Demand forecasting is a technique for estimation of possible future variants of a phenomenon or an object based on past demand (van der Laan et al., 2016). The forecasting creates a base for planning logistics, finances and flow of products. Its main function is to enable the business to plan future needs and consequently make rational decisions. In addition, the demand forecasting provides the information regarding the volume of products, place and the time horizon in which they will be needed. Therefore, businesses use different types of systems and tools to accelerate the flow of raw material, beginning with the supplies to modification of the products and its distribution. Collaborative forecasting is one of the most common methods followed by retail and manufacturing businesses (Fliedner, 2003).
The concept of collaborative forecasting emerged in the 1900s’ in the most recognizable form of collaborative planning and replenishment (Goodwin, 2015). It aims to enhance logistics by supporting and assisting the joint parties. This type of forecasting involves Vendor management inventory (VMI) and continuous replenishment programs (CRP). Collaborative forecasting makes it possible to take advantage of the expertise of all, or at least several, supply chain members.
One benefit that is suggested to follow from this is a reduced reliance on historical records (Sari, 2008). Through collaborative forecasting, a business or the logistics department can get access to better information on important demand drivers, such as promotions. This makes it possible to complement conventional forecasting methods like time series forecasting with regression analysis. This helps in examining the relationship between sales and other variables, such as advertising. It also helps with subjective forecasting, which relies on expert opinion.
Collaborative Planning Forecasting and Replenishment model (CPFR)
The most visible undertaking and the igniting spark of the current collaboration boom are the Collaborative Planning Forecasting and Replenishment (CPFR) process model developed by the Voluntary Inter-Industry Standards (VICS) association (Fliedner, 2003). CPFR links sales and marketing practices to operational planning and execution processes to increase availability while reducing inventory, transportation and logistics costs. Wal-Mart and Warner-Lambert incepted the CPFR model in the 1990s’ (Sari, 2008). The model comprises of:
- Strategy and Planning.
- Demand and Supply Management.
According to the model, members of the organization and third parties work in collaboration to satisfy the demands of the end customer, who is at the center of the model (Goodwin, 2015). For instance, in the retail industry, a retailer typically fills the buyer role, a manufacturer fills the seller role, and the consumer is the end customer. The strategy and planning segment helps to establish the ground rules for the collaborative relationship. This segment of the model allows determining product mix and placement and developing event plans for the period. In the next stage, forecasting of demand and supply and sales remains created; exceptions or discrepancies remain identified and resolved.
The CPFR process improves forecast accuracies by having customers and suppliers participate in the sales forecast. Tying the buyer and seller together so that their goals are compatible. In the execution stage, the order is generated, shipments are prepared and delivered, products are received and stocked on retail shelves, sales transactions are recorded and payments are made (Goodwin, 2015). In the last stage of analysis, monitor planning and execution activities for exceptional situations are conducted. If a discrepancy occurs, the two trading partners can get together and share insights and adjust plans to resolve the discrepancies.
Challenges for demand forecasting
There is always a challenge accuracy of forecasting. Demand forecasting is never 100% accurate and therefore using the 4 step CPFR model helps mitigate the challenge of relying completely on forecasted values. The demand planning has no one size that would fit all scenarios. Every business and every product has a different demand, that too changeable with time and market (Goodwin, 2015; Fu, 2016). Strategy and planning stage allows identifying the demand for every scenario and time. Since this stage comprises of determining product mix and placement for a specific market at a specific time, the challenge of steadiness and implementation can be tackled using the CPFR model.
Sales fluctuation at a different time of the year is one of the biggest challenges for good forecasting and demand planning initiatives. Using the 4 steps of CPFR model the exceptions or discrepancies of sales and demand fluctuations can be assessed allowing efficient planning of product logistics and manufacturing (Fu, 2016). Estimating the fluctuations helps in the efficient preparation of shipments to customers.
For instance, the CPFR initiative used by Wal-Mart has helped to share its forecasted data with its suppliers to improve coordination in the supply chain thereby resulting in effective collaboration. The company used the CPFR model to help its partners and suppliers to forecast effectively in times of fluctuating demands. Walmart and P&G partnered to jointly forecast sales of P&G products at Wal-Mart stores and then jointly plan replenishment strategies. This collaboration ensured that there is no gap between what Wal-Mart plans to sell and what P&G plans to produce.
- Fliedner, G. (2003) ‘CPFR: An emerging supply chain tool’, Industrial Management and Data Systems. doi: 10.1108/02635570310456850.
- Fu, H. P. (2016) ‘Comparing the factors that influence the adoption of CPFR by retailers and suppliers’, International Journal of Logistics Management. doi: 10.1108/IJLM-10-2014-0168.
- Goodwin, P. (2015) ‘Collaborative Planning, Forecasting, and Replenishment’, in Wiley Encyclopedia of Management. doi: 10.1002/9781118785317.weom100245.
- van der Laan, E. et al. (2016) ‘Demand forecasting and order planning for humanitarian logistics: An empirical assessment’, Journal of Operations Management. doi: 10.1016/j.jom.2016.05.004.
- Sari, K. (2008) ‘On the benefits of CPFR and VMI: A comparative simulation study’, International Journal of Production Economics. doi: 10.1016/j.ijpe.2007.10.021.
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