Optimal Replenishment Research Articles

Optimal Replenishment Strategy for a High-Tech Product Demand with Non-Instantaneous Deterioration under an Advance-Cash-Credit Payment Scheme by a Discounted Cash-Flow Analysis

This study investigated non-instantaneous deteriorating items because not all products deteriorate immediately. In the high-tech product life cycle, the product demand increases linearly substantially in the growth stage and maintains a near-constant level in the maturity stage. This is a ramp-type demand rate. To satisfy the demand as shortages occur, partial backlogging is necessary. The advance-cash-credit payment scheme, comprising advance, cash, and credit payments, has gained popularity in business transactions to improve cash flow flexibility among supply chain participants. This study explored a partial backlogging inventory model with ramp-type demand for non-instantaneous deteriorating items under generalized payment. The proposed model also incorporated discounted cash flow analysis to account for the time value of the profit function. This study attempted to determine the optimal replenishment strategy to maximize the present value of the total profit. Finally, we conducted a sensitivity analysis to examine the efficacy of the proposed model and gain managerial insights. The optimal total profit rises with an increase in the permissible delay period and sale price but decreases with an increase in ordering and purchase costs. Then, the decision-maker can refer to the managerial insights to choose the appropriate parameter value for the operation.

Research on Pricing and Dynamic Replenishment Planning Strategies for Perishable Vegetables Based on the RF-GWO Model

This paper addresses the challenges of automated pricing and replenishment strategies for perishable products with time-varying deterioration rates, aiming to assist wholesalers and retailers in optimizing their production, transportation, and sales processes to meet market demand while minimizing inventory backlog and losses. The study utilizes an improved convolutional neural network–long short-term memory (CNN-LSTM) hybrid model, autoregressive moving average (ARIMA) model, and random forest–grey wolf optimization (RF-GWO) algorithm. Using fresh vegetables as an example, the cost relationship is analyzed through linear regression, sales volume is predicted using the LSTM recurrent neural network, and pricing is forecasted with a time series analysis. The RF-GWO algorithm is then employed to solve the profit maximization problem, identifying the optimal replenishment quantity, type, and most effective pricing strategy, which involves dynamically adjusting prices based on predicted sales and market conditions. The experimental results indicate a 5.4% reduction in inventory losses and a 6.15% increase in sales profits, confirming the model’s effectiveness. The proposed mathematical model offers a novel approach to automated pricing and replenishment in managing perishable goods, providing valuable insights for dynamic inventory control and profit optimization.

Analyzing how inflation affects non-instantly decaying goods with demand linked to ads and selling price in a dual-warehouse setup

This study explores inventory management strategies specifically designed for non-instantaneous deteriorating goods under inflationary conditions, utilizing a dual-warehouse system—one owned and the other rented with limited storage capacity. We examine the effects of advertising frequency and product selling price on demand rates, taking into account the gradual decline in customer patience, which leads to partial backlogging of shortages. The primary objective is to determine optimal replenishment policies for retailers that effectively minimize total costs per unit time. In real life, managing oil inventory is crucial for industries where factors like gradual deterioration, demand fluctuations due to advertising and pricing, and sensitivity to inflation require sophisticated inventory models to optimize replenishment policies and minimize costs. To validate our proposed inventory model, we provide a numerical. A sensitivity study using MATLAB R2024a software highlights the impact of parameter changes, providing significant information for decision-makers across various industries.

Conditional Lead-Time Flexibility in an Assemble-to-Order System

Problem definition: We introduce and formalize a concept termed conditional lead-time flexibility (CLF), which refers to the industry practice where a manufacturer requests that its upstream suppliers dynamically adjust the pipeline orders’ remaining lead times. Over a finite horizon, an assemble-to-order manufacturer makes joint decisions on inventory replenishment and lead-time adjustment to minimize the total discounted expected cost. Methodology/results: The problem is formulated as a multiperiod dynamic programming with an enlarged state space to track the adjustable pipeline orders. The replenishment and lead-time adjustment decisions are made sequentially in each period. The analysis reveals that (i) the optimal cost-to-go function is both convex and additively separable, that (ii) both the optimal replenishment and lead-time adjustment decisions follow general base-stock policies, and that (iii) the convexity and additive separability allow us to perform a derivative analysis on the optimality equations and design an efficient algorithm. Managerial implications: Using real industry data, we numerically find that (i) CLF achieves notable cost savings over a wide range of parameters, that (ii) the value of CLF comes from both the order expediting and deferring, and that (iii) CLF outperforms dual sourcing when the unit adjustment cost is less than 40% of the unit ordering cost from the express source. Our findings underscore the importance of CLF. Funding: T. Deng acknowledges financial support from the National Key Research and Development Program of China [Grant 2022YFB3305602] and the National Natural Science Foundation of China (NSFC) [Grant 72188101]. Supplemental Material: The electronic companion is available at https://doi.org/10.1287/msom.2023.0520 .

Optimal replenishment time and lot-splitting delivery policy for nondeteriorating and deteriorating items with advance–loan–deposit scheme

Previous studies of retailer payments within the supply chain have usually focused on three approaches: (1) cash in advance, (2) cash on delivery, and (3) permissible payment delay. However, small- and medium-sized enterprises often must use bank loans when ordering goods in quantity due to insufficient cash availability. This paper develops two mathematical models, nondeteriorating and deteriorating items, for a two-echelon supply chain system in which the buyer adopts an advance–loan–deposit scheme to pay suppliers for goods. When the deterioration rate approaches 0, the deteriorating items model reduces to the nondeteriorating one. Two algorithms were developed to explore the optimal replenishment time and lot-splitting delivery policy. Numerical examples illustrate the proposed model and algorithm. A sensitivity analysis reveals the effects of four important parameters—i.e., ordering cost, unit-purchasing cost, receiving cost, and demand rate—on the optimal strategy. Managerial insights are also explored.

Automated Pricing and Replenishment Decisions for Vegetable Products Based on Data Analysis

This study delves into the critical factors for the successful operation of vegetable products supermarkets: efficient inventory management and accurate market demand analysis. Given the perishability of vegetables, their variety, and the complexity of supply chains, a linear regression model and the method of least squares have been developed to estimate model parameters. Additionally, the ARIMA model is utilized to forecast the unit prices of commodities. In a more comprehensive approach, considering various influencing factors, a combinatorial optimization algorithm (integrating linear regression and greedy algorithm) is employed to determine the optimal replenishment strategy. Based on this, the study forecasts the sales volume and unit prices for various categories over the next seven days, and accordingly, establishes the pricing strategy and predicted replenishment volume for each category. The research underscores the necessity for retailers to precisely analyze sales data and market trends, optimize supply chain management, and formulate effective pricing strategies to cope with seasonal variations and limited sales space. These measures are essential to satisfy consumer demand and enhance profitability.

Vegetable Replenishment and Pricing Planning Based on ARIMA Forecasting

In fresh food supermarkets, vegetables have a short shelf life and can deteriorate quickly, necessitating daily replenishment and pricing decisions based on historical sales to maximize profits. This article, based on the historical sales data of vegetable products from a major supermarket, constructs a replenishment and pricing planning model based on ARIMA forecasting for six types of vegetables, seeking the optimal replenishment and pricing strategy for the next seven days. The results indicate that leafy and flowering vegetables, as well as eggplant-type vegetables, require more replenishment on Fridays and weekends, whereas aquatic root vegetables need more on Thursdays and Fridays. The replenishment of chili vegetables and edible mushrooms fluctuates, and there is a constraint relationship between pricing and replenishment volume. This study aims to assist merchants in making reasonable replenishment and pricing decisions to reduce costs and maximize economic benefits.

Pricing and Replenishment Prediction Model for Vegetable Commodities Based on Mixed Integer Linear Programming Optimization XGBoost

In fresh food superstores, the replenishment volume and pricing strategy of vegetables are affected by various factors such as seasonal seasons, supply demand, purchasing costs, and profits of vegetable categories. In this paper, we use the data related to the sales flow details and wholesale prices of each commodity from July 1, 2020 to June 30, 2023 as a dataset, and build a model based on the gradient boosting tree algorithm, XGBoost algorithm [1], to predict the total daily replenishment volume and pricing strategy of each vegetable category in the coming week (July 1-7, 2023) [2]. It is also optimized using a mixed-variable 0-1 linear programming (MILP) [3] model with decision variables including a binary variable of whether to order the vegetable category or not and a continuous variable of the number of vegetable categories ordered to compute the optimal replenishment volume and pricing strategy for July 1, 2023 for each item. Through the model in this paper, under the premise of meeting the market demand for each category of vegetable goods, while keeping the control within a specific range, the superstore can get the optimal replenishment plan and pricing strategy, reasonably allocate the resources of the warehouse space, improve the market competitiveness of the superstore, and maximize the revenue.

IoT and machine learning approach for the determination of optimal freshwater replenishment rate in aquaponics system

IoT and machine learning approach for the determination of optimal freshwater replenishment rate in aquaponics system

Research on automatic pricing and replenishment strategy of vegetable commodities based on ARIMA algorithm

In just a few years' time, supermarkets have transformed from a novelty into a mainstream channel that unifies the retail world. Since superstores sell vegetables with a wide variety of types, different origins, and shorter freshness periods, the incoming transaction time of vegetables is often in the early morning, so superstores usually replenish their stocks on a daily basis according to the historical sales and demand of each commodity. First, this paper visualizes the historical data of vegetable commodity purchasing and pricing in superstores in order to observe the distribution characteristics and trends, and then analyzes the correlation between categories or individual products using statistical methods such as correlation factors and covariance. To predict replenishment and pricing strategies for the coming week, this paper analyzes the relationship between overall sales and cost-plus pricing for each vegetable category, using given sales data to establish the relationship between sales volume and price for each category. Based on the ARIMA predicted sales model and given costs, the optimal replenishment quantities and pricing strategies were determined using an optimization algorithm.

Genetic Algorithms Application for Pricing Optimization in Commodity Markets

The perishable nature of vegetable commodities poses challenges for superstores, as reselling them is often unfeasible due to their short freshness period. Reliable market demand analysis is crucial for boosting revenue. This study simplifies the pricing and replenishment decision-making process by making reasonable assumptions about the selling time, wastage rate, and replenishment time for vegetable commodities. A single-objective planning model with the objective of profit maximization was constructed by fitting historical data using the nonparametric method of support vector regression (SVR). The study reveals a specific relationship between sales volume and cost-plus pricing for each category and predicts future cost changes using an LSTM model. Combining these findings, we substitute the relationship between sales volume and pricing as well as the LSTM prediction data into the model, and solve it using genetic algorithms in machine learning to derive the optimal replenishment volume and pricing strategy. Practical results show that the method can provide reasonable pricing and replenishment strategies for vegetable superstores, and after careful accounting, we arrive at an expected profit of RMB 22,703.14. The actual profit of the supermarket was RMB 19,732.89. The method, therefore, increases the profit of the vegetable superstore by 13.08%. By optimizing inventory management and pricing decisions, the superstore can better meet the challenges of vegetable commodities and achieve sustainable development.

Research on the Optimization of Pricing and the Replenishment Decision-Making Problem Based on LightGBM and Dynamic Programming

Vegetables have a short period of freshness, and therefore, the purchase of vegetables has to be carefully matched with sales, especially in the “small production and big market” setting prevalent in China. Therefore, it is worthwhile to develop a systematic and comprehensive mathematical model of replenishment plans and pricing strategies for each category of vegetables and individual products. In this paper, we analyze the following three questions: Question One: What is the distribution law and relationship between the sales volume of vegetable categories and single products? Question Two: What is the relationship between total sales volume and cost-plus pricing of vegetable categories? And is it possible to provide the daily total replenishment and pricing strategy of each vegetable category for the following week to maximize supermarket profit? Question Three: How can we incorporate the market demand for single vegetable products into a profit-maximizing program for supermarkets? Is it possible to further formulate the replenishment plan requirements for single products? To answer the first question, we created pivot tables to analyze occupancy. We found that mosaic leaves, peppers, and edible mushrooms accounted for a larger proportion of occupacy, while cauliflowers, aquatic rhizomes, and eggplants accounted for a smaller proportion. For the single items, lettuce, cabbage, green pepper, screw pepper, enoki mushroom, and shiitake mushroom accounted for a large proportion of their respective categories. We used the Pearson correlation coefficient and the Mfuzz package based on fuzzy c-means (FCM) algorithm to analyze the correlation between vegetable categories and single products. We found that there was a strong correlation between vegetable categories. Moreover, the sale of vegetable items belonging to the same category exhibited the same patterns of change over time. In order to address the second question, we established the LightGBM sales forecasting model. Combined with previous sales data, we forecasted and planned an efficient daily replenishment volume for each vegetable category in the coming week. In addition, we developed a pricing strategy for vegetable categories to maximize supermarket profits. For the third question, we built a dynamic programming model combining an optimal replenishment volume with a product pricing strategy for single items, which let the supermarket maximize its expected profits.

Research on automatic pricing and replenishment decision of vegetable commodities based on optimization model

In fresh food superstores, because vegetable commodities are characterized by perishability and high value loss, superstores usually replenish daily commodities based on the historical sales and demand of various commodities. In order to make the optimal replenishment decision, this paper firstly firstly uses Matlab fitting toolbox to get the functional relationship between the total sales volume and the cost-plus pricing, so as to analyze the relationship between the total sales volume and the cost-plus pricing of each vegetable category, and then takes the category as a unit, and then constructs the optimization model, and solves the optimal replenishment volume and pricing strategy. The replenishment decision is obtained by filtering out the most profitable single product among the factors of unit price, sales volume, and total replenishment volume through objective planning under the constraints of considering the single product category and minimum display volume.

The Impacts of Payment Schemes and Carbon Emission Policies on Replenishment and Pricing Decisions for Perishable Products in a Supply Chain

In the supplier–retailer–consumer system, the retailer’s replenishment and pricing strategies impact the entire transaction process, forming a comprehensive trading market. Suppliers offer advance-cash-credit payments to retailers, while retailers provide customers with cash-credit payment options. In the current health-conscious consumer market, purchasing decisions are influenced not only by commodity prices but also by the freshness of products, particularly perishable goods. Growing awareness of climate change and the advent of carbon emission policies have raised concerns about the environmental costs of business transactions. This study focuses on perishable products whose demand is influenced by both price and freshness. It explores the adoption of various payment methods by suppliers and retailers, as well as the impact of carbon emission cap-and-trade policies or carbon tax policies on management and pricing strategies. Suitable inventory models are established to determine the optimal replenishment and pricing strategies for maximizing the current value of total profit. We illustrate that the current value of total profit demonstrates joint concavity concerning both the selling price and the replenishment time. Finally, we verify the proposed models using numerical examples and present the findings of sensitivity analyses. The findings of this study yield several valuable insights for inventory management of perishable goods.

Research on pricing and replenishment based on optimal programming model

This paper studies the law and correlatoin of different categories of vegetable commodities, and makes the total amount of replenishment and pricing strategy of each vegetable category in the next week, so as to maximize the profit of supermarket. Firstly, the correlatoin between different categories of vegetable products was analyzed based on Pearson correlatoin analysis. The results showed that flower-leaf, cauliflower, pepper had strong correlatoin with edible fungi, while aquatic rhizome and solanaceae had relatively low correlatoin with other categories of vegetables. Then, the monthly and daily sales volume of each category and single vegetable were counted, and the monthly, weekly and daily distribution function of each category and single vegetable was obtained by fitting with Python software using the least square method. The vegetable category cluster analysis based on systematic clustering was used to cluster 6 categories of vegetables, and the 6 categories of vegetables were divided into 3 categories. Then the least square method was used to fit the processed data, and the fitting function expression between the sales volume and the cost plus pricing of each category of vegetables was obtained. The replenishment quantity decision model based on time series was established, and the daily replenishment quantity decision of each vegetable category in the next week was obtained. Finally, according to the correlatoin between sales volume and price, the optimal pricing model based on dynamic market demand is established, and the most reasonable pricing is obtained and the optimal replenishment decision is made to maximize the profit of supermarket.

A Study on Replenishment Decision of Vegetable Goods Based on Polynomial Regression and Arima Time Series Forecasting

In a fresh food superstore, the superstore replenishes daily based on the historical sales and demand of vegetable commodities. To formulate the optimal replenishment decision, this paper analyses the sales flow, purchase cost, and loss of a supermarket in a period, and through the use of a variety of models, it can give a reference opinion on the formulation of automatic pricing and replenishment decisions for vegetable commodities. The first text describes the process of analyzing the relationship between total sales volume and cost-plus pricing, including data integration, preprocessing, and multiple regression analysis methods. Through these analyses, a general relationship between total sales volume and merchandise selling price is identified. Univariate linear regression, polynomial regression, random forest regression, and BP neural network regression models were all used to analyze this relationship. The focus is then on the total daily replenishment and pricing strategy for the coming week to maximize the superstore's revenue. The text details the use of ARIMA time series analysis methods, as well as steps for noise reduction and forecasting using Wavelet wavelet functions. Through these methods, the text provides sales forecast results for different vegetable categories for the coming week for the superstore management's reference.

Managing Replenishment and Clearance of Perishables: Last-In, First-Out (LIFO) Issuing Policy and Age-Sensitive Demand

In supermarkets, customers choose the unit(s) they purchase, which leads to inventory being sold in a last-in, first-out (LIFO) order for perishable products. Furthermore, for such products, the demand for inventory may depend on its age since customers may choose to walk away if the freshest available inventory is too old for them. Despite the widespread occurrence of LIFO-based inventory systems, few studies have analyzed them. In this study, we contribute by developing insights on joint replenishment and clearance policy for a perishable product with a general, finite lifetime using a periodic review model such that the inventory is sold in a LIFO order and demand is age-dependent. The model seeks to optimize two decisions every period: how much of fresh inventory to order and how much of existing inventory to clear. A key objective of the model is to understand the effect of age-dependence of demand on the optimal replenishment and clearance policy. We find that the optimal clearance policy for all but the oldest inventory is a multiindex policy such that between any pair of adjacent indexes, either no inventory is cleared or all the inventory is cleared till the lower index. For the oldest inventory, we show that the optimal policy may have multiple forms depending on how sensitive demand is to inventory’s age. This is a notable result in inventory theory since parameters usually affect only the policy value(s) and not the policy structure. Since the optimal policy has a complex structure in general, which makes it difficult to compute, we develop an efficient heuristic to compute the replenishment and clearance quantities. The heuristic underperforms the optimal policy by [Formula: see text]0.92% on average.

Optimizing Vegetable Commodity Pricing and Replenishment: An Integration of ARIMA and Nonlinear Programming Approaches

Automated pricing and replenishment decision-making for vegetable items in supermarkets has attracted the attention of many operators and become a trend, and has become an indispensable part of supermarkets for many operators. In order to develop reliable automatic pricing and replenishment decisions for vegetable products, this paper firstly uses logistic regression analysis to obtain the regression equations of total sales and cost-plus pricing for each vegetable category, and then uses the ARIMA time-series forecasting model to forecast the average wholesale prices of vegetable categories for the next 7 days. After selecting the average wholesale price and pricing-sales volume model for each vegetable category, a nonlinear programming model is constructed to solve the total daily replenishment and pricing strategy for each vegetable category in the coming week. Considering the constraints of limited merchandising categories, this paper introduces three constraints: the number of saleable items, the minimum display volume and the selling space, and estimates the future wholesale price by using the historical average to make a prediction. The optimal replenishment quantity and price are derived by combining the nonlinear programming model and guided by the constraint function.

Piling and discounting perishable products: optimal replenishment and discounting policy for perishable products

Piling and discounting perishable products: optimal replenishment and discounting policy for perishable products

Piling and Discounting Perishable Products: Optimal Replenishment and Discounting Policy for Perishable Products

Piling and Discounting Perishable Products: Optimal Replenishment and Discounting Policy for Perishable Products