Optimal Purchase Research Articles

Dynamic Supplier Selection and Its Optimal Strategy Considering Full Truck Load and Fuzzy Demand using Fuzzy Expected Value-Based Programming

This article proposes a linear integer optimization model incorporating fuzzy parameters to find the optimal solution for a dynamic supplier selection problem with uncertain demand. The uncertain demand value is represented using a fuzzy variable. A fuzzy expected value-based linear optimization solver is used to address the optimization problem, to minimize the total cost under fuzzy demand values. Several computational experiments were conducted to evaluate and analyze the model. The results show that the proposed model effectively identifies the optimal suppliers for each product. Additionally, the model determines the optimal purchase volumes for each product type from the selected suppliers, leading to the minimal total expected cost.

Profit-driven distributed trading mechanism for IoT data

Data trading is a crucial means of unlocking the value of Internet of Things (IoT) data. However, IoT data differs from traditional material goods due to its intangible and replicable nature. This difference leads to ambiguous data rights, confusing pricing, and challenges in matching. Additionally, centralized IoT data trading platforms pose risks such as privacy leakage. To address these issues, we propose a profit-driven distributed trading mechanism for IoT data. First, a blockchain-based trading architecture for IoT data, leveraging the transparent and tamper-proof features of blockchain technology, is proposed to establish trust between data owners and data requesters. Second, an IoT data registration method that encompasses both rights confirmation and pricing is designed. The data right confirmation method uses non-fungible token to record ownership and authenticate IoT data. For pricing, we develop an IoT data value assessment index system and introduce a pricing model based on a combination of the sparrow search algorithm and the back propagation neural network. Finally, an IoT data matching method is designed based on the Stackelberg game. This establishes a Stackelberg game model involving multiple data owners and requesters, employing a hierarchical optimization method to determine the optimal purchase strategy. The security of the mechanism is analyzed and the performance of both the pricing method and matching method is evaluated. Experiments demonstrate that both methods outperform traditional approaches in terms of error rates and profit maximization.

On the effects of public subsidies for severe and mild dependency on long-term care insurance

Extant theoretical work on long-term care (LTC) and its insurance has neglected an important fact: Benefits of LTC insurance as well as the amount of public subsidization of LTC can differ between severe and mild dependency. The objective of this paper is to revisit the study of optimal purchase of LTC insurance and its crowding out by public subsidies dissociating coverage for the risk of dependency in nursing home and of dependency at home. This study examines three prevalent models of LTC insurance indemnities commonly encountered in various LTC insurance markets. It also studies the presence of potential intergenerational moral hazard and shows how it drives the crowding out or crowding in of LTC insurance by public subsidization according to the insurance models and risk aversion behaviours.

Supply chain risk in grain trading: Inventories as real options for shipping grain

AbstractIntegrating trading and logistics is an important challenge in commodity trading. Trading and logistics are strategic decisions and are integral to most commodities including grain shipping by rail, in addition to other modes (barges, ocean shipping). There are substantial risks, such as the ordering and placement of rail cars. The other risk is having sufficient grain stocks to load rail cars. Alternatives for managing these risks include holding grain inventories and the strategic use of shipping options. The purpose of this study is to develop a model for determining an optimal grain inventory strategy for shippers. The real option methodology is used to value uncertainty in rail car velocity and determine optimal purchases and inventories. The results show the importance of integrating trading and logistics decisions and illustrate that inventories can be interpreted and valued as real options that are affected by uncertainties. Taken together, the real options indicate how much extra inventory shippers should maintain to mitigate risks and maximize profits.

Trend-Chasing Versus Minimalism: Selling Fewer, Better Products to Fashion-Sensitive Customers

Fashion sellers are sometimes critiqued for selling low durability products, resulting in waste. Blame is also directed at consumers, who purchase new fashions despite having accumulated a closet full of prior fashions. The “slow fashion” movement encourages sellers to produce higher durable products, thus supporting less frequent purchases by consumers. Using an infinite-time model and considering strategic consumer behavior, including their ability to accumulate a “closet” of varieties over time, we analyze the seller’s profit-maximizing price and product-durability decisions. We initially assume a static price but later analyze the potential profit gains from dynamic pricing. When analyzing a heterogeneous consumer market, we initially allow customers to vary (distributed uniformly) in their sensitivity to fashion. Subsequently, we explore alternative distributions for consumers’ fashion sensitivity and the correlation between their fashion sensitivities and product valuations. Using this framework, we show how the seller’s optimal price and durability decisions yield distinct shopping segments, which we refer to as the minimalist versus trend-chasers. We find that if the degree of fashion uncertainty is moderate, the seller’s optimal choice of product durability will support the emergence of both behaviors. As the variety uncertainty expands, if the seller’s costs are sufficiently low, it will support a throwaway culture via disposable products. Otherwise, given high costs, the seller optimally targets a slow fashion-type outcome, with consumers targeting reuse (with durability) rather than variety. Our findings shed light on consumers’ optimal purchasing behaviors in relation to both market parameters and the firm’s pricing and durability decisions.

Investment Decision Using Technical Analysis: A Study on Selected Stocks in Indian Stock Market

Technical analysis is a methodology in financial markets that asserts its capability to predict future stock directions by scrutinizing historical market data, patterns in price movements, and correlations between price and volume. Traders widely utilize these data in their decision- making processes. The approach also takes into account the real-time behavior of market prices and volumes, operating under the assumption that these are the most influential factors in determining a stock’s or market’s future trajectory. Investors often grapple with decisions regarding which stocks to buy, the optimal purchase quantities, and when to execute selling or holding actions to maximize profits in the trading period. This research delves into these considerations, offering insights for short-term traders by analyzing the stock price trends of twelve companies across three distinct sectors over the 2020-2022 period.

Replenishment And Automatic Pricing Strategy for Vegetable Products Based on Genetic Algorithm

In order to improve the accuracy of replenishment and pricing of vegetable commodities sold in supermarkets, and to maximise the benefits of vegetable commodities sold in supermarkets, this paper comprehensively establishes a strategic planning model for replenishment and automatic pricing of supermarkets. Firstly, we carry out descriptive statistical analysis and non-linear fitting on the historical sales data of supermarkets to find the distribution pattern of the sales of each single product of vegetables, and then find out the relationship between each single product of vegetables through correlation analysis. and then find out the relationship between each vegetable item through correlation analysis. After that, we built an LSTM time series forecasting model based on the cost data to predict the replenishment volume of the six vegetable categories in the coming week. Finally, in order to maximise the revenue of the superstore, we constructed a pricing planning model for the combination of vegetable categories, and solved the optimal purchase quantity and pricing strategy based on genetic algorithm.

Analisis Penerapan Metode Economic Order Quantity Sebagai Upaya Pengendalian Persediaan Bahan Baku Pada UD Imanuel Tompaso Baru

Economic Order Quantity is a method for determining the optimal purchase quantity so that there is no excess or shortage of raw materials. The aim of this research is to see how the Economic Order Quantity method is applied as an effort to control raw material supplies. The type of research used is descriptive qualitative. The research results show that there are quite large differences in the number of purchases, purchase time, and total inventory costs using the company's own method and the application of the Economic Order Quantity method. The number of purchases of raw materials according to the company is greater than using the Economic Order Quantity method, the company's purchasing frequency is more frequent than using the Economic Order Quantity method and there is a large comparison of total inventory costs. Therefore, it is hoped that companies can apply the Economic Order Quantity method to determine the optimal purchase quantity. The right repurchase time and at the same time can reduce total inventory costs.

Implementasi Penyusunan Anggaran Bahan Baku Dengan Metode Economic Order Quantity (EOQ) Pada Batik Sekar

This study analyzes the Economic Order Quantity (EOQ) as a raw material budgeting in Batik Sekar Jati. The purpose of this study is to determine whether the raw material inventory control applied by UKM Batik Sekar Jati has optimal in emphasizing production costs, and to determine the minimum total inventory cost. This research uses descriptive method that is quantitative in nature. Descriptive analysis techniques used to analyze data on the problem of controlling the raw material budget in an effort to reduce production costs are using the Economic Order Quantity (EOQ) method. The results showed that the company can save costs if the company uses the EOQ method, where the ordering cost was lower than the ordering cost according to the method that the company was currently running. The optimal purchase of raw materials according to the company's actual data was more than the purchase according to EOQ which purchases less. For budget control of raw materials Batik Sekar Jati can use Economic Order Quantity (EOQ) analysis, Safety Stock (SS), Reorder Point (ROP) and Total Cost (TC).

Research on Resource Allocation of Autonomous Swarm Robots Based on Game Theory

To address the issue of resource allocation optimization in autonomous swarm robots during emergency situations, this paper abstracts the problem as a two-stage extended game. In this game, participants are categorized as either resource-providing robots or resource-consuming robots. The strategies of the resource-providing robots involve resource production and pricing, whereas the strategies of the resource-consuming robots consist of determining the quantity to be purchased based on resource pricing. In the first stage of the game, the resource-providing robots use the Cournot game to determine the resource production according to market supply and demand conditions; in the second stage of the game, the resource-providing robots and the resource-consuming robots play the price game and establish the utility function of the swarm robots to seek the optimal pricing and the optimal purchasing strategy of the swarm robots. After the mathematical derivation, this paper demonstrates the existence of a single Nash equilibrium in the constructed game. Additionally, the inverse distributed iterative search algorithm solves the game’s optimal strategy. Finally, simulation verifies the game model’s validity. This study concludes that the designed game mechanism enables both sides to reach equilibrium and achieve optimal resource allocation.

Analysis of Supply Quantity Control of Argon Gas Raw Material At PT XY

The background in this research is that there is no method used to manage and control the quantity of raw material supplies at PT XY. This situation causes problems in ordering raw materials so that ordering costs and costs for storage will not be optimal. The purpose of this study was to determine the effect of applying the EOQ method to the total cost of raw material inventory at PT. XY. Raw material inventory costs can be minimized by applying the EOQ (Economic Order Quantity) method. This method can show the economic order quantity, optimal purchase frequency, time to reorder/ROP (Re-Order Point), and the amount of safety stock. Data collection was carried out using documentation techniques related to observation inventory documents and interviews with several informants, for data analysis using EOQ by calculating total inventory cost, ROP, reorder frequency and safety stock. The results of calculations and data analysis using the EOQ method show that the total cost of inventory is lower than the company's actual method.

Application of the Economic Order Quantity (EOQ) Method in Soybean Raw Material Inventory Control at the Haji Maman Tofu Factory in Matraman District, East Jakarta

This research is based on the lack of implementation of a method in controlling the inventory of soybean raw materials at Haji Maman’s Tofu Factory in the Matraman District of East Jakarta. Haji Maman’s Tofu Factory stil relies on daily records and sometimes uses estimates or assumption in its operational activities. In the procurement of raw materials, there is often an excess due to the lack of precise calculations regarding the ordering needs of raw materials, resulting in accumulation in the warehouse. In this study, the researcher utilized the Economic Order Quantity (EOQ) method, which determines the optimal quantity of raw material orders for each purchase, the optimal purchase frequency, and establishes the Re-Order Point. The approach and type of research used is a quantitative research method with a descriptive research approach. The objective of this study is to examine the implementation of the EOQ method in controlling the inventory of soybean raw materials at Haji Maman’s Tofu Factory in the Matraman District of East Jakarta. Data Collection was conducted through observation and interviews with Mr. Haji Maman, the owner. The data analysis technique employed was the EOQ method, taking into account purchase frequency, Re-Order Point, and Total Inventory Cost (TIC). The results of this study indicate that the optimal average frequency of raw material orders is 17 times per year. The total inventory cont using Haji Maman’s Tofu Factory policy amounts to Rp. 9,709,261, while using the EOQ method it amounts to Rp. 7,492,129, resulting in savings of Rp. 2,217,132 for Haji Maman’s Tofu Factory. The Re-Order Point is determined to be 216 kilograms.

Inventory control strategy: based on demand forecast error

PurposeThis study examines the relationship between demand forecasting error and retail inventory management in an uncertain supplier yield context. Replenishment is segmented into off-season and peak-season, with the former characterized by longer lead times and higher supply uncertainty. In contrast, the latter incurs higher acquisition costs but ensures certain supply, with the retailer's purchase volume aligning with the acquired volume. Retailers can replenish in both phases, receiving goods before the sales season. This paper focuses on the impact of the retailer's demand forecasting bias on their sales period profits for both phases.Design/methodology/approachThis study adopts a data-driven research approach by drawing inspiration from real data provided by a cooperating enterprise to address research problems. Mathematical modeling is employed to solve the problems, and the resulting optimal strategies are tested and validated in real-world scenarios. Furthermore, the applicability of the optimal strategies is enhanced by incorporating numerical simulations under other general distributions.FindingsThe study's findings reveal that a greater disparity between predicted and actual demand distributions can significantly reduce the profits that a retailer-supplier system can earn, with the optimal purchase volume also being affected. Moreover, the paper shows that the mean of the forecasting error has a more substantial impact on system revenue than the variance of the forecasting error. Specifically, the larger the absolute difference between the predicted and actual means, the lower the system revenue. As a result, managers should focus on improving the quality of demand forecasting, especially the accuracy of mean forecasting, when making replenishment decisions.Practical implicationsThis study established a two-stage inventory optimization model that simultaneously considers random yield and demand forecast quality, and provides explicit expressions for optimal strategies under two specific demand distributions. Furthermore, the authors focused on how forecast error affects the optimal inventory strategy and obtained interesting properties of the optimal solution. In particular, the property that the optimal procurement quantity no longer changes with increasing forecast error under certain conditions is noteworthy, and has not been previously noted by scholars. Therefore, the study fills a gap in the literature.Originality/valueThis study established a two-stage inventory optimization model that simultaneously considers random yield and demand forecast quality, and provides explicit expressions for optimal strategies under two specific demand distributions. Furthermore, the authors focused on how forecast error affects the optimal inventory strategy and obtained interesting properties of the optimal solution. In particular, the property that the optimal procurement quantity no longer changes with increasing forecast error under certain conditions is noteworthy, and has not been previously noted by scholars. Therefore, the study fills a gap in the literature.

Fixed and variable longevity income annuities in defined contribution plans: Optimal retirement portfolios taking social security into account

AbstractThis paper investigates retirees' optimal purchases of fixed and variable longevity income annuities using their defined contribution (DC) plan assets and given their expected social security benefits. As an alternative, we also evaluate using plan assets to boost social security benefits through delayed claiming. Using a calibrated life‐cycle model, we determine that including deferred income annuities in DC accounts is welfare‐enhancing for all sex/education groups examined. We also show that providing access to well‐designed variable deferred annuities with some equity exposure further enhances retiree well‐being, compared to having access only to fixed annuities. Nevertheless, for those facing the highest mortality rates, delaying claiming social security is mostly preferred, whereas those anticipating living longer than average will benefit more from using accumulated DC plan assets to purchase deferred annuities.

Trend Forecasting of the Top 3 Indonesian Bank Stocks Using the ARIMA Method

The number of investors in Indonesia increases annually. This is due to the growing popularity of investing, particularly stock investment. There are currently three largest equities in the banking industry, namely BBCA, BBRI, and BMRI. Stock prices fluctuate and form multiple patterns of price movements; therefore, investors must be able to recognize the patterns and trends of securities on the capital market in order to plan long-term investments, maximize potential profits, and reduce the risk of investment losses. In addition to knowing the trajectory of the stock market's trend, investors rely heavily on forecasting. Forecasting is necessary so that investors can anticipate future prices. The Autoregressive Integrated Moving Average (ARIMA) method is a frequently used method for forecasting time series data. In general, ARIMA is represented by the formula ARIMA (p, d, q), where p represents the Autoregressive (AR) order, d represents the difference, and q represents the Moving Average (MA) order. The trend of BBCA, BBRI, and BMRI stock data was effectively predicted using the ARIMA method. The results of this study are presented as diagrams of actual and forecasted data for the next 12 periods, as well as predictions of the optimal purchase price points for stocks. The ARIMA model of each stock also generates a low MAPE error value, with MAPE values of 4% for BBCA, 5% for BBRI, and 7% for BMRI. The MAPE value derived by each model is incorporated into the MAPE value with a high degree of precision, as it falls below 10%.

Integration of Economic Order Quantity (EOQ) Method and Min-Max Stock Method in Controlling Raw Material Inventory in The Furniture Industry

Indonesia is a country with a very rapidly developing economy in the manufacturing sector that produces furniture products. The existence of fierce competition between companies and the limited supply of raw materials makes the need for raw materials in the production process uncontrollable so that the desired goals of the company cannot be achieved. One of the factors that greatly influences is regarding the problem of raw material inventory levels. The issue of raw materials is very important for an enterprise because it has a great influence on the profit obtained.Economic Order Quantity (EOQ) and Min-Max Stock methods are carried out to control raw material inventories resulting in savings with optimal purchases by not experiencing inventory shortages.This research is expected so that waste and inventory that are too small can be known at intervals of time to be more effective and efficient in the production process.From the results of analysis and data processing, it can be seen that the number of orders for sonokeling wood raw materials needs is 17.670 m3 and amara wood raw materials are 4.325 m3 with an order frequency of 1 time and an ordering cycle distance of 33 days. So the delay in raw material inventory is influenced by several factors, namely the purchase price of wood raw materials, estimates in using raw materials, purchasing policies from companies and government policies related to regulations with the Multistakeholder Timber Verification and Legality System (SLVK) for inventory control are still lacking.

Minimizing Cost of Milk Raw Material Inventory Using the Economic Order Quantity (EOQ) Method

Inventory in raw materials is a very important thing to pay attention to by the company in carrying out its business processes. Economy Order Quantity (EOQ) is a method that is generally used in determining raw material availability policies in order to obtain minimum yields. This method will help plan raw material inventory in a company to be more efficient. In a manufacturing company, inventory is classified into three parts, specifically raw goods, semi-finished goods and finished goods. If a supply is sufficient then the production process will run smoothly. The purpose of this study is to make an analysis and make a control plan for milk raw materials contained in CV. Milkinesia Nusantara using the EOQ method. After analyzing the results obtained, the optimal purchase frequency is 240 times a year because milk raw materials are easily spoiled with a total purchase of milk raw materials every time one order is 119 liters, the main raw material is considered safe as much as 183 liters with what was ordered back when the raw material amounted to 457 liters. Then the total cost of availability of the company's raw materials, which was originally Rp. 85,023,395.55 - can be saved to Rp. 42,006,726, - so that the savings that occur are Rp. 43,016,669.55.

Facial Trustworthiness Influences Age Differences in Visual Attention Toward Credible Versus Non-credible Messages.

The literature on consumer decision-making and aging suggests that older adults make less optimal buying decisions than younger adults do, partly because older adults tend to perceive salespersons' faces as more trustworthy. This study aims to directly test the difference in the effect of perceived facial trustworthiness on buying intention between younger and older adults. It also aims to reveal the underlying mechanisms of this age-related difference by testing whether a more trustworthy face disrupts older adults' attention toward credible (vs. non-credible) information to a higher degree compared with younger adults. A sample of 92 younger (aged 18-37 years) and 83 older (aged 60-82 years) adults viewed advertisements for 32 products while their eye movements were captured by an eye tracker to measure their fixation duration (as an indicator of attention). The advertisements varied in terms of the credibility of the content and the trustworthiness of the salesperson's face. Both age groups showed higher buying intentions for products featured in advertisements with higher credibility and facial trustworthiness. When facial trustworthiness was lower, both age groups showed greater attentional preferences for credible over non-credible content. However, this distinction in attention disappeared in older but not younger adults with an increase in facial trustworthiness. Our findings suggest that although facial trustworthiness generally increases buying intention of both younger and older adults, it only reduces older (but not younger) adults' attentional discrimination between credible and non-credible content. This paper offers a novel and promising mechanism for the increase in fraud vulnerability in late adulthood.

A Learning-Based Optimal Decision Scenario for an Inventory Problem under a Price Discount Policy

This paper aims to design an inventory model for a retail enterprise with a profit maximization objective using the opportunity for a price discount facility given by a supplier. In the profit maximization objective, the demand should be increased. The demand can be boosted by lowering the selling price. However, lowering the selling price may not always give the best profit. Impreciseness plays a vital role during such decision-making. The decision-making and managerial activities may be imprecise due to some decision variables. For instance, the selling price may not be deterministic. A vague selling price will make the retail decision imprecise. To achieve this goal, the retailer must minimize impreciseness as much as possible. Learning through repetition may be a practical approach in this regard. This paper investigates the impact of fuzzy impreciseness and triangular dense fuzzy setting, which dilutes the impreciseness involved with managerial decisions. Based on the mentioned objectives, this article considers an inventory model with price-dependent demand and time and a purchasing cost-dependent holding cost in an uncertain phenomenon. This paper incorporates the all-units discount policy into the unit purchase cost according to the order quantity. In this paper, the sense of learning is accounted for using a dense fuzzy set by considering the unit selling price as a triangular dense fuzzy number to lessen the impreciseness in the model. Four fuzzy optimization methods are used to obtain the usual extreme profit when searching for the optimal purchasing cost and sale price. It is perceived from the numerical outcomes that a dense fuzzy environment contributes the best results compared to a crisp and general fuzzy environment. Managerial insights from this paper are that learning from repeated dealing activities contributes to enhancing profitability by diluting impreciseness about the selling price and demand rate and taking the best opportunity from the discount facility while purchasing.

Effects of BOPS implementation under market competition and decision timing in omnichannel retailing

One of the most significant fulfillment paradigms is the buy online and pick-up-in-store (BOPS) in omnichannel retailing. However, BOPS incurs many new operational challenges in the presence of competition, like competitive intensity and decision-making timing. To mitigate these problems, a multi-stage, non-cooperative duopoly game is constructed to investigate the competitive implications of introducing a BOPS strategy. First, we consider the situation when competing retailers simultaneously make BOPS decisions. Our results examine how heterogeneous customers choose optimal purchase decisions to maximize their utility and identify the mechanisms of three major effects (i.e., channel migration effect, price self-compensation effect, and limited market share effect). Meanwhile, we analyze when the intensity of competition is strong, intermediate, and weak respectively, the equilibrium strategy of different-type retailers is determined by which configuration. After that, with consideration of total consumer surplus, we shed light on how competing retailers obtain win–win configurations, i.e., both competing retailers and customers are better off, after deploying the BOPS. Subsequently, the investigation extends to broader cases with sequential decisions. Contrary to the common view that second-mover superiority, there exists first-mover superiority in deploying the BOPS. Finally, numerical examples are provided to analyze the impact of cross-selling benefits, fixed cost of BOPS, heterogeneous customer behavior, BOPS convenience, operation cost and competitive intensity on the optimal profit. Our finding was compared with previous studies to provide a novel way to design the BOPS for responding to competitors to maximize customer-oriented profits.