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Demand forecasting

How to develop an Effective Scientific Retail Demand Forecast? Purpose of the Forecast The ability to effectively forecast demand is critical to the success of a retailer. In this hyper competitive environment of ever diminishing margins, every paisa saved or earned is critical. A robust demand forecast engine, can have significant impacts on enhancing both top & bottom lines. In today’s world, the retailers require forecasts that would be instrumental in directing the organization through a minefield of capacity constraints, multiple sales geographies and a multi-tier distribution channel.

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Demand forecasting helps understand key questions biz. Which market would place demands for which specific type of product, which manufacturing unit should cater to which retailer, how many product units are required in a given season etc.? Given the sophisticated tools & techniques available today, all retailers should replace gut based decision making, with scientific forecasts. The benefits, throughout the lifestyle of the analysis will far outweigh the one time set up and ongoing maintenance costs.

There is a lot of value in answering these questions through scientific methodologies s compared to educated guesses, or Judgmental forecasts. Business Benefits Scientific forecasting generates demand forecasts which are more realistic, accurate and tailored to specific retail business area. It facilitates optimal decision-making at the headquarters, regional and local levels, leading to much lesser costs, higher revenues, better customer service and loyalty. Range of Business Users Traditionally, only the sales department has used forecasts, but in evolved markets the usage of forecasts is now pan organizational.

Sales Revenue Forecasting, Marketing & Promotion Planning, Operations Planning, Inventory Management etc. Also extensively use sales forecasts. Indian retail needs to imbibe this discipline as their scale of operations grows larger and they are unable to cope with the entrepreneurial style of functioning, which was the key to their success in the start up phase. Typical Challenges Faced! I enough manna Directorates Is an Important aspect AT a retail Dustless, more oaten than not, it is laced with multiple challenges.

Some of them could be: Level/Scope of the Forecasts A large retailer may have thousands of SSW. A conscious decision has to be made regarding the product hierarchy level at which the forecasts are needed, as it is very Hellenizing to produce forecasts for all existing SKU, neither does it make sound financial sense in most cases. Other concern would be the number of stores a typical large retailer possesses, and whether a separate forecast is needed for each of the stores. In order to optimism the cost-benefit, TAG recommends creation of forecasts at the “Store-cluster” & “SKU-Cluster” levels.

The store clusters are created using store characteristics, like past demand patterns and local/ regional demand factors. The SKU clusters are determined by the category type, life cycle etc. New Product Sales/Demand Forecasts A retailer typically launches new products every month/season. Using past data to forecast is not feasible, as past data does not exist. TAG, would tackle the situation by considering complementary products, based on their key characteristics like target segment, product category, price level, features etc.

A rapidly emerging methodology is the estimation of future demand using Advanced Bayesian Forecasting Models (Fig. 3). Bizarre/Missing Historic Sales Pattern The erratic sales figures for many items in the store often pose a lot of issues for scientific methods of forecasting. In these situations, we need to resort to extensive statistical data cleaning exercises. Non-availability of True Historic Demand Historic sales are used to estimate the future demand, as it is the only reliable quantitative indicator available about customer demand.

However it is possible that sales data end-up with a bias because of the inventory rupture or temporary promotional activities. These situations need correction to sales history to reflect the true demand. Since demand bias is very business specific, such corrections usually require in-depth domain expertise to interpolate/extrapolate the sales figures. Forecasting Techniques Demand forecasting techniques are broadly divided into two categories: Judgmental Ana statistical. Fig. – Hierarchy of Forecasting Models The Scientific (Statistical) Forecast Models Scientific models are divided into two categories, Extrapolation Models & Causal Models (Fig. 2). The extrapolation models are based exclusively on the past/historic sales data where the trend, seasonality & cyclist prevalent in the historic sales data are examined to project the sales in future. However it is pretty intuitive that the future sales not only depend on the past sales but also on the other factors biz. Economic trends, competitors’ movement, festive events, promotional activities etc.

In order to incorporate such external factors in forecasting, a variety of causal models are available. In absence of such external factors’ data, the extrapolation models provide decent forecasts in most of the situations. Key Comparisons of Various Scientific Models Extrapolation Models Causal Models Smoothing Model Family Box-Jenkins Model Family Unobserved Component Models Bayesian Dynamic Models What Data do We Need? Historic Sales Data Holsters sales Ad Store Traffic Data Major Festive/Sporting Events Promotional Activities

Competitors’ Actions Macro Economic Data Weather Data What Business Problems can it solve? Forecast of Future Sales using Just the Past Sales Forecasts are of interest in the short term time horizon Useful for Store Managers -Store Labor/elementary Order Planning Prediction of Future Sales using the Past Sales as well as external factors in the medium – long term period Understand the impact of external factors (drivers) on Sales Useful for Top Management – High Level Strategic Planning What are the Merits? Simplistic Forecasts at short notice!!

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