Vision Chain

A Real NoWHERE Man?

by Lora Cecere on January 4, 2012 · 3 comments

The briefing starts with the statement, “We are a DSR provider.”  I then ask, “What does that mean?” And the fun begins….

In 2005, Kara Romanow (now at Consumer Goods Technology (CGT) http://consumergoods.edgl.com), and I coined the term Demand Signal Repository (DSR). We were colleagues at AMR Research.  After the original report was published, it seemed like EVERYONE  declared that they were a Demand Signal Repository (DSR) vendor.  They came out of the woodwork.  So much so, that I wondered if many of them had ever READ the original definition.  I often laugh when I got to a conference and look at the DSR signage.  The term is EVERYWHERE; but for me, the realization of the original concept is NO WHERE.  In the words of the old Beatles song, I feel like I am a Nowhere Man. Remember the words?

He’s a real nowhere man,
Sitting in his Nowhere Land,
Making all his nowhere plans
for nobody.

Doesn’t have a point of view,
Knows not where he’s going to,
Isn’t he a bit like you and me?

Nowhere Man please listen,
You don’t know what you’re missing,
Nowhere Man, the world is at your command!

The Beatles

The original definition of the DSR was broad. A demand signal repository is an enterprise software application that cleanses, synchronizes, harmonizes and uses multiple forms of demand data to improve enterprise decision making.  The original writings included the statement that it was to include all types of demand data:  structured and unstructured data, point of sale transactional and syndicated data, ratings and reviews, and sentiment data.  However, as the technology providers have used the definition, I am afraid that it has become a new way to spiff-up their old marketing.  Not all companies using the term DSR are “enterprise applications”, and the development of the software to “use” downstream data has been disappointing.

The Good News

In December, I uncovered two technology advancements that give me hope.  Here I share my insights on these market shifts and give advice to the buyer of demand sensing software.

SAP and HANA:    SAP is finally getting into the DSR space. The push is to build a data model  on their HANA architecture. They are being very deliberate.  I am pleased to see the building of a DSR to be one of the HANA sponsored projects at the board level.    I am also pleased that they have announced a partnership with NetBase.  NetBase is a listening technology for multiple forms of social data.  It is a Software as a Service (SaaS) technology that visualizes 52 weeks of data from Twitter and Facebook.

Recommendation:  I am glad to see SAP move in the direction of demand sensing and translation, but I feel that SAP’s work on the Demand Signal Repository will take many years.  The cycle of ERP enterprise application development is much SLOWER than the cycle for sentiment analysis.  As a result, experiment with NetBase or other sentiment/listening tools (Bazzarvoice, Clarabridge, Netezza’s Sentiment Appliance, or SAS) outside of the SAP HANA environment first and work with SAP on co-development of the DSR strategy.

As you work with sentiment and listening tools, use these technologies to move from a marketing-driven to a market-driven process. Harness the power to listen to customer feedback with little data latency.  I find it sad that most listening projects are in the digital marketing sectors of the business. Harness the power.  Use these technologies to build outside-in listening processes that tie back to quality, R&D, and customer service. 

Vision Chain and Market Execution:  I was also pleased to see Vision Chain’s new product development last week. I am excited about two new developments:  predictive analytics for market sensing (market execution) and a mobile application on the iPAD for sales teams to work with the data to improve shelf availability.  This has been a long time coming, but is a step in the right direction.  The predictive analytics has been developed in conjunction with Matt Waller of the University of Arkansas.

Recommendation:  I think that Vision Chain’s predictive analytics module for shelf sensing should be on the implementation roadmap for all Vision Chain clients.  I also think that the mobile application makes the data more relevant and offers the potential to streamline store audits. 

Also, be wary, with SAP introducing a DSR strategy themselves, Vision Chain’s partnership with SAP will weaken. 

The SO What?

For the DSR market to ever move out of the NOWHERE MAN state that it is in right now, we have to help companies USE the data.  Both of these announcements are steps in this direction.

The SAP Net Base announcement will accelerate the consideration of enterprise listening tools within the installed base of SAP.  It will push the question of how to best integrate structured and unstructured data for demand sensing.  However, the complete answer will take time. The applications that use demand data will have to be redefined (e.g. trade promotion management, demand forecasting, replenishment/VMI), but companies can take baby steps now to use sentiment data in enterprise processes to more quickly respond to customer feedback.

The Vision Chain announcement will start a chain reaction for other “DSR” vendors to launch predictive analytics and mobile applications.  As this happens, challenge the vendors to define “predictive analytics” and test the depth of the solutions in a bake-off.  However, when the dust settles.  This will be a good thing for all.

In summary, I am tired of being a NOWHERE MAN.  I want this market to move along.  I want to see companies move from a marketing-driven to a market-driven approach.  Social for the sake of social is a bright and shiny object that dulls quickly in the halls of digital marketing.  Sales teams and supply chain teams are frustrated that they are surrounded by data that they cannot use.  I want demand data to help companies to build strong outside-in processes to drive true customer-driven supply chains.  These are steps in the right direction. However, users need to push their vendors for quicker answers. Let’s hope that we see more and more….

So, what do you think? I look forward to getting your response.

For more on the usage of Downstream Data check out these blog posts:

http://www.supplychainshaman.com/supply-chain-excellence/trends-i-am-watching/

http://www.supplychainshaman.com/new-technologies/more-than-just-a-f-word/

http://www.supplychainshaman.com/altimeter-group/user-in-the-era-big-data-supply-chains/

http://www.supplychainshaman.com/downstreamdata/three-things-i-have-learned-about-using-downstream-data/

http://www.supplychainshaman.com/demanddriven/a-leap-of-faith/

http://www.supplychainshaman.com/demanddriven/start-a-new-conversation-free-the-data-to-answer-the-questions-that-you-dont-know-to-ask/

http://www.supplychainshaman.com/new-technologies/is-this-the-future-of-downstream-data/

http://www.supplychainshaman.com/supply-chain-excellence/crossing-the-great-divide/

 

Is this the Future of Downstream Data?

by Lora Cecere on March 29, 2010 · 5 comments

It started as simple sales reporting. It is no longer simple.  In 2005, there were five consumer product leaders actively using downstream data (retail inventory and sales data).  Today, over 80% of consumer products companies greater than 1 billion in revenue are redefining work processes to use it.  The space is murky. It is not easy, and it is evolving. However, the deeper teams explore the usage of the data, the more excited they get.  This has been FUN to watch.

One of the things that you get to do as an analyst is name things.  We get to put OUR stamp –usually a cool three-letter acronym—on new technologies, processes and trends.  (At least, we think that it is cool.)  I am one of the mother’s of the Demand Signal Repository (DSR) term. It was originally defined by Kara Romanow, now Executive Editor at Consumer Goods Technology (CGT). I was an early collaborator with Kara.  It took shape and form from my fingertips as I wrote about the usage of downstream data in consumer value networks.  I have followed the market for the past six years.  Kara and I like to joke, that we were both mothers in the genesis and market acceptance of the term DSR.

A DSR is a repository of demand information. Demand information comes in many different forms—orders, shipments, syndicated data, point of sale information, warehouse withdrawal information from retailers, customer panel groups—and needs to be used by many different roles within the organization—sales for account reporting, marketing for promotional/new item acceptance and market share analysis, supply chain for forecasting and out-of-stock sensing, and R&D for new product launch insights.  The problem is that everyone wants it in a different form—frequency, granularity, attributes—and the data processes of cleaning, harmonizing and synchronizing are messy requiring a strong understanding of the data.

Help me Get it Right

So, as one of the mother’s—some would even disdainfully say “muther“—of the term DSR, I have been thinking about the evolution of this market and the market drivers, and I wanted to get some community input.  I am writing a report on market evolution and wanted to get community input on the evolution of the technologies.  (One of the exciting aspects of Altimeter Group’s research model is open research with the community.)  So, I welcome your feedback, do you think that these are the right trends?

Right Trends?

2010 will be the year of predictive analytics.  The DSR is not the end state.  The value of downstream data is the USAGE of the data into new business processes/work streams.  Whether it is Vision Chain’s new sensing of out-of-stocks, or Terra Technology’swork on short life cycle product sensing.  2010 is the year of predictive analytics.  Look for new applications to evolve.  I predict exciting launches in the area of price and promotion compliance, market basket analysis, shopping patterns, damage, and category analytics.  I find the convergence of loyalty data, point of sale information and geo mapping technologies very exciting to give live representations of market out-of-stocks, customer demand, and near real-time sensing of market trends.

In parallel, we will see market convergence of the technologies.  There are just too many sales reporting applications in the market, and predictive analytics vendors will need a database structure to enable insights from harmonized, disparate data sources (E.g. Nielsen’s TDLinkx product, orders, shipments, syndicated data, point of sale, warehouse inventory levels, retail inventory levels, etc).

It won’t just be about modern trade.  Hopefully, by now, it is clear to everyone that there has been a step change in data sharing by retailers in North America and Europe.  No, not everyone is sharing data; but, the data that is being shared gets more significant, and better quality, each year.  Customers that are working at getting the data now have  70% of North American and 30% of the European grocery markets.  (The secret is knowing how to ask for it.)

As companies use downstream data for modern trade, they will use the techniques to build demand networks to tackle the challenge of emerging markets.  The bullwhip effect of distributor relationships is just too painful. Consider the differences in table 1 for the food industry.

Table 1:  Bullwhip effect

Supply Chain Type Demand Latency from Shelf to Order Order Cycle Time Manufacturing Cycle
Modern trade for warehouse distribution 10-14 days 3 days 10-20 days
Emerging markets 40-48 days 1 day 30-40 days
Food service 24-35 days 2-3 days 30-40 days

 

Leaders will build distributor networks (similar to Anheuser’s Budnet) as part of the infrastructure to capture market share in the evolving markets.

Differentiation will come from enrichment and the design of the information layer.  To serve multiple roles and to enable new discovery, the secret sauce is the design of the information layer.  It is not a traditional Master Data Management (MDM) problem. Instead, it requires flexible data assembly and quick data parsing.  I believe that we will see the use of Search Engine Optimization (SEO) technologies like Endeca evolve to help companies solve this problem. (I saw some interesting evolutions of this concept in India past week.)   In parallel, new content—store demographics, in-store shopping data, panel data—will evolve.  These two elements will enable the true POWER from the new predictive analytics.

SAAS, License, Cloud.Initial forays into the usage of downstream data will be deployment neutral:  a true toss-up between license and Software as Service (SaaS) models.  However, as data enrichment and advanced predictive analytics evolve, the DSR will come behind the firewall. Similarly, as social media turns into social commerce, an information layer will evolve in the cloud for the value chain.  This value chain information layer will enhance not replace the enterprise DSR.

Figure 1: Downstream Data Evolution

Social Commerce will Power the Tipping Point.  As power shifts from the retailer to the shopper, social media technologies will power social commerce business processes (the ability to buy, return, shop on mobile devices based on reviews, price and inventory levels). Channels will blur as we enter the hype cycle of social commerce.  To power these applications Point of Presence (where is the shopper) will combine with Point of Sale (POS) to track the success of social CRM. The amplitude of the hype cycle will be immense—a mini dot.com era—but, it will be the tipping point for the usage of downstream data and the design of outside-in value network.

 Evolution. Do I have it Right?

So, what do you think? Do I have it right? Will it look like figure 1?  Let me know your thoughts.  Your input will serve as foundational input to refine the models for the report that I am writing on downstream data technologies in August.

Until then….  The Supply Chain Shaman is happy to be back from a very fruitful trip to India, and will be busy with research and advisory calls this week on the east coast of the United States.