Data driven forecast

For data driven forecast, it is suggested to apply statistical forecast models, which will generate good forecast accuracy results, and will also automate the forecasting calculation, saving demand planners time to devote to more complicated and/or variable SKUs. 

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. 

Figure 3.6A and B Data Model for Market-Driven Forecasting Data... 

Introduce S OP processes based on a strong demand-driven forecasting process that focuses on data and analytics to sense demand signals, and to shape and to translate demand to create a more accurate demand response. 

The last case is when a product has low demand variability, and in this case, a data driven statistical forecast should be applied, as it will allow capture the benefits of a push system. The approach described above brings light to help define when a company should be demand driven or forecast driven. Based on Croxton et al. (2002), it is proposed to expand the matrix to also include the tools and approaches that can be used in each one of the three situations, as detailed and illustrated in Fig. 4.4. 

If data of the real system is available, the developed simulation model can be tested for similarity with the real system in a qnantitative way (bottom-right cell in Table 4.8). For this purpose, a lot of statistical procedures can be applied depending on the specific object to be tested. Typically, regression techniqnes, distribution tests, or time series analysis methods are used. A reliable qnantitative approach is to generate a forecast of the near future by means of the simulation model which is then compared with the real systems behaviour after the forecast period has expired. This is called predictive validation A mixture of trace analysis and fixed-value test is the trace-driven simulation where a historical situation is simulated. The model s output is compared with the historical records then. 

Today, too few companies are thinking about the use of social data to improve the demand forecast to become market driven. In interview after interview with pioneers, we have been looking for this change but so far, it has not happened. What is the difference between a market-driven and marketing-driven approach It is distinguished by three elements ... 

Christopher (2000) also states that to be truly agile, a supply chain must possess four distinguishing characteristics, being one of them Market sensitive, which means that the supply chain is capable of reading and responding to real demand or being demand-driven. The problem is that most organizations are forecast-driven rather than demand-driven. In other words, because they have little direct feedforward from the marketplace by way of data on actual customer requirements, they are forced to make forecasts based on past sales or shipments, and convert these forecasts into inventory. 

The key issnes of snpply chain as discussed in this chapter emphasizes a need for a total snpply chain management approach. With the expansion of outsonrcing and Internet driven e-supply chain, it is essential that key players and stakeholders nnderstand the importance of the accnracy and transparency of data for collaborative management for mutual benefits. Improved forecast accuracy and the real-time exchange of data not only reduces the bullwhip effect , but also reduces processing cost, inventory level and improves cnstomer service. We have also discnssed the trend towards the service-based economy and the... 

At the heart of the customer order management system is a requirements plan that is market-driven. The inputs to this plan include data and information relating to enquiries and orders, price changes, promotional activity and product availability. This information provides the basis for the forecast that then drives the requirements plan. Alongside this is a process for the fulfilment of current orders. They are not separated but closely integrated through the information system. 

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