The shelf life of a product is a length of time an SKU can be stored on shelf without becoming unfit for use. Retailers commonly use the strategy of “Stock Rotation and Rationalization” — moving the SKU’s with the earliest sell dates so that shoppers pick them before the expiration date without any loss to the retailer.
Sometimes it becomes necessary to sell the slow-moving SKU’s with a shorter shelf life at a lower price point to increase the velocity of SKU.
When a SKU/Product Line is sold at a lower price point or a discounted price, it results in a Marginal Loss to the retailer. For retailers, coming up with maximum possible discount without incurring a Marginal Loss is a challenge.
(Co-efficient = Delta Y / Delta X) Step 1: Co-efficient Estimation
Based on the distribution of the data, the appropriate Statistical Equation is applied to the data to estimate the co-efficient of price Variables.:
Y = Velocity of a Product Line of perishable Product Line/List of SKU
Infogain’s Algorithm uses the co-efficient obtained from the co-efficient estimate for price variables equation, running Optimization to arrive at an optimal discount price point based on Marginal ROI Analysis:
Although the Volume Sales will go up with the increased discount, Value Sales won’t necessarily go up as well.
Optimization: Mathematical Linear Programming and Optimization
Objective Function: Minimize the Delta Value Sales
Variable: Discount Percent
Constraint: Marginal ROI should be equal to 1
Optimal Discount Percent based on Marginal ROI Analysis = Discount percent 15%
Discount Percent Point without incurring Marginal loss = 30%
Any Discount beyond 30% will lead to Marginal loss
What is the base /current scenario? Marginal cost and profit evaluation of current Situation
(Are you currently below optimal Discount or above Optimal Discount or incurring loss? Or is there any scope of improvement)
What is the Optimal Discount Rate? (15 % as seen above)
What is the highest possible discount rate that can be offered without incurring marginal loss? (Ex. Scope to increase discount rate up to 30% as seen above)
This relationship is mostly linear in nature. Infogain has suggested Linear Programing for optimization based on the Domain experience, managerial economic price demand function theory and APAC Market Knowledge. If we don’t see a linear relationship between price and demand, however, an appropriate non –linear programming will be applied for Optimization and statistical equation based on trend observed in the data.
The Marginal ROI Model is used for solving many computational financial problems ranging from Option Pricing to investment decisions such as cost minimization and profit maximization. Several Marketing and Consumer Packaged Goods (CPG) companies have adopted the Marginal ROI Analysis for best pricing in order to achieve Maximize Profitability.
Linear programming Marginal ROI Model can help retailers select the best possible discount strategy for perishable goods without incurring any marginal loss . The Scenario planner can help them evaluate the marginal cost vs profit of various discount alternatives. The Model can also help retailers forecast profitability for future.
To learn more about the Infogain’s Linear programming Marginal ROI Model, contact firstname.lastname@example.org.
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