Beyond the Perfect Order Metric: DSRs and Shelf Level Collaboration @ ARC's 2011 Industry Forum
 

Beyond the Perfect Order Metric: DSRs and Shelf Level Collaboration @ ARC's 2011 Industry Forum

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Beyond the Perfect Order Metric: DSRs and Shelf Level Collaboration @ ARC's 2011 Industry Forum by Steve Banker. ...

Beyond the Perfect Order Metric: DSRs and Shelf Level Collaboration @ ARC's 2011 Industry Forum by Steve Banker.
GMA and the FMI Perfect Order Index (2009)
Percentage of Cases Shipped vs. Cases Ordered;
Percentage of On-time Deliveries;
Percentage of Data Synchronized SKUs;
Order cycle time;
Percentage of Unsaleables (damaged product);
Days of supply;
Service at the Shelf!
A Manufacturer’s Job is not done when the Goods arrive at the Retailer’s DC!

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Beyond the Perfect Order Metric: DSRs and Shelf Level Collaboration @ ARC's 2011 Industry Forum Beyond the Perfect Order Metric: DSRs and Shelf Level Collaboration @ ARC's 2011 Industry Forum Presentation Transcript

  • Beyond the Perfect Order Metric: DSRs and Shelf Level Collaboration Steve Banker Service Director, SCM ARC Advisory Group sbanker@arcweb.com
  • 2 © ARC Advisory Group The Perfect Order is Necessary But Not Sufficient GMA and the FMI Perfect Order Index (2009)  Percentage of Cases Shipped vs. Cases Ordered;  Percentage of On-time Deliveries;  Percentage of Data Synchronized SKUs;  Order cycle time;  Percentage of Unsaleables (damaged product);  Days of supply;  Service at the Shelf! A Manufacturer’s Job is not done when the Goods arrive at the Retailer’s DC!
  • 3 © ARC Advisory Group From Sell Into to Sell Through The Goal is to Improve Product Availability While Reducing Landed Costs
  • 4 © ARC Advisory Group In Theory, Sell Through is a Win-Win 31% 26% 19% 15% 9% Buy item at another store Substitue different brand Sustitute same brand (differentsize) Delay purchase Do not purchase item Hurts Retailer Hurts Manufacturer Hurts Both Hurts Both Corsten, Daniel and Thomas Gruen, “Stock-outs Cause Walk Outs” Harvard Business Review, 2004
  • 5 © ARC Advisory Group Downstream Data is Necessary DSRs Always Contain (by Definition): POS or DSD data DSRs Will Often or Usually Contain: Store & Retail DC Inventory Levels Retail DC Shipments DSRs Sometimes Contain: Wholesale Inventory Levels and Sales Syndicated Data Third Party Demographic Content Store Loyalty Data RFID Product Movement Events Store Policies Surrounding Ordering CG Company Marking Campaign Data Customer Panel Data Planogram and Store Layout Data Unstructured Brand and Product Data DSRs need applications – in the form of analytics, execution functionality, and optimization!
  • 6 © ARC Advisory Group DSR Applications are Cross Functional DSR Applications Definition Store Operations and Merchandising CG merchandisers or DSD teams, or third-party brokers hired by the CG company, utilize these solutions. One common application involves real- time alerts that suggest that a particular product, often a promoted product, is not on the shelf. There are also advanced predictive analytics that predict that phantom inventory exists. Supply Chain Management Consumer goods supply chain personnel use these applications for better demand forecasting and dynamic replenishment planning. Downstream data can also be used to drive transportation savings and warehouse capacity planning. Sales and Marketing Consumer goods retail account teams use analytics to understand their sales performance (year to date and versus last year), their profitability for the retailer (YTD and LY cost of sales), their service (must arrive by date), and inventory performance (sell in vs. sell through, which stores are out of stock), and how they are doing against other key retail partner KPIs. Category captains get this data for all SKUs, including competitors, in the category they manage. Marketing teams use applications in this area to calculate the after the fact profitability of different promotions. They can be used to calculate price elasticity curves, and to support scan-based trading.
  • 7 © ARC Advisory Group Causes of OOS Causes of OOS Solutions Retailer DC to store replenishment failures  Rare, ignore them. Manufacturing DC to Retail DC failures  Improve base level supply chain capabilities.  Improve forecasting by incorporating downstream data. Forecast store orders.  Network view of inventory that includes plants, manufacturing DCs, retailer DCs, stores.  Lean initiatives for factories to allow for quick changeovers and smaller lots.  Dynamic replenishment. Failure at store level to move inventory out of back room to the shelf  Simple analytics detect OOS. Send broker or store merchandising team into talk to store manager.  Account team calculates lost sales and provides it to district managers.  Consider moving to store deliveries (force outs) for key promotions. Failure at store level to build end caps  Detect using predictive analytics, send broker or store merchandising team into talk to store manager.  Save the information and share when negotiating future promotions.  Consider not including regions or store groupings in future promotions if they have a history of noncompliance. Phantom inventory  Detect using predictive analytics, send broker or store merchandising team into talk to store manager.  Account team calculates lost sales and provides it to district managers. Incorrect store replenishment settings  Account team uses advanced analytics to detect.  Situation discussed in weekly meeting with retailer replenishment team Increased Need for integrating the Supply Chain & Merchandising Teams!
  • 8 © ARC Advisory Group Not all Retailer’s Can Provide this Data For many Consumer Goods Companies:  Walmart represents over 20 percent of sales • They provide the best Downstream Data  The next four to seven of their top retailers represent from 40 to 60 of Revenues  Data is not sufficient, collaboration may also be necessary  Leading Retailers collaborate much more effectively with Category Captains
  • 9 © ARC Advisory Group DSR’s: Still an Immature Technology?  CG companies report that it is difficult to understand how to make this data actionable.  Data is often inaccurate or unreliable.  Suppliers offering a hosted SaaS architecture have significant architectual advantages in cleansing data.  Scaling up to a single global instance of a DSR is doubtful.  Several existing DSRs were constructed to provide quick analytics.  Some companies put downstream data in Business Warehouses that were not purpose built for this purpose.
  • 10 © ARC Advisory Group Thank You. For more information, contact the author at sbanker@arcweb.com or visit our web pages at www.arcweb.com