| 词条 | Demand sensing |
| 释义 |
Traditionally, forecasting accuracy was based on time series techniques which create a forecast based on prior sales history and draws on several years of data to provide insights into predictable seasonal patterns. However, past sales are frequently a poor predictor of future sales. Demand sensing is fundamentally different in that it uses a much broader range of demand signals (including current data from the supply chain) and different mathematics to create a more accurate forecast that responds to real-world events such as market shifts, weather changes, natural disasters, consumer buying behavior etc. History and limits of traditional forecasting{{unreferenced-section|date=June 2016}}The cornerstone of traditional forecasting is based on the Fourier series time series mathematical analysis conceived by Joseph Fourier in 1822. Fourier statistical modeling uses a historical data series to create seasonal forecasts and set the course of forecasting for the next 125 years. In 1957, Holt-Winters took time series analysis to a new level with exponential smoothing. In the 1980s, low-cost computing paved the way for larger and more complex time-series models and Moore’s law continues to fuel the trend of increasingly sophisticated models in the pursuit of refining forecast accuracy. There remains, however, a ceiling for time series forecast accuracy. A ceiling governed not by processing power and memory, but rather fundamental limitations imposed by information theory and the fact that historical data does not reflect current events or market conditions.
Adding current dataBreaking this ceiling requires the inclusion of current demand signals from throughout the supply chain and new mathematics to sort through the masses of data and determine what is predictive. There is no shortage of near real-time data collected by manufacturers in their supply chain and it grows exponentially, once retailer data is included. According to a McKinsey & Company report, “Manufacturers can improve their demand forecasting and supply planning by the improved use of their own data. But as we’ve seen in other domains, far more value can be unlocked when companies are able to integrate data from other sources including data from retailers, such as promotion data (e.g., items, prices, sales), launch data (e.g., specific items to be listed/delisted, ramp-down plans), and inventory data (e.g., stock levels per warehouse, sales per store). By taking into account data from across the value chain (potentially through collaborative supply chain management and planning), manufacturers can smooth spiky order patterns. The benefits of doing so will ripple through the value chain, helping manufacturers to use cash more effectively and to deliver a higher level of service. Best-in-class manufacturers are also accelerating the frequency of planning cycles to synchronize them with production cycles. Indeed, some manufacturers are using near-real-time data to adjust production.” This last sentence refers to demand sensing solutions. For more information see McKinsey Global Institute's report, "Big Data: The Next Frontier for Innovation, Competition and Productivity." [2] References1. ^Jane Barrett, Michael Burkett, Hussain Mooraj, Gartner, July 15, 2010 “Supply Chain Strategy for Manufacturing Leaders: The Handbook for Becoming Demand Driven.” 2. ^James Manyika, Michael Chui, Brad Brown, Jacques Bughin, Richard Dobbs, Charles Roxburgh, Angela Hung Byers, McKinsey Global Institute, May 2011, “Big Data: The Next Frontier for Innovation, Competition and Productivity.” 2 : Demand|Supply chain analytics |
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