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 (different size)

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 DataThird Party Demographic Content

Store Loyalty DataRFID Product Movement Events

Store Policies Surrounding OrderingCG Company Marking Campaign Data

Customer Panel DataPlanogram 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 OOSCauses 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 Needfor 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 atwww.arcweb.com