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!

1. Beyond the Perfect Order Metric:
DSRs and Shelf Level Collaboration
Steve Banker
Service Director, SCM
ARC Advisory Group
sbanker@arcweb.com

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© 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!

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© ARC Advisory Group
From Sell Into to Sell Through
The Goal is to Improve Product Availability
While Reducing Landed Costs

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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

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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!

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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.

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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!

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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

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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.

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© ARC Advisory Group
Thank You.
For more information, contact the author at
sbanker@arcweb.com or visit our web pages at
www.arcweb.com