Report Details: The quantitative research for this report was conducted via an online survey from April 4 - May 26, 2017. Surveys were conducted among manufacturers who use and are familiar with downstream data (n=55). Objective: To understand how companies are using specific types of downstream data and how it shapes business outcomes. Highlight: There is untapped opportunity in the use of point of sale data -- it will differentiate leaders from laggards by improving on-shelf availability and lowering the levels of inventory write-offs. The barriers lie with change management issues involved with process redesign.

1. The Power of Downstream Data
Understanding the Value Proposition
for Using Channel Data
07/25/2017
By Lora Cecere
Founder and CEO
Supply Chain Insights LLC

2. Page 2
Contents
Research Methodology
Disclosure
Executive Summary
Research Insights
Impact
Recommendations
Summary
Appendix
Additional Related Research
About Supply Chain Insights LLC
About Lora Cecere
3
4
5
8
10
11
11
12
16
17
17

3. Page 3
Research Methodology
Our goal is to be the first place that business leaders turn to in order to understand the future of
supply chain. We write for visionaries. This report is based on insights from ten years of interviewing
companies using channel data (downstream data) in the supply chain, and recent results from a study
on the use of downstream data. An overview of the quantitative study is shown in Figure 1.
Figure 1. Study Goals, Objectives, and Overview of the Methodology
The definition of downstream data used in the survey is shown in Figure 2. In this study we evaluated
the impact of the use of syndicated data (common suppliers of syndicated data in the Consumer
Products industry are IRI and Nielsen) versus the use of retail point-of-sale data received directly
from a retailer.

4. Page 4
Figure 2. Definition of Downstream Data Used in the Study
Disclosure
Your trust is important to us. In our business, we are open and transparent about our relationships. In
this research process, we never share the names of respondents, or give attribution to open-ended
comments collected during the research.
Our philosophy is, “You give to us, and we give to you.” We collect data from a private network of
qualified participants and openly share the results. The participants of our research always receive
the final reports. We also share insights from the studies with the respondents of our quantitative
surveys through a virtual roundtable discussion among respondents.
This report is written and shared using the principles of Open Content research. It is intended for you
to read and share freely with your colleagues, and through social channels like LinkedIn, Facebook
and Twitter. When you use the report all we ask for in return is attribution. We publish under the
Creative Commons License Attribution-Noncommercial-Share Alike 3.0 United States and our citation
policy is outlined on the Supply Chain Insights Website.
.

5. Page 5
Executive Summary
The first capture of point-of-sale (POS) data by a retailer was in 1988. Three decades later, POS is
available for 55% of the retail channel for food and beverage manufacturers, but few use it to drive
supply chain decisions.
Instead, each silo within an organization uses different forms of demand data. The supply chain team
uses order and shipment data. The sales account team uses POS data for reporting, assortment
planning & category management, and retail execution. In contrast, the marketing department relies
on syndicated data. Each form of demand data moves at a different cadence, level of granularity and
accuracy.
Figure 3. The Long Tail of the Supply Chain (The Area in Green Represents the Long Tail)
Many organizations think that POS and syndicated data are comparable data forms. They are not.
Syndicated data has less granularity and more latency than POS, but a bigger difference is that they
are out of sync on time. Syndicated data is usually three weeks later than the cash register sale. The
longer the tail of the order pattern within the supply chain (reference Figure 3), the more that this
becomes an issue. The products on the long tail of the supply chain are usually the most important to

6. Page 6
the growth strategy, i.e. new product launch, special promotions, and custom displays. The longer the
tail, the more important it is to decrease demand latency and use channel data (POS and retail
warehouse withdrawal information).
POS data also helps in providing unique assortments and responding to localized demand. In Figure
4 we share an example of market demand for an allergy product distributed by an over-the-counter
drug manufacturer. The blue bars represent historic average order demand profiles, while the colored
lines represent regional POS demand patterns. Note the regional swings. These geographical
patterns are not clear when looking at historic demand averages.
Figure 4. Regional Demand Patterns

7. Page 7
So what is the value proposition for the use of channel data in consumer-facing supply chains? In this
research we find three strong value propositions:
• Improves Demand Sensing and Replenishment. With slowing growth, companies do not want to
miss a potentially lost sale. Simulation work with Supply Chain Insights’ upcoming game SCI
IMPACT! shows that the use of POS data for long-tail products improves replenishment by 60-
70%.1
There are fewer out-of-stocks.
• Guides Decision-Making for Localized Assortment. When it comes to providing excitement in
the store, and driving localized and specialized assortment, there is no substitute for the use of
POS data. Broad-brushing markets and not responding to consumer patterns is an opportunity cost
for the business leader.
• Sell-In for New Product Launch. New products have a demand error of 70% and a bias of 35-
40%. As a result, the product forecast is a poor indicator of what will sell. Initial shipments are also
not a good signal. The reason is simple. Initial shipments represent pipeline fill. Without a good
forecast, or a good signal of historic shipments, POS data aligns the organization on new product
launch fulfillment.
1
Game play of SCI Impact!

8. Page 8
Research Insights
Retail data is available, but its use requires knowledge of the data elements, and professional
services expertise for harmonization and synchronization. In our study, the average company
received channel data, including POS, from 14 retailers. While there is a perception that getting POS
data is a barrier, companies in the study had 55% coverage of the retail channel in terms of revenue.
Large retailers—Walmart, Target, Amazon and Sam’s Club—are the most willing and able to share
channel data. Shown in Figure 5 are retailers sharing channel data with the respondents in our study.
Figure 5. Retail Channel Data
Synchronizing demand data requires a Demand Signal Repository (DSR). We have not interviewed
any company successful in building their own. In the study 45% of respondents consolidated demand
data into a common database (a DSR), and 29% overall consider the initiative effective. The gap for
most companies is master data synchronization. In the selection of technologies, the vendor’s
knowledge of retail data is an important factor for success.

9. Page 9
Figure 6. Consolidated Database for Retail Channel Data
Figure 7. Barriers for Use of Downstream Data

10. Page 10
When asked, the respondents’ biggest barriers were data not being shared, dirty data, and the lack of
clear processes for usage. (While the perception is that data is not shared, we find that 55% of the
retail channel is shared.) Since most processes are inside-out (dependent on enterprise data) there is
no clear integration path into outside-out flows. As a result, POS data flows into the sales account
teams, but not into other vertical silos within the organization. These barriers are shown in Figure 7.
Impact
As shown in Figure 8, the use of POS data makes a substantial difference in retail execution,
inventory management, and improving on-shelf availability. The differences are great; yet we know of
only a handful of companies that have successfully implemented a DSR and used POS data to align
sales, marketing and the supply chain. Most organizations have many stops and starts.
Figure 8. Differences Between Retail Point-of-Sale and Syndicated Data for Retail Execution
One of the most successful is The Hershey Company. We believe that this is one of the driving
factors in Hershey’s outperforming their peer group in the 2017 Supply Chains to Admire Analysis.

11. Page 11
Recommendations
As companies think about market data, and driving business leadership, we share three
recommendations:
• Get Clear on the Data Types. Make demand data actionable. Align the organization to build
outside-in processes and synchronize demand data across sales/marketing and supply chain.
Educate the team on the differences of data types and test the impact of POS data on retail
execution.
• Select Technologies Based on Knowledge of Channel Data. Most companies fail at the
implementation of a custom DSR. Partner with a company with a deep knowledge of channel data
and drive insights from new forms of analytics. As you work this strategy, recognize that the
synchronization of demand data should not be considered a data lake. Demand data is active data.
• Learn to Speak the Language of Demand. Synchronization is not integration. Charting the latency
of demand signals, based on the long tail of your supply chain, yields great insights on the
difference. Recognize that an order is a poor demand signal and that demand is about much, much
more than forecasting. (For additional insights on speaking the language of demand, check out this
blog post.)
Summary
Point-of-sale data availability now spans three decades; yet the use of the data for cross-functional
decision making remains an opportunity. As markets become more complex, the use of channel data
will differentiate leaders from laggards. The opportunity is a 50-70% improvement for on-shelf
availability, and lower levels of inventory write-offs through improved organizational alignment. While
the technology has matured, the larger barriers lie with change management issues involved with
process redesign. It remains an untapped opportunity.

12. Page 12
Appendix
In this section, we share the demographic information of survey respondents, along with relevant
research findings to support the key insights shared in this report.
Our philosophy is that “respondents give to us and we give to them.” All respondents participating in
this survey will be given the results of this study and invited to share in a roundtable discussion with
other survey participants to gain additional insights.
In our research, the names, both of individual respondents and companies participating, are held in
confidence. The demographics and additional charts are found in Figures A–F. At the bottom of each
image are the specific questions asked in the survey along with the survey details.
Figure A. Overview of Respondents

13. Page 13
Figure B. Respondent Profile by Industry
Figure C. Respondent Title, Role and Familiarity with Downstream Data

14. Page 14
Figure D. Retailers by Volume Sold
Figure E. Types of Data Used

15. Page 15
Figure F. Number of US Retailers Sending Retail Channel Data

16. Page 16
Additional Related Research
Supply Chain Insights regularly publishes reports. Unlike other industry analyst groups—who keep
research behind a paywall—we share research openly to help all global supply chain leaders. All of
the research is archived in our community on Beet Fusion, for social sharing on SlideShare and on
the Supply Chain Insights website. To gain an understanding of supply chain excellence, check out
this related research:
In Search of Supply Chain Excellence
Driving Improvement Through Supply Chain Centers of Excellence
Supply Chains to Admire 2015
Supply Chains to Admire 2016
Supply Chains to Admire 2017

17. Page 17
About Supply Chain Insights LLC
Founded in February 2012 by Lora Cecere, Supply Chain Insights LLC is beginning its fifth year of
operation. The Company’s mission is to deliver independent, actionable, and objective advice for
supply chain leaders. If you need to know which practices and technologies make the biggest
difference to corporate performance, we want you to turn to us. We are a company dedicated to this
research. Our goal is to help leaders understand supply chain trends, evolving technologies and
which metrics matter.
About Lora Cecere
Lora Cecere (twitter ID @lcecere) is the Founder of Supply Chain Insights LLC and
the author of popular enterprise software blog Supply Chain Shaman currently read
by 15,000 supply chain professionals. She also writes as a Linkedin Influencer and
is a a contributor for Forbes. She has written five books. The first book, Bricks
Matter, (co-authored with Charlie Chase) published in 2012. The second book, The
Shaman’s Journal 2014, published in September 2014; the third book, Supply
Chain Metrics That Matter, published in December 2014; the fourth book, The
Shaman’s Journal 2015, published in September 2015, and the fifth book, The Shaman’s Journal
2016, published in September 2016. A sixth book will publish in September 2017.
With over 14 years as a research analyst with AMR Research, Altimeter Group, and Gartner
Group and now as the Founder of Supply Chain Insights, Lora understands supply chain. She has
worked with over 600 companies on their supply chain strategy and is a frequent speaker on the
evolution of supply chain processes and technologies. Her research is designed for the early adopter
seeking first mover advantage.