Industrial Engineering, Supply Chain Management, Demand Planning Methodology, Winter Model, Forecasting. 1. Introduction
1.1. Variability of Demand:
Demands for any products changes rapidly from period to period, often due to predictable influence. These influences include seasonal factors that affect products, as well as non-seasonal factors (e.g. Promotional or product adoption rates) that may cause large, predictable increases and decline in sales. Predictable variability is change in demand that can be forecasted. Products that undergo this type of change in demand cause numerous problems in the supply chain, ranging from high levels of stockouts during peak demand periods to high levels of excess inventory during periods of low demand. These problems increase the costs and decrease the responsiveness of the supply chain. Supply and demand management have the greatest impact when it is applied to predictably variable products. Supply chain can influence demand by using pricing and other forms of promotion. 2. Demand Planning Methodology
2.1. Demand Planning activities:
· Changing Base Demand Forecast
· Adding Impactors.
How can we measure the variability today:
Data Evolution Report:
This report gives a week to week view change of the plan, also broad view of supply key figures changes. This report need to be extracted to be analysed on a more aggregated level. These may be · SKU (Stock Keeping Unit)
· Factory line
2.2. Cleaning History: Creating Base-line Demand
The Demand Planning process is based on the concept that the final Consensus Plan consists of two components: 1. Baseline or Base Demand
2. Activities and Circumstances
The baseline Demand is the expected volume of a product if it is not promoted and no exceptional circumstances influence sales.
Activities are generated internally through Marketing and Sales and are related to TTS (Total Trend Spend) or PFME (Product Fixed Marketing Expenses).
Circumstances are internal or external and include stock outs, cannibalization, listing – delisting, competitor activity, unusual weather conditions, etc.
The possible reasons for a FMCG company to push markets to adopt this fundamental concept are: · a better control on the efficiency of promotions and the related spends · to improve the Demand Plan Accuracy (DPA)
This concept will allow the use of statistical forecast methods to forecast the baseline. 2.2.1: General Aspects on Cleaning History
The Cleaned Base History (CBH) is used for statistical forecasting: The output of the cleaning process is the Cleaned Base History. These numbers are the single input for the Statistical Forecasting process. The process of cleaning is a mandatory step before applying Statistical Forecast methods. Cleaning is linked to how we forecast:
Considering that cleaning is mainly carried out for the purpose of Statistical Forecasting, the process needs to be closely linked to the Statistical Forecasting process. E.g., a statistical...