scholarly journals Joint Ordering and Pricing Decisions for New Repeat-Purchase Products

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Xiang Wu ◽  
Jinlong Zhang

This paper studies ordering and pricing problems for new repeat-purchase products. We incorporate the repeat-purchase rate and price effects into the Bass model to characterize the demand pattern. We consider two decision models: (1) two-stage decision model, in which the sales division chooses a price to maximize the gross profit and the purchasing division determines an optimal ordering decision to minimize the total cost under a given demand subsequently, and (2) joint decision model, in which the firm makes ordering and pricing decisions simultaneously to maximize the profit. We combine the generalized Bass model with dynamic lot sizing model to formulate the joint decision model. We apply both models to a specific imported food provided by an online fresh produce retailer in Central China, solve them by Gaussian Random-Walk and Wagner-Whitin based algorithms, and observe three results. First, joint pricing and ordering decisions bring more significant profits than making pricing and ordering decisions sequentially. Second, a great initiative in adoption significantly increases price premium and profit. Finally, the optimal price shows a U-shape (i.e., decreases first and increases later) relationship and the profit increases gradually with the repeat-purchase rate when it is still not very high.

Author(s):  
R. Ghasemy Yaghin ◽  
S. M. T. Fatemi Ghomi ◽  
S. A. Torabi

Analysis of inventory systems involving market-oriented pricing decisions has recently become an interesting topic in the field of inventory control. Price and marketing expenditure are considered as important elements when selling goods and enhancing revenues by manufacturers. The importance of accounting for uncertainty in such environments spurs an interest to develop appropriate decision making tools to deal with uncertain and ill-defined parameters (such as costs and market function) in joint pricing and lot-sizing problems. In this research, a fuzzy chance constraint multi-objective programming model based on p-fractile approach is proposed to determine the optimal price, marketing expenditure and lot size. Considering pricing, marketing and lot-sizing decisions simultaneously, a possibilistic programming based on necessity measure is considered to handle imprecise data and constraints. Discount strategy as a fuzzy power function of order quantity is determined. After applying appropriate strategies to defuzzify the original possibilistic model, the equivalent multi-objective crisp model is then transformed by a single-objective programming model. A meta-heuristic algorithm is applied to solve the final crisp counterpart.


2007 ◽  
Vol 49 ◽  
pp. 139 ◽  
Author(s):  
Maryam Esmaeili ◽  
Panlop Zeephongsekul ◽  
Mir-Bahador Aryanezhad

2021 ◽  
Vol 257 ◽  
pp. 02074
Author(s):  
Xi Sun

Fresh e-commerce appeals to consumers with its fast speed, easy operation, low price and various types in the field of fresh. However, the fresh e-commerce market is facing unprecedented competitive pressure. The repeat purchase behavior of consumers has become the focus of fresh e-commerce enterprises. Based on the literature research of consumer satisfaction and fresh e-commerce repeat purchase behavior, through the investigation of online shopping experience of consumers using fresh e-commerce and empirical research on the relationship between consumer satisfaction and fresh e-commerce repeat purchase, this paper puts forward some suggestions to improve consumer satisfaction and increase fresh e-commerce repeat purchase rate.


2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Jiangbo Zheng ◽  
Yanhong Gan ◽  
Ying Liang ◽  
Qingqing Jiang ◽  
Jiatai Chang

We use Machine Learning (ML) to study firms’ joint pricing and ordering decisions for perishables in a dynamic loop. The research assumption is as follows: at the beginning of each period, the retailer prices both the new and old products and determines how many new products to order, while at the end of each period, the retailer decides how much remaining inventory should be carried over to the next period. The objective is to determine a joint pricing, ordering, and disposal strategy to maximize the total expected discounted profit. We establish a decision model based on Markov processes and use the Q-learning algorithm to obtain a near-optimal policy. From numerical analysis, we find that (i) the optimal number of old products carried over to the next period depends on the upper quantitative bound for old inventory; (ii) the optimal prices for new products are positively related to potential demand but negatively related to the decay rate, while the optimal prices for old products have a positive relationship with both; and (iii) ordering decisions are unrelated to the quantity of old products. When the decay rate is low or the variable ordering cost is high, the optimal orders exhibit a trapezoidal decline as the quantity of new products increases.


2019 ◽  
Vol 52 (13) ◽  
pp. 106-111
Author(s):  
Paulin Couzon ◽  
Yassine Ouazene ◽  
Farouk Yalaoui

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