Written by 8:18 am Demand, Downstream data, Market-Driven

It Is Just You and Me, Dude. The Secret Is Safe. Promise.

At the end of the presentation today, it happened. At break, after sharing research on the principles of becoming market driven, I was relaxing with my coffee when I heard a person softly say, “I am sorry to be so dumb, but I don’t think that I understand the concepts of becoming market driven … or the differences between market driven and demand driven. It is probably me, and I hate to ask it in the group, and I would certainly hate to APPEAR in your blog tomorrow, but can you explain it ONE more time?”

It is ok. It is just you and me, dude. I will not share your name publicly. Since so many people have the same question, I thought that it would make a good blog post. I have done it in the form of an open letter.

Dear Gnarly Dude:

First of all, there is no such thing as a ”dumb question.”

It takes courage to ask tough questions, and I appreciate it. These concepts are not easy.  In fact, it took me eight years of research. So, please don’t apologize. It is ok. 

Let’s start with the difference between market-driven versus marketing-driven processes. In the old-fashioned, conventional organization, functional processes are usually marketing driven. Marketing hones what they think is a brilliant message and broadcasts the message to the crowd through media tactics. The marketing group tightly controls the message to build brand. It is hard to change because the marketing organization driving a marketing-centric program has worked over the last two decades. Change is tough.

Contrast this to a market-driven company, where you are serving the customer by listening, testing and learning. It is not about control. Instead, you understand that the crowd has wisdom to share and you want to listen. You want the supply chain to be designed to drive unique assortments and to reliably respond to changes in demand. To do this, the supply chain is designed to sense, learn and then respond. Today’s conventional supply chains only respond, and the design of the systems usually gives us a “fairly dumb response” based on history.

Additionally, in a marketing-driven company, good news happens fast. When a product is selling and marketing is meeting the business objectives, everyone is quick to grab a beer and do a toast. I am sure you have a lot of T-shirts in your closet from these launches. However, when the sales are not at plan, the news travels slowly in the organization and there is often a lot of denial. As a result, organizations are usually struggling to write off SLOB (slow and obsolete inventory).  A discussion with marketing about SLOB is never a good thing.

So, what is the difference between a market-driven and a demand-driven value network? A demand-driven value network senses demand with minimal latency to drive a near real-time response for demand shaping and demand translation. In this network, the bullwhip effect is minimized using channel data.  Contrast this with a market-driven network that builds on the demand-driven concepts. It takes it one step further. Being demand-driven is a prerequisite to be market driven. In a market-driven value network, the use of market data is used to orchestrate trade-offs market-to-market ( buy- and sell-side markets or channel to supplier trade-offs) through the use of advanced analytics in horizontal processes to orchestrate demand and supply decisions based on analysis of profitability, mix and volume against the business strategy.

Why do we need to change? It comes down to good business. In most companies, growth is stalled. Traditional marketing tactics are not as effective as they used to be. Power has shifted to the shopper. Companies today are unable to drive profitability, and manage inventory cycles, while absorbing the complexity of a rapidly changing product mix. The traditional supply chain is designed to support high volume, predictable items in known markets. When things change, it cannot adapt.

So, if you buy the argument, here are some steps to take. The first step is the building of an architecture to match customer attributes to product attributes. Think about these concepts:

  • Building of Listening Posts and Actively Listening and Learning from Consumer Data. Most of this is unstructured  data—Facebook, Twitter, Ratings and Reviews, and Blogs—which requires the deployment of sentiment and text mining applications. These technologies are new, and the process evolution to support the use of the data is evolving. The first step is to set up a cross-functional team to review this data weekly and then start to use the data in conventional processes (e.g. rating and review data into forecasting as a causal factor for new product launch, discussions on true customer sentiment to drive the decision of how much to make on the second production run after launch, or market receptivity to a new promotion, etc.)
  • Design of Outside-in Processes. The use of channel data—point of sale, warehouse withdrawal, basket and retail partner perpetual inventory data—to understand channel flows and improve demand sensing. When companies are market-driven they use channel data to drive a pull-based response while actively designing push/pull decoupling points to maximize flexibility while minimizing costs. This channel data is archived in a system of reference, often termed a Demand Signal Repository, for reuse. Using cognitive reasoning engines and advanced optimization, unique insights radically improve the response.
  • Rethinking Planning.  This channel data is then used to drive planning. Demand planning models are based on attribute logic. So, as items change, the new item is forecasted based on profiles of the history of products with like attributes.
  • Embrace Test-and-Learn. Actively design in vitro test-and-learn scenarios based on carefully designed testing based on market data. Use test and control markets to adopt assortment and demand shaping activities. Use new forms of analytics to learn from channel sales. Build a supply chain to support this type of agile response.
  • Use New Forms of Analytics to Drive Demand and Supply Orchestration. Traditional supply chains respond to volume-based pulls based on orders and shipments. The data are stored as an item sold, at a location, based on volume. In market-driven value networks, companies actively use optimization and predictive analytics to match price and demand shaping activities market-to-market. In horizontal processes, like revenue management and Sales and Operations planning (S&OP), new forms of analytics are actively used to make trade-offs of mix, profitability and volume. Commodity markets are too volatile for this to be a passive process. For example, if a product has a high commodity cost, or is difficult to manufacture with unsure reliability, the company would question if this is the right product to promote or market. The orchestration of demand to supply evaluates the price elasticity of market pricing against the commodity risk in sourcing and the reliability of processes to deliver. Let me give you an example. During the recession, there were two competing breakfast cereal companies. Each had the option to promote either cereals with corn or wheat. It was proven that consumer sentiment was equally disposed to either product. Corn was skyrocketing in cost and one cereal company orchestrated demand and supply and decided to promote a line of wheat products. The other company promoted corn-based products based on history. The company that promoted wheat-based products gained market share and managed profitability. The other company reported a serious decline in earnings.
  • Alignment of Functional to Corporate Metrics. Focus on the Use of New Forms of Analytics in Horizontal Processes. Market-driven companies understand that the most efficient supply chain is not the most effective. They actively design the supply chain based on the probability of demand and the uncertainty of supply. It is clear that the complex trade-offs of the supply chain cannot be made in spreadsheets. As a result, they model the potential of the supply chain by analyzing the trade-offs of growth, profit, cycles and complexity in new forms of analytics that support the horizontal processes of revenue management, Sales and Operations Planning (S&OP), supplier development and corporate social responsibility.

So, gnarly dude, I put you at the head of the class. You listened intently in this morning’s session, and you asked wonderful questions. I wish you well in your market-driven journey. I cannot wait to write your case study.

Sincerely yours,

The Shaman