Stop Letting Out-of-Stock Hurt Your Bottom Line
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Learn 3 ways to use data to increase profits and avoid out-of-stocks.
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Stop Letting Out-of-Stock Hurt Your Bottom Line
- 1. Janet Dorenkott 440-899-3296 x225 janetd@relationalsolutions.com
- 2. Confidential - for limited circulation only Agenda • Relational Solutions/Mindtree Background • Case Studies • Identify Costly Replenishment Issues • Reclaim Lost Sales Dollars • Set Alerts to avoid Out-of-Stock • What Makes this so Difficult? • Go Beyond OOS • What to do Next!
- 3. Confidential - for limited circulation only Mindtree at a Glance 3 We engineer meaningful technology solutions to help businesses and societies flourish. $715M annual revenue 17,000 Mindtree Minds 42 offices 17 countries 65% US 25% Europe 10% APAC 22.5% YOY growth Top 7 IT company in India
- 4. Confidential - for limited circulation only Offerings 4 Solution Architecture & Enterprise Foundations Agile Application Development & Maintenance Application Maintenance ServicesManaged Services Project & Program Management CPG, Retail & Manufacturing Travel, Transportation & Hospitality Banking, Financial Services & Insurance Technology & Media Digital (e-commerce, Marketing, Mobile, Cloud) Data- warehousing & Integration ERP EAI (BPM, SOA) Engineering R&D Independent Testing Infrastructure Management Analytics & Insights (Predictive & Prescriptive) Mainframe & Midrange
- 5. Confidential - for limited circulation only Global Presence Basel, Switzerland Brussels, Belgium Cologne, Germany Dublin, Ireland London, UK Paris, France Solna, Sweden Vianen, Netherlands Europe Asia Dubai, UAE Kuala Lumpur, Malaysia Singapore Sydney, Australia Shanghai, China Tokyo, Japan North America Chicago, IL Cleveland, OH Dallas, TX Gainesville, FL Los Angeles, CA Minneapolis, MN New York, NY Ontario, Canada Phoenix, AZ Scottsdale, AZ San Jose, CA Schaumburg, IL Redmond, WA Warren, NJ Company HQ Delivery Center Delivery & Training Center India Bangalore Bhubaneswar Chennai Hyderabad Pune 5
- 6. Confidential - for limited circulation only #1 SAP HANA implementation partner #1 Duck Creek implementation partner Acquisitions in 2015-16 6 Leader in demand signal repositories, data integration & COE for analytics (predictive & prescriptive) Platinum Salesforce implementation partner
- 7. Confidential - for limited circulation only Our CPG Focus 7 Digital Marketing Demand Chain Management Predictive Analytics Trade Promotion Management and Post- Event Analytics Channel Collaboration Demand Signal Repository & Data Ingestion BI and Analytics Distributor Management Sales Force Automation CRM & Master Data Management
- 8. Confidential - for limited circulation only Agenda • Relational Solutions/Mindtree Background • Case Studies • Identify Costly Replenishment Issues • Reclaim Lost Sales Dollars • Set Alerts to avoid Out-of-Stock • What Makes this so Difficult? • Go Beyond OOS • What to do Next!
- 9. Confidential - for limited circulation only Aligned Shipments with Sales Improved Vendor Managed Inventory CPG Case Study: Demand Signal Repository 9 Sales Performance Tracking Velocity And Distribution Report Cross Retailer/ Cross Channel Reports Customer Profitability Analysis
- 10. Confidential - for limited circulation only Cross Selling
- 11. Confidential - for limited circulation only Providing Recommendations on Suggested Order Quantities Cloud enabled collaboration platform for 18,000 distributors in 22 countries Reduced order-to-cash cycle by 60% Savings $15M on store audits in 3 years 11 CPG Case Study: Winning the New Age Consumer
- 12. Confidential - for limited circulation only Unified Digital Marketing platform 70 unique brands 182 countries 1600 digital properties 40% cost savings 50% less time-to- market Consumer Goods Giant Engaging with Consumers Faster and Better Across all Digital Channels 12
- 13. Confidential - for limited circulation only Root Cause Analysis
- 14. Confidential - for limited circulation only The Perfect Store • The Rep is adding to the recommendations on our Suggested Order Quantity. • Learned process that ultimately results in the perfect store
- 15. Confidential - for limited circulation only Agenda • Relational Solutions/Mindtree Background • Case Studies • Identify Costly Replenishment Issues • Reclaim Lost Sales Dollars • Set Alerts to avoid Out-of-Stock • What Makes this so Difficult? • Go Beyond OOS • What to do Next!
- 16. Confidential - for limited circulation only Lowes Walgreen’s Costco Shipments Other POS data EDI/FF/txt/Access, etc Wholesaler/Dist IMS Submit Reports Most time spent gathering disparate data •G ather ing •Cleaning •Integr ating •Justifying ? Inconsistent reports lead to questionable decisions Kroger/Market 6 Wal-Mart, Shiloh Retail Link, EDI/AS2 ? ? ? ? ? ? What Makes Analytics So Difficult? Kmart IRI Nielsen Third Party Data NPD Weather DunHumby RSI EDI
- 17. Confidential - for limited circulation only • G a t h e r i n g • C l e a n i n g • I n t e g r a t i n g • J u s t i f y i n g Why POS Data Cleansing? Property of Relational Solutions, Inc. By Janet Dorenkott June 2015 Big Data, Omni-Channel & New Data Sources End User Requests Change Data Frequency is Inconsistent Data Formats Vary From Source to Source You Are at the Mercy of Retailer Decisions Constantly Changing Conditions POS is Not Always Available & May Cost Money Everyone Wants to See Data Differently Different Sources? Different Reliability Every Retailer Provides Different Data Elements Lack of Integration & Manageability
- 18. Confidential - for limited circulation only Every Retailer Provides Different Data Elements Date Time Units Price Store # In Transit OH Inv OH Qty In Warehouse On Order 9/16/2015 08:12:00 150 $3.20 #442 30 20 10 20 50 Date Time Units Store # 9/16/2015 10:14:06 122 #210 Retailer A Retailer B
- 19. Confidential - for limited circulation only Aligning Different Week Start & End Dates Monday Tuesday Wednesday Thursday Friday Saturday Sunday Sunday Monday Tuesday Wednesday Thursday Friday Saturday Saturday Sunday Monday Tuesday Wednesday Thursday Friday
- 20. Confidential - for limited circulation only Different Sources, Different Formats EDI 852 EDI 867 SAP Retailer Portals AS2 Oracle TXT SQL Excel CSV How Do I get all these sources into 1 common database type? FTP Access
- 21. Confidential - for limited circulation only Different Sources, Different Reliability Missing Data EDI Inconsistencies UPC Issues Portal is Down Re-Casts • Data was supposed to come in from CVS today! Where is it? • RetailLink is down! • Walgreens is padding UPC codes in the front with 2 digits! • Food Lion is padding UPC codes in the back with 1 digit! • Kroger wants us to buy data from Market 6! • Dollar General was missing 2 days from last month! • Target EDI came in 4 hours late!
- 22. Confidential - for limited circulation only Different Retailers, Different Hierarchies, Different Vernacular Internal Category Brand Sub-Brand Item Product # Retailer A Category Sub- Category Brand Product # Product ID Sku Retailer B Category Sub- Category Item # Item Name Sku
- 23. Confidential - for limited circulation only Frequency of Feeds Vary EDI Daily Syndicated Data Monthly ERP Data Every Second Currency Conversion Daily Daily POS Once a Month RMA Annually Plan Data When Available Weather Trends Quarterly
- 24. Confidential - for limited circulation only Constantly Changing Market Conditions New Competitors New Retailers Trends New Laws Acquire a Company Economy Management Direction National Conditions Natural Disaster New Corporate Direction Retailers Partners Company switches data providers The company adds a new data source Retailer Contracts New CEO
- 25. Confidential - for limited circulation only You Are Subject To Retailer Decisions Make Portal Changes Start Sending Inventory New POS Source Category Focus Changed New Buyer Update Contract Restrictions Policy Changes More Data Offered Opened New Stores Acquired Another Retailer
- 26. Confidential - for limited circulation only End User Requirements Change I Need to see Target compared to Walmart Include Shipments in this Report How do I compare my sales with plans? We just bought new demographic data I need to calculate potential impact I upgraded our Nielsen contract We switched from SAP to Oracle I want to compare this with weather trends I want to compare POS from the retailer with POS from IRI
- 27. Confidential - for limited circulation only POSmart Consistency • Easy Access to Information • Improved Productivity Streamline Data Gathering Synchronize, Integrate & Validate POS Wal-Mart/Shiloh EDI /AS2/Retail Link Promo/Forecasts Costco Nielsen/Spectra/RSi Shipping Kmart Kroger IRI/Mkt6/NPD EDI, AS2 Walgreens Wholesaler/Dist Home Depot Productive Meetings Integration & Harmonization
- 28. Confidential - for limited circulation only Agenda • Relational Solutions/Mindtree Background • Case Studies • Identify Costly Replenishment Issues • Reclaim Lost Sales Dollars • Set Alerts to avoid Out-of-Stock • What Makes this so Difficult? • Go Beyond OOS • What to do Next!
- 29. Confidential - for limited circulation only Military, Distributor Budget 3rd Party GL Forecasts Reports Scheduled & Cached Shipments SourcesRetailers WalMart CVS Costco Kroger/DunH Meijers Sam’s Distributors POSmart Integrate, Validate, Synchronize & Manage BlueSky Over 200 KPI’s Or Other BI Tool DW POS Integrator BIS EDI, Text, Flat Files, Access, Retail Link, Partners SAP, JDE, DB2, Oracle, JDA Users access via web 3rd Party Data: AC Nielsen/IRI Spectra/NPD, Market6, RSi Forecast Shipments, Promo, Vendor TradeSmart POSmart “Smart” Architecture OtherTPM BlueSky Analytics Power User BlueSky Viewer Casual Users or BlueSky XL Users Control Center
- 30. Confidential - for limited circulation only Deployed RSI TradeSmart- Post-event analytics solution 6% sales increase 1% profit gain Go Beyond POS Integration: Post event Trade Analysis to Optimize Spend 30 Promotion Execution Baseline Sales Uplift & ROI Analysis Optimized Spend Allocation Cannibalization & Halo Effect Analysis Pantry Stocking/Post Event Dip Trade Promotion Optimization
- 31. Confidential - for limited circulation only Agenda • Relational Solutions/Mindtree Background • Case Studies • Identify Costly Replenishment Issues • Reclaim Lost Sales Dollars • Set Alerts to avoid Out-of-Stock • What Makes this so Difficult? • Go Beyond OOS • What to do Next!
- 32. Confidential - for limited circulation only Next Steps: Complete Our “Getting Started” Guide Coordinate a Demo for your Peers Start with key retailers and syndicated data providers Automate standard reports Start Providing new Insights Predictive Impact & Prescriptive Recommendations
- 33. Confidential - for limited circulation only Follow our Relational Solutions Training Blog www.relationalsolutions.com/blog Join Demand Signal Repository Institute Group Follow Relational Solutions Connect with Janet Dorenkott and Karen Kurtzweil Watch our Training & Video’s on Relational Solutions Channel Follow @POSmartBlueSky & @JanetAtRSI Like Relational Solutions Contact Janet Dorenkott 440-899-3296 x225 Janetd@relationalsolutions.com Contact Karen Kurtzweil 440-899-3296 x235 Karenk@relationalsolutions.com Stay Connected & Informed!
- 34. India | USA | UK | Germany | Sweden | Belgium | France | Switzerland | UAE | Singapore | Australia | Japan | China
Editor's Notes
- Client food company with products ranging from peanut butter and jelly, coffee beans, cake mix, cooking oil and even pet food (a recent acquisition). We built their Demand Signal Repository for their VMI team. We worked with a wide range of data sources including direct downstream point-of-sale retailer data, EDI VMI data, multiple IRI databases, Promotion data from Siebel, as well as internal master data, shipments and order data. The data sizes are in the terabytes. They needed to know things like How do our sales align with shipments? What are our inventory turns? How does that compare with our competition? Where are your items selling? Are there geographies that sell your products better/worse than other geographies? What drives that? Are you growing or shifting volume? How does your price compare across geographies/channels and are you competitive? They needed to understand On-shelf Availability Tracking OSA OOS Report Predicted Out Of Stock Phantom Inventory & Zero Scans Root Cause Analysis
- This retailer had a need to help their retailers understand what would sell. Cross Selling – We Use “R” (open source predictive analytics model/scripts). It looks at one store and the stores around it to see if it accurately reflects the products/proper assortment, mix and volumes. It takes into account store size and what various retailers are selling. For example, Target is carrying a product and selling $100K/month, but Walmart, right down the street, isn’t even carrying it. A rep could use this information to tell Walmart that they are losing business. For this calculation to be made, cross reference is needed. Whose forecast drives production? Did you make enough product to keep up with demand? Is there an opportunity to introduce a complimentary item to stores to grow sales ? Suggested Order Quantity (SoQ) Providing a SoQ will allow smarter orders to achieve optimal sales The SoQ can be calculated using consumption data and ESR, and enhanced further with the inclusion of shipment and order data. (comparing suggested to actual and benchmarking achievement rates) Use SoQ to feed the CPG mfg Ordering systems Cross Sell Identifying the best items to merchandise or promote together for the highest sales impact Identify what other items a store has a high propensity to sell from CPG mfg that are not currently selling in the store but in the market
- This customer had a Replenishment project that needed to understand Order issues And Safety Stock requirements What they Must Stock Shrinkage And Unsaleables Demographics Based Assortment They wanted us to offer Suggested Order Quantities. We can suggest quantities for any item across the globe based on either POS, Shipments, or Orders. We can also provide seasonal information. SOQ in advance tells you, based on history and information leading up to the event, what should be ordered. Seasonal SOQ is Predictive. In order to calculate this, we use historical sales data and sales data leading up to the event.
- This customer had a need for Demand Forecasting In this case We Uses historical sales data to determine future demand & to better project inventory needs They needed to know At what price do sales fall off? At what price do sales accelerate? What is the optimal price in each geograhpy/channel? Does your product sell better/worse during certain seasons or weather events or social media events? What is influencing sales?
- If your product is not on the shelf at the retail store it has no opportunity to sell. Our solution enables you to track lost sales even without counting on retailer on hand data. We compare expected sales to actual sales and send alerts when stores are not living up to expectations and an out of stock is likely. Root Cause Analysis Put the logic/decision tree in place to distinguish between upstream supply, mfg distribution centers, retailer distribution centers, 3rd party distributors, and store level or demand issues that lead to an out-of-stock occurrence. This logic can begin with consumption data but will be more robust once additional data is included. We also do something called Virtual On Shelf Availability (OSA) We don’t rely on the retailer inventory information to calculate this, especially for DSD (direct store delivery) manufacturers. We have an algorithm that learns. OOS is based on the estimated sales rate. We compare what shipped to what sold.
- The Perfect Store – This is the analytics we use to determine whether the Rep is implementing the SOQ that we are recommending. This is a learned process that takes into consideration our recommendations and what was actually ordered by the rep ordered in the field. Here’s what we recommend, here’s what you did, here’s the result. How many stores are authorized to sell your items? How many are selling? Are their stores that based on sales rate or other variables be eliminated from your product distribution list? Is the POG integrity kept or are there items that have been placed in the wrong location that take space away from items the consumer is looking for? Perfect Store The best mix of product assortment, placement, space and price by store. Scorecard that measures how a store is performing vs KPIs (compliance to SoQ, cross sell achievement…) Store Clustering Grouping the stores based on common store and/or demographic characteristics. Clusters can be built from sales performance, size, type, demographics, etc. One or more of the above store characteristics can be used to build clusters. The clusters will help determine the best mix of merchandise and the assortment quantities.
- This slide depicts the data dilemma that consumer goods companies face. The data is there. You get it. You might even have the impression you’re getting too much data. But that isn’t the case. The issue is having that data in a usable and reliable format. The data comes in from so many different sources that it’s impossible to get the full value out of it. Most companies are using it just to produce the reports they need. They just don’t have the time to create new insights even though the data is there. Each one of these source provide information and potentially reports. But analysts spend an average of 80-90% of their time gathering and pulling together data. Inevitably, there are discrepancies in the reports and analysts are then stuck spending more time trying to figure out where the numbers came from. Not only is this a very tedious task but when people’s numbers don’t match, there is a lack of confidence in the data and rightfully so. But it wastes not only the analysts time but it also leads to wasted time on managements part when they argue over who’s data is right and could potentially lead to costly and incorrect decisions. On top of that since 90% of an analysts time is spent gathering, cleaning, integrating and justifying their reports. This is time that keeps analysts away from actually getting to analyze the data and gain new insights. This is why our enterprise solution offers companies a very fast ROI.
- This slide depicts the data dilemma that consumer goods companies face. The data is there. You get it. You might even have the impression you’re getting too much data. But that isn’t the case. The issue is having that data in a usable and reliable format. The data comes in from so many different sources that it’s impossible to get the full value out of it. Most companies are using it just to produce the reports they need. They just don’t have the time to create new insights even though the data is there. Each one of these source provide information and potentially reports. But analysts spend an average of 80-90% of their time gathering and pulling together data. Inevitably, there are discrepancies in the reports and analysts are then stuck spending more time trying to figure out where the numbers came from. Not only is this a very tedious task but when people’s numbers don’t match, there is a lack of confidence in the data and rightfully so. But it wastes not only the analysts time but it also leads to wasted time on managements part when they argue over who’s data is right and could potentially lead to costly and incorrect decisions. On top of that since 90% of an analysts time is spent gathering, cleaning, integrating and justifying their reports. This is time that keeps analysts away from actually getting to analyze the data and gain new insights. This is why our enterprise solution offers companies a very fast ROI.
- POSmart streamlines the cleansing and integration of all these various data sources and puts them into a common repository where the data is clean and reliable. It’s also in a format that’s designed for easy reporting. Reports can be automated to send to users and power users also have an easy to access database where they can do ad-hoc querying. The result is far more productivity both from the analysts position as well as more productive management meetings. In addition, you have more access to reliable information than you’ve ever had. This data will help you know more about your business, provide more value to your management, understand your customer’s better and provide more insights to generate more profits.
- The POSmart architecture is open. The data base is independent and can reside on Teradata, Oracle, DB2 and most others. If you can imagine everything on the left side of the white line exists at all our customers. This is just a sample of data sources, but you get the concept. On a nightly basis our BlueSky Integration Studio looks for new data. Any new data is then brought into our staging area where we cross reference the retailer data with your internal data, apply business rules, we align internal data with external data and we load the data into the POSmart data model where end users can easily access and query the data. Reports that were scheduled to run are then cached on the server so when end users come in in the morning, all their reports are already there and waiting for them. In addition, analysts are able to create new reports.
- CPG companies spend as much as 20% of sales funding trade promotion activity. We generate baseline sales to understand lift on promotion as well as evaluate total cost to achieve that lift to uncover ROI for both the retailer and CPG company. Do all stores need a secondary display location when on promotion? Do all stores achieve incremental sales on promotion? Post Promotion Analytics Allows analysis of trade spend ROI Aligns trade funds with consumer events Trade Promotion Optimization Takes Post Promotion Analysis further with predictive analytics which identifies optimal promotions to run The client estimated that implementing a post event trade spend analysis tool would increase sales by a MINIMUM of $15 mil/yr (0.32% of gross sales), which translated to a $2.5 mil/yr increase in profit gain (or 0.054% of gross sales), easily covering the cost of the implementation in the first year. The above ended up being a very conservative estimate. The realistic estimate of sales increase was 6% ($276 mil/yr), which would proportionately translate to $46 mil/yr in profit gain (or 1% of gross sales). Promotion Execution Baseline Sales Uplift & ROI Analysis Optimized Spend Allocation Cannibalization & Halo Effect Analysis Pantry Stocking/Post Event Dip Trade Promotion Optimization How are consumers shopping? Do you have a standardized calendar and KPIs to evaluate your total business using all available retailers? Are you seeing volume shifts across channels? Does that align with your strategy/spend/efforts? What is your total cost to serve a retailer? Are you and the retailer both profitable?
- So please join us next Wednesday, July 16th for our 2pm Demo. You can register by going to our website and clicking on events. You will also receive a follow up email with a way to register as well. In that demo we will cover POSmart and TradeSmart as well as PromoPro, baselines, explain why shipments are needed in more detail and go over some TradeSmart Case studies.
- We would like to thank you all for joining us today. We invite you to follow our Relational Solutions training blog by going to our website at relationalsolutions.com. We also suggest you join the Demand Signal Repository Institute on LinkedIn. You can also see more training video’s on our Relational Solutions, YouTube channel. We ask that you follow Relational Solutions and Janet on Twitter, LinkedIn and Facebook and connect with us on LinkedIN. You are also free to contact us via phone and email. Thanks again for joining us and we look forward to your participation next Wednesday!