Channel data, demand

Market-driven demand management utilizes data from market and channel sources to sense, shape, and translate demand requirements into an actionable demand response bidirectionally from market to market. A true market-driven forecast is an unconstrained view, or a best estimate of market demand based on channel data. Demand shaping is based on campaigns to combine price, new product launches, trade and sales promotions and incentives, advertising, and marketing programs to impact what and how much customers will buy. 

The company has championed process innovation. In 2003, P G introduced the term customer-driven supply chain to the market. It also pioneered top-to-top meetings with retailers, championed barcode product adoption, and automated store checkout in the late 1980s. In 2002, it built one of the first demand signal repositories to use channel data to sense demand. 

One of the little-known facts about the success of Apple and Dell is the conscious design of their supply chains to use daily channel data daily. When they launched their new business models, they designed the processes to use daily demand data to build outside-in processes. 

Downstream data The use of channel data to sense and shape demand. This can include data for sales at the point of transaction, sales through distributors, inventory in channel trading partners warehouses, and demand insight data. 

A forecast is not a forecast is not a forecast. As companies work on demand architectures, they will find that they have multiple forecasts—sales forecast, financial forecast, production forecast, supply chain forecast, and procurement forecast—each with a different data model, granularity, and bias. As a result, tight integration is not a good idea and the so-called one-number forecast is not realistic. Instead, as companies work through the issues, they will find the need to model market demand in a ship to or channel data model, and translate this demand to a ship from data model. The sales forecast then becomes an input into the corporate forecast, and this corporate forecast becomes the input into the financial forecast. These concepts require education and are often a major change management issue. 

Data. Working with data is a challenge for all. In traditional, supply-centric processes, the most common data input is customer orders. The second most common data source comes from customer shipments, or replenishment data. While market data—point-of-sale (POS) data and channel shipments—is growing in frequency and availability, it is not being effectively used today in 95 percent of organizations. Ironically, in the consumer products industries, POS data has been available for 32 years, but fewer than 10 percent of companies use channel data to drive their demand forecasts. 

When companies launch new products, they can now use action buttons to sense the market response to their new product launches. These data can then be used to build forecasting models. For example, if a fan in a company s loyal demographic wants the new product, it is a powerful causal factor to put into a demand forecast. If the majority of consumers hate the product, it is probably time to rethink the product build plan. The value is the speed at which a company can access this insight. Instead of a 7- to 14-day latency to get channel data, the company is able to see the end consumer s response in near real-time (1 to 2 days). 

Evaluate the fit of the data model to use channel data. Investigate integration of downstream data, such as retail POS data, to provide a better source of true demand. Synchronize data inputs. Embrace new forms of channel data to drive innovation. 

Sense. Reduce demand latency through the use of channel data. Use this redefinition to build outside-in processes to sense and shape demand. 

Many experimenters have adopted the practice of feeding a preformed mixture of steam and water to their test sections, either out of interest in this type of system or else to avoid the power demanded by long channels. The CISE Laboratories in Italy have produced a considerable amount of data of this kind (S4), and a typical example of their results is shown in Fig. 13. The curves have a characteristic swan-neck shape similar to the Russian data for unstable flow conditions shown in Fig. 9, and the burn-out flux values are generally below those for normal steady-flow conditions. 

Recursive procedures demand special attention to flow control and data structures. For instance, the flow control within the procedure must correctly handle an error, say missing file information or inconsistent data, that is discovered several iterations deep. Should the procedure break and return to the previous level only Should it force return to the level of the original invocation of the procedure Should it allow an interactive user a choice of supplying missing data, and if so, on any level of iteration If files are opened within the procedures, should they be closed when a recursive call is needed, or are new channel numbers to be requested, using up system resources ... 

The analysis and experimental procedures are very demanding in obtaining accurate cross-section data and there are particular problems in obtaining unique cross sections (see Huxley and Crompton (1974) and Kumar (1984) for details). If only the elastic channel is open the momentum-transfer cross section can be obtained reliably and accurately in some cases (e.g. He, 2%). With the addition of inelastic channels the uncertainty in the derived cross sections due to lack of uniqueness increases. 

The most often discussed application of SPAs is optical interconnection. All applications of these arrays involve optical interconnection in one way or another, but the term optical interconnection most often refers to optical data channels between electronic processors and devices. This includes data transfer between machines, racks, boards, and chips. SPAs are being developed for optical interconnection at the board and chip level where the demand is very high for the dense packaging of the electronic and optical devices. Figure 2 illustrates two of several envisioned configurations for board-to-board and chip-to-chip optical interconnection. 

In interviews, no supply chain leaders in either company could imagine running operations without daily demand data. The insights to be gained from updated channel views are too important to managing a dynamic supply chain. 

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