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Price optimization utilizes data analysis to predict the behavior of potential buyers to different prices of a product or service. Depending on the type of methodology being implemented, the analysis may leverage survey data (e.g. such as in a conjoint pricing analysis [7]) or raw data (e.g. such as in a behavioral analysis leveraging 'big data' [8] [9]).
[6] [7] Wiendahl used Harris and Andler's equation for the determination of the optimal quantity. [8] Härdler took into account the costs of storage and delivery in determining the optimal batch quantity (EBQ). [9] Muller and Piasecki asserted that inventory management is explained only with the basics of an optimal quantity calculation. [10] [11]
The dynamic lot-size model in inventory theory, is a generalization of the economic order quantity model that takes into account that demand for the product varies over time. The model was introduced by Harvey M. Wagner and Thomson M. Whitin in 1958.
Given a transformation between input and output values, described by a mathematical function, optimization deals with generating and selecting the best solution from some set of available alternatives, by systematically choosing input values from within an allowed set, computing the output of the function and recording the best output values found during the process.
Revenue is the product of price and quantity and cost is given by the firm's cost structure, so profit is: = (+) (). The best response is to find the value of q 2 {\displaystyle q_{2}} that maximises Π 2 {\displaystyle \Pi _{2}} given q 1 {\displaystyle q_{1}} , i.e. given the output of the leader (firm 1 {\displaystyle 1} ), the output that ...
Bayesian-optimal pricing (BO pricing) is a kind of algorithmic pricing in which a seller determines the sell-prices based on probabilistic assumptions on the valuations of the buyers. It is a simple kind of a Bayesian-optimal mechanism, in which the price is determined in advance without collecting actual buyers' bids.
where is a random variable with probability distribution representing demand, each unit is sold for price and purchased for price , is the number of units stocked, and is the expectation operator. The solution to the optimal stocking quantity of the newsvendor which maximizes expected profit is:
Where is the change in quantity demand for the respective change in price , with Q and P representing the quantity and price of the good before a change was made. [25] The price elasticity is important for managerial economics as it aids in the optimization of marginal revenue of firms. [25]