Demand
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Demand Forecasting is the activity of estimating the quantity of a product or service that consumers will purchase. Demand forecasting involves techniques including both informal methods, such as educated guesses, and quantitative methods, such as the use of historical sales data or current data from test markets. Demand forecasting may be used in making pricing decisions, in assessing future capacity requirements, or in making decisions on whether to enter a new market.
Contents [hide]
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1 Necessity for forecasting demand o 1.1 Stock effects o 1.2 Market response effects 2 Methods o 2.1 Methods that rely on qualitative assessment o 2.2 Methods that rely on quantitative data 3 See also
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4 References
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[edit] Necessity for forecasting demand Often forecasting demand is confused with forecasting sales. But, failing to forecast demand ignores two important phenomena[1]. There is a lot of debate in the demand planning literature as how to measure and represent historical demand, since the historical demand forms the basis of forecasting. Should we use the history of outbound shipments or customer orders or a combination of the two to proxy for demand. [edit] Stock effects The effects that inventory levels have on sales. In the extreme case of stock-outs, demand coming into your store is not converted to sales due to a lack of availability. Demand is also untapped when sales for an item are decreased due to a poor display location, or because the desired sizes are no longer available. For example, when a consumer electronics retailer does not display a particular flat-screen TV, sales for that model are typically lower than the sales for models on display. And in fashion retailing, once the stock level of a particular sweater falls to the point where standard sizes are no longer available, sales of that item are diminished. [edit] Market response effects The effect of market events that are within and beyond a retailer’s control. Demand for an item will likely rise if a competitor increases the price or if you promote the item in your weekly circular. The resulting sales increase reflects a change in demand as a result of
consumers responding to stimuli that potentially drive additional sales. Regardless of the stimuli, these forces need to be factored into planning and managed within the demand forecast. In this case demand forecasting uses techniques in causal modeling. Demand forecast modeling considers the size of the market and the dynamics of market share versus competitors and its effect on firm demand over a period of time. In the manufacturer to retailer model, promotional events are an important causal factor in influencing demand. These promotions can be modeled with intervention models or use a consensus process to aggregate intelligence using internal collaboration with the Sales and Marketing functions.
[edit] Methods No demand forecasting method is 100% accurate. Combined forecasts improve accuracy and reduce the likelihood of large errors.[2] [edit] Methods that rely on qualitative assessment Forecasting demand based on expert opinion. Some of the types in this method are, • • • • • • • •
Unaided judgment Prediction market Delphi technique Game theory Judgmental bootstrapping Simulated interaction Intentions and expectations surveys Conjoint analysis
[edit] Methods that rely on quantitative data • • • • • • • •
Discrete Event Simulation Extrapolation Quantitative analogies Rule-based forecasting Neural networks Data mining Causal models Segmentation
[edit] See also • •
Supply and demand Calculating demand forecast accuracy
[edit] References 1. ^ Forecasting Demand(Aug 05), Oracle Retail 2. ^ Armstrong & Green (2005), Demand Forecasting: Evidence-based Methods Retrieved from "http://en.wikipedia.org/wiki/Demand_forecasting" Categories: Financial terminology | Statistical forecasting Views • • • •
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Contents [hide]
•
1 Necessity for forecasting demand o 1.1 Stock effects o 1.2 Market response effects 2 Methods o 2.1 Methods that rely on qualitative assessment o 2.2 Methods that rely on quantitative data 3 See also
•
4 References
•
•
[edit] Necessity for forecasting demand Often forecasting demand is confused with forecasting sales. But, failing to forecast demand ignores two important phenomena[1]. There is a lot of debate in the demand planning literature as how to measure and represent historical demand, since the historical demand forms the basis of forecasting. Should we use the history of outbound shipments or customer orders or a combination of the two to proxy for demand. [edit] Stock effects The effects that inventory levels have on sales. In the extreme case of stock-outs, demand coming into your store is not converted to sales due to a lack of availability. Demand is also untapped when sales for an item are decreased due to a poor display location, or because the desired sizes are no longer available. For example, when a consumer electronics retailer does not display a particular flat-screen TV, sales for that model are typically lower than the sales for models on display. And in fashion retailing, once the stock level of a particular sweater falls to the point where standard sizes are no longer available, sales of that item are diminished. [edit] Market response effects The effect of market events that are within and beyond a retailer’s control. Demand for an item will likely rise if a competitor increases the price or if you promote the item in your weekly circular. The resulting sales increase reflects a change in demand as a result of
consumers responding to stimuli that potentially drive additional sales. Regardless of the stimuli, these forces need to be factored into planning and managed within the demand forecast. In this case demand forecasting uses techniques in causal modeling. Demand forecast modeling considers the size of the market and the dynamics of market share versus competitors and its effect on firm demand over a period of time. In the manufacturer to retailer model, promotional events are an important causal factor in influencing demand. These promotions can be modeled with intervention models or use a consensus process to aggregate intelligence using internal collaboration with the Sales and Marketing functions.
[edit] Methods No demand forecasting method is 100% accurate. Combined forecasts improve accuracy and reduce the likelihood of large errors.[2] [edit] Methods that rely on qualitative assessment Forecasting demand based on expert opinion. Some of the types in this method are, • • • • • • • •
Unaided judgment Prediction market Delphi technique Game theory Judgmental bootstrapping Simulated interaction Intentions and expectations surveys Conjoint analysis
[edit] Methods that rely on quantitative data • • • • • • • •
Discrete Event Simulation Extrapolation Quantitative analogies Rule-based forecasting Neural networks Data mining Causal models Segmentation
[edit] See also • •
Supply and demand Calculating demand forecast accuracy
[edit] References 1. ^ Forecasting Demand(Aug 05), Oracle Retail 2. ^ Armstrong & Green (2005), Demand Forecasting: Evidence-based Methods Retrieved from "http://en.wikipedia.org/wiki/Demand_forecasting" Categories: Financial terminology | Statistical forecasting Views • • • •
Article Discussion Edit this page History
Personal tools •
Try Beta
•
Log in / create account
Navigation • • • • •
Main page Contents Featured content Current events Random article
Search
Interaction • • • • • •
About Wikipedia Community portal Recent changes Contact Wikipedia Donate to Wikipedia Help
Toolbox •
What links here
• • • • • •
Related changes Upload file Special pages Printable version Permanent link Cite this page
•
This page was last modifi
