Demand Planning - Essence of Forecasting - Part I

This is my second article on SCM in this blog, addressing the issues related to Forecasting. Forecasting has been considered as one of the top supply chain issues in the globalized world. Organizations are striving to predict customer demand as accurately as possible. Accurate forecasting kick-starts demand and supply chain planning What is a forecast ? In layman language, Forecast is a statement or inference about the future usually based on historical data. Decision can be taken based on intuition or guts feelings also. In general, the decisions with supporting evidence are widely accepted. Why Forecast ? Can't we do away with Forecast ? Is Forecast necessary Evil ? Let us study the selling pattern of two computer manufacturers ie., Dell and Wipro to understand the forecasting pattern. Dell is selling the computers directly to the customers based on their indent. The customer specifies the machine configuration and make the payment through online. Based on the users inputs the computers are configured and shipped to the customers directly as per agreed delivery schedule say 3 weeks. In this case, Dell is reacting in response to the customer demand (“Pull”) , need to keep meager quantity of components (inventory) at their disposal. Delivery schedule to the customer is decided by Dell based on lead time in getting the components from other manufacturer, Assembling and delivery time to the customers. This is an example of make to order with zero lead time as the customer is willing to wait for finished product. This model requires minimal forecast. In Wipro, the customers pick up the computers from the retailers and hence they need to keep different configuration machines. Wipro need to know in advance, the model (desk top, laptop etc) and geography (state, area, town) wise estimated volume (quantity) to be sold for a particular month, to keep the finished stocks ready at their end before beginning of the period. Wipro reacts in anticipation of customer demand (“Push”). This model requires lot of forecasting effort as they need to ensure that the plant/factory is having the capacity to produce the forecasted (estimated) volume for the month and also to ensure availability of components to produce the saleable goods. Benefits of Forecast ¨ Increase customer satisfaction ¨ Reduce stock-outs ¨ Schedule production more efficiently ¨ Lower safety stock requirements ¨ Reduce product obsolescence costs ¨ Manage shipments better ¨ Improve pricing and promotion management ¨ Negotiating superior terms with suppliers Forecast Techniques Qualitative :- They are primarily subjective. Rely on Judgment, opinion, intuition, comparative techniques or surveys. It is non-scientific nature and hence difficult to standardize or validate for accuracy. This type is used in case of Marketing intelligence Quantitative : - Uses Time Series ie., set of evenly spaced or continuous numerical data which is historic in nature, where the basic demand pattern varies little between years. It assumes the factors influencing past will continue in future. It uses statistical models as forecasting tools. Casuals : - Uses the relationship between demand and some other factor (Sales Promotion, discount, Retailer incentive, income, population etc.) to develop forecast. Correlation method, Regression and econometric models are used in this technique. Types of Forecast Short term Forecast – This is for short period say 2 to 3 months basically used for operational or production planning. Since the short term forecast drives business on an ongoing basis and has a direct impact on the financial performance of an organization. Ideally, Sales team is responsible for short term forecast. Long term forecast – It is for the period 1 to 2 years and it is meant to meet the financial obligations. Marketing team is responsible for long forecast. Characteristics of Forecast - Forecasts are always Wrong. This is true because forecast is an estimation and not actual. -Long term forecasts are less accurate than short term forecast. -Aggregate forecasts are more accurate than disaggregate forecast. It is easier to forecast more accurately at product family level than at SKU level. For example Maruti company can forecast better for all models put together than selective models. -Information get distorted when moving away from the customer. When your distribution network contains intermediate channels like distributors and retailers, the demand information passes through them will get distorted. In Marketing parlance this is termed as “bullwhip effect”. It is like ripple created in pond when you throw a stone. Steps in Forecasting Process - Establish objectives for the forecast. Reduce the Forecast Error or Variation. - Determine what to forecast. Volume forecast for Production Planning and Value forecast for company value growth. - Specify the Axis and Level. Forecast works on Four Axis ie., Product, Customer, Time and Fact. Fact could be volume or value Forecast. Level for each axis varies as per organization classification. For example Product Axis can contain SKU, Brand, Family Group etc. - Gather historical data for particular period ( 2 to 6 years) and analyze. For example if Maruti company sales were increased in Nov’08 due to 10% discount scheme for all brands. The sales data for Nov’08 is distorted due to discount scheme and this data should be normalized before using it in forecast. - Select a forecasting method. Various models like Moving Average, Exponential Smoothing, Weighted Moving Average, Regression, Arima, Winter, Holtz etc are available. Utmost care should be taken while deciding the suitable method. - Make the forecast. - Present or Interpreting the forecast results. - Monitor and control the forecast. Of all the above steps selecting the forecast method and interpreting the forecast result correctly is the herculean task and it requires specialized effort. Statistical error measures (e.g. MAPE, MSE) are popular yet widely misunderstood and misinterpreted. Quite often, end-users are not equipped to interpret the forecasting accuracy through such error measures. In the next session we will explore about Forecasting technique and methods along with Time Series. Looking forward to your comments and queries.

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