An overview presented by Margie Dominguez MAT 540 August 10, 2013
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Slide

 Background  Mix

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Modeling  Predictive Modeling  Genius Forecasting  Inventory Forecasting  Simulations Modeling  Decision Tree  Conclusion

Many companies and businesses use forecasting. Whether its to predict sales growth, consumer demand, profit or plan production, management wants to know how to proceed in making an informed decision about the future. This presentation will examine some of today’s most popular forecasting models by highlighting how leading companies are putting them to use.

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Mix modeling – Marketing strategy
Mix modeling can help with marketing strategies by measuring the potential value of all market input and marketing investments. The goal is a long-term revenue growth. Mix modeling’s multiple-regression technique is conducted based on the number of inputs and how these inputs relate to an outcome. The data that go into creating a marketing mix model includes: • Economic data • Industry data • Category data • Advertising data (including copy testing)  Promotional data  Competitive data  Service data • Product data- Pricing data, Features & performance • Market outcome data- sales, revenue, profits

Reference: http://www.decisionanalyst.com/Services/MarketingMixModeling.dai
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Predictive Modeling for Consumer Demand forecast
Predictive modeling is an effective forecasting model for consumer demand. The technique is based on accumulated data regarding consumer behavior. Within this process, there will be a continual monitoring of demand to track changes and trends. Tools: • Analysis Tools are used for estimating future demand • Systematic Patterns vs. Trends is used to predict future demand.  Patterns repeat systematically over time  Trends do not repeat

Reference: http://www.statsoft.com/textbook/demand-forecasting/
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Genius Forecasting is an undetermined set of processes used to guess future outcomes.

• Based on guessing, along with some combination of luck, insight, and intuition. • Undetermined results until the forecast predicts expected results. • Accuracy depends on psychic prediction of individuals.

Reference: http://www.statpac.org/research-library/forecasting.htm
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Inventory Forecasting and Replenishment • Advanced process of inventory buying functionality that supercharges inventory purchasing in an existing ERP system. How it works? • Use strategic inventory policies in setting up an inventory forecasting system. An example of such a policy would be setting service levels to maintain a certain level of inventory turns. • The system then automatically analyzes demand and current daily stock status data. • The result will help to optimize inventory reorders automatically. Reference: http://www.thrivetech.com/Inventory_Forecasting.html

Inventory Replenishment

Store Replenishment

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Simulation is a descriptive technique that involves developing a model and conducting experiments to evaluate behaviors under various conditions. How does it work? • Creating a quantitative model. • Running simulations to better understand the potential impact of the uncertain variables. • Testing a range of what-if choices.

Reference: http://www.solver.com/better-decisions-with-simulation
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A Decision Tree is a graphical representation illustrating the structural relationships between alternative choices. • A framework to consider the probability and payoffs of decisions to help analyze decisions. • Process flow diagram helps to give visual choices/decisions/ actions under certain situations. • Can handle complex situations by building multiple sub-system. • Helps to consider many possible decision outcomes. References: http://www.mindtools.com/dectree.html http://www.ehow.com/list_6740944_advantagesdisadvantages-decision-trees.html#ixzz2bavqbeUi
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We have presented several forecasting methods that can be used for different time periods. The one thing we must remember is that forecasting is not an exact science, but it is a useful tool that companies and executives can use to plan for the future.

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