How AI Integrates S&OP and Trade Promotion via a Unified Demand Signal

1. How AI Integrates S&OP and Trade Promotion via a Unified Demand Signal

2. Agenda • Introductions • What is AI? • Using a Unified Demand Signal • Enhancing A Unified Demand Signal Approach with AI • Closing Thoughts / Q&A

3. What is Artificial Intelligence?

4. What is Artificial Intelligence (AI)?

5. Artificial Intelligence is… It’s big data It’s machine learning It’s robots It’s magic

6. AI is the ability of machines to perceive, learn, formulate and react independently to external stimulus in an effort to maximize successful outcomes – just like us!

7. Why is AI more relevant today? It’s the coming together of cloud technology and hardware, data availability(Big Data), talent availability, open source, connectivity and the democratization of knowledge.

8. Where is AI impacting business change? Direct to consumer models “Amazonization” Changing consumer expectations Better selection, convenience and price offerings Service level pressure from retailers Example of Walmart OTIF in the US Leveraging AI to PREDICTand SHAPE demand is imperative to SUCCESS

9. Top Areas of Analytics Focus “Within machine learning, the best areas of investment are demand forecasting, replenishment, pricing and trade promotion, since [the technology] can help read patterns of information in a self-learning manner as well as provide predictive data and scenario-based modeling of outcomes.” Sahir Anand – Managing Vice President, Research & Strategy for EnsembleIQ Research Solutions 36% 13% 23% 27% 19% 34% 36% 21% 8% 31% 25% 25% 28% 28% 6% 28% 41% 25% Consumer Goods Retail

10. Using a Unified Demand Signal for Consumer Goods Companies

11. Business Challenges • Build a centralized solution for store ordering and trade management • Leverage a unified demand signal for product production, distribution, route delivery and trade effectiveness • Connect sales and supply chain teams to integrated planning for store ordering and effective trade promotions

12. 30% improvement in forecast accuracy 2-3% Revenue Improvement from reducing lost sales due to stock outs 8-10% cost reductions from a decrease in returns and salvage from over-stocks ~5% margin improvement identified via reinvesting trade promotions to more effective brand/channel/time delivery Improvements to trade strategy through identification of top five drivers of promotional effectiveness (e.g. price was not the most significant lever) Business Successes

13. AI Enables the Concept of a Unified Demand Signal Demand ApplicationsDesigned for integrated planning and decisioning Machine Learning Input host data, generate forecast & event driver uplift Store Fulfillment External events data Trade Promotion Financial Forecasting POS Supply Chain Planning Unified Demand Signal Internal data Assortment Planning

14. Integrated Planning Tears Down Traditional Silos between Supply Chain Planning and Sales/Revenue Managers Trade Management Plan, budget, and execute trade promotions that influence dispatch and route delivery Store 1 Store 2 Sales Centers Production Center Sales Production Managers Supervisors Route Service Professionals Sales & Rev. Managers Planning of raw material purchases and production Planning of dispatch and logistics Deliveries, order entry, payments, inventory capture Inform route delivery and promotion execution Measure the effectiveness of the promotion and inventory clearance (TPR) events Store 4 Store 3 Trade Analytics

15. Enhancing a Unified Demand Signal Approach with Artificial Intelligence

16. Predicting demand is only half the story we need a precise understanding of the drivers to shape demand Step 1: Predict demand Step 2: Shape demand

17. Step 3: Leverage an AI platform that connects the science, human decision, and technology for a powerful experience Technology Approachable and repeatable means of engaging with the analytics AI Driven Unified Demand Signal Science Human Improve forecast accuracy, leverage ML, and optimize best outcomes Promote right behaviour, empower decision making, align stakeholders

18. Step 1: Create a foundational forecasting framework Manage new products and customers Identify events and outliers Determine demand driver coefficients Create base demand history Decompose demand history Arrive at optimal level to forecast Use self-learning models to maximize accuracy Analytically disaggregate forecast to lowest consumption level Forecast basedemand

19. Step 1: Arrive at optimal levels to forecast accurately Leverage base demand history • Automatically parse through the hierarchy to determine the right balance of information loss and noise reduction (dynamic data aggregation) • Perform dynamic data aggregation for every customer/location/SKU combination Dynamic Data Aggregation 3rd aggregation level (Product Category, Country) Benefits of AI Approach 2nd aggregation level (Product Group, State) • Accurate base demand forecast • Minimal information loss • Outcome: right hierarchy levels to perform forecasting 1st aggregation level (Product, City) Lowest level (SKU/ Store)

20. Step 3: Evaluate the value of each demand driver separately to understand the impact Trend Seasonality Understanding demand driver impacts • Product Lifecycle • Seasonality • Trends • Market Entry • Holidays • Promotions • Price changes • Inventory Clearance Total Demand Events Price Effects Promotions Lifecycle * For Illustration only. Not based on real data

21. Step 3: ‘True’ lift is determined and confidently shared with supply chain, demand planning, and others to close the loop Cannibalization Lift from Buy one, get one Lift from Holiday event Wastage Wheat bread @ Walmart Demand Volume Base demand Out of stock Demand lag due to pantry loading Halo Bagel @ Walmart Week 1 Week 2 Week 3 Week 4 Week 5 (White Bread @ Walmart Example) Time

22. The result is better visibility to volume uplifts and ROI trade-offs across brands and channels that inform future investment strategy Volume Lift Across Brands/Channels ROI Across Brands/Channels

23. The ability for the solution to surface key drivers of effective promotions in an unsupervised manner B Brand 2 for 3 Promotion Offer BOGO Machine Learning algorithms give order of importance of variables for promotional effectiveness by identifying the most important variable achieving the strategic goal Banner C B A D 160% 130% 300% Incremental Volume Uplift 90%

24. Future planning becomes easier as AI predicts the best outcome by brand, banner, attributes, and budgetary constraints 500% The ability to conduct scenario analysis or what-if planning empowers users to not only make strategic decisions but plan with greater confidence as all outcomes are weighed simultaneously against goals and constraints. 400% 300% Uplift % Legend 200% Brand B Banner D 100% Banner C 1.0 0 1.5 2.0 2.5 3.0 ROI

25. Remember our bread example, this unified demand signal enabled greater forecast accuracy, less wastage, and better ROI

26. How are you leveraging AI in your organization? Allow this example of how traditional silos are broken by leveraging a unified demand signal to integrate plans that encourage how AI drives changes your organization

27. About Antuit Company Facts What we do Thank you • Founded in 2013 • Operating globally with over 250+ professionals in 8 countries • Backed by Goldman Sachs & Zodius Capital • Deliver IP-enabled solutions that unlock the value of people, process and technology investments • Leverage deep domain knowledge in CPG, retail, manufacturing and logistics, and other demand-driven industries • Create long-lasting value for more than 60 global brands