more productive 2. It increases the number of people who can build AI solutions Time NumberofOpportunities 100 50 0 150 Automated Machine Learning Greatly Increases Capacity
- 17. It goes beyond just being able to forecast we need a hundred blouses,” (Amazon’s Jenny) Freshwater says. “We need to be able to determine how many do we expect our customers to buy across the sizes, and the colors. And then ... where do we actually put the product so that our customers can get it when they click ‘buy.’ ” NPR November 2018 Everyone Else is Racing to Get There First “
- 18. Walmart Labs vice-president of supply chain technology engineering Parvez Musani told Business Insider: “Eden’s suite of apps helps Walmart associates better monitor and care for fresh fruits and vegetables that are waiting to be shipped from distribution centres to stores.” “Eden has so far saved Walmart $86 million during its trial in 43 distribution centres since January 2017.” Business insider/Foodprocessing.com March 2018 Everyone Else is Racing to Get There First “
- 19. Nike’s ‘Express Lane” strategy is on the way to cutting lead times in half and “driving double-digit growth in many of our key cities,” CEO Mark Parker said on a recent earnings call. The CEO said the company's next task will be to tackle demand forecasting with technology, so Nike can capitalize on its shorter lead times.” SupplyChainDive.com September 2018 Everyone Else is Racing to Get There First “
- 20. How Does it Work? Align Stakeholders Business, Data Science, and Executives need to work together to iterate and explore tradeoffs in modeling and operations. Execute Focus on automation and deployment. Integrate results with dashboards, planning systems, etc. Track Measure Return On Investment, build momentum, and develop and AI-Driven Enterprise Feedback Model optimization, capture unforeseen events, retune, refine predictions
- 21. 1. Retailers have the opportunity right NOW to become an AI-Driven Enterprise and stay competitive 2. Demand forecasting should inform operations of all areas of the business 3. The stakes for companies couldn’t be higher Actionable Takeaways from this Session: BOOTH 1231
Notes de l'éditeur
- JAY: My name is Jay Schuren and I’m the GM of our Time Series activities at DataRobot. My focus is on working with our customers to tackle forecasting use cases across industries. YASH: My name is Yash Bhatt, and I manage the Data Science team at Walmart Canada e-Commerce. My role entails leveraging AI to drive revenue, reduce cost and improve customer experience.
- JAY: DataRobot pioneered Automated Machine Learning – the company was founded in 2012, has raised over 200M, we have over 200 data scientists – 4 have been ranked #1 in the world in the competitive data science contest Kaggle.
- JAY: Before we start – I want to be sure we are using the same definitions. JAY: Machine learning is learning from the past to predict the future. Deep learning is a a subset of machine learning. JAY: AI are systems able to perform tasks that ordinarily require human intelligence. JAY: Within an organization there are many AI systems – Here the gray – these are boring things, think rule based systems – planning systems. The dark blue is a deep learning use case, potentially something with image related, and the vast majority are machine learning. JAY: Each one of these AI systems produces revenue by automating a process, optimizing a process, and producing actionable insights.
- JAY: The importance of AI for companies today can’t be understated. Here is the CEO of Softbank, Masayoshi Son saying those who rule data will rule the entire world.
- JAY: You can’t be an AI company and not quote elon musk – I don’t know how carefully you read the NRF paperwork, but it was in the presentation guide. JAY: If your competitor is racing to build AI and you don’t – they will crush you!
- JAY: Gartner said that AI will generate 2.9 Trillion in value, and recover 6.2 billion of hours of worker productivity by 2021.
- JAY: It’s a struggle to think of a company or organization that won’t be affected by AI. In fact, according to a Forrester study, there will be an incredible $1.2 Trillion that will be transferred to companies that are AI Driven. We call it the AI Divide, which will separate companies into haves and have-nots. With chaos comes a huge opportunity and equally big risk. Each of us has to decide on what side of the technology divide we’re going to be on.
- YASH: What does this mean for Retail? YASH: The pace at which retailers are adopting AI has skyrocketed over the last few years. Most of the adoption for retailers has been in the area of personalization, demand prediction, marketing and communications such as chat bots. Personalization comes in where retailers want to personalize a customer’s shopping experience based on their purchase history. Demand prediction is used to understand what future demand looks like in order to better manage/plan inventory, labor, etc. YASH: I believe that if retailers are early in identifying opportunities to leverage AI in different domains of their business and reduce the integration time in terms of embedding it in the enterprise they are at a superior advantage.
- YASH: Demand forecasting is a conventional problem, but still remains one of the most valuable challenge to solve for any business. The plethora of different variables that impact demand are highly dynamic in nature. The key is to kind the right variables that directly impact your business. Jay: Historical data captures the collective experience -- companies have the data necessary to get started. Jay: Retailers understand the constraints and the improved demand forecasts are often inputs to existing planning processes. Jay: AI plus that data and the human expertise to drive profits
- YASH: There are hundreds of opportunities for retailers to optimize operations. I’m not going to talk through each use case here YASH: Demand is central to all these use cases – We all intuitively know that if demand was to double, that has to impact staffing, production, and operations generally. Demand forecasting allows us to be proactive and capitalize on the opportunity. The tighter the connection between operations and demand, the more efficient and profitable the retailer an be. YASH: Beyond individual use case, these are often interconnected. The same dataset drives many areas of the business…. YASH: Labor planning is a great starting point… from their you can go up to financial planning and exec dashboards, then down to sku level for replenishment and in-stock/out-of-stock items
- JAY: What are companies doing?
- JAY: Every single article and report on AI starts with a reference to these companies, and so I thought I’ll honor that tradition also. While they are certainly leading the charge and using AI to disrupt the competition, and building massive teams and absorbing every smart data scientist that gets produced, I want to assure you there is a different way and you too can compete using AI
- JAY: The biggest bottleneck to becoming AI Driven is data scientists. They are in such short supply because it’s almost impossible to have all these skills - intimate knowledge of the domain, ability to write a lot of code, and know the ins and outs of Machine Learning algorithms.
- YASH: Even with an army of data scientists, this doesn’t scale. YASH: Hand coding is time consuming and difficult, and you need experienced data scientists to do this YASH: Consultants are expensive, and their long term motivation is to continue working with you YASH: Vendor models are narrow, and inflexible, and maybe not a great fit YASH: Even throwing money at all three of these doesn’t match the demand that just keeps growing JAY: There is no shortage of opportunities – it is really time and a race to tackle as many as possible. JAY: Even model deployment can be incredibly prohibitive – the bigger the team the harder that can get.
- JAY: Our vision is built AI-Driven Enterprise – where AI is applied in every business process to predict outcomes, offering unpresented scale, speed, and efficiency to compete.
- JAY: Automated machine learning allows organizations to scale to meet the need. This comes in a few a few ways. JAY: First – shifting data science teams to an automation first approach drastically increases throughput and second - increasing the number of people who can safely build AI solutions by leveraging automation
- JAY: Everyone is racing to leverage AI to improve demand forecasting --- Amazon is pushing the envelope. JAY: By forecasting demand for each product, down to the size/color/location, they are shifting how they operate to offer 1-hour deliveries. How much market share will that unlock? I’ve ordered slower pizza’s.
- Yash: With accurate demand forecasting on a per item level can potentially reduce food waste by not over/under stocking and save companies millions of dollars
- JAY: Nike’s CEO said in their last earnings call that they see demand forecasting as the path to achieving double-digit growth”. The last time I was involved in this use case they company estimated it was worth hundreds of millions. YASH: The opportunity is there – but the challenge is to act.
- YASH: Historically the throughput for AI projects has been prohibitive. With the emergence of automated machine learning, the limiting factor starts to shift back to execution. YASH: It is critical for the stakeholders to be aligned. Like with any activity, things happen when people work together to improve a process. YASH: For instance…