Inventory Pricing Research Articles

An Analysis Of The Recording And Assessment Methods Of Inventory At Carnova Variasi Kota Bengkulu

A better inventory system can increase profit, while a less efficient system can erode profit and make a business less competitive. The main benefit of inventory formation is to protect the company from unforeseen events and disruptions in business. The aim of this research is to analyze the recording and assessment methods of inventory at Carnova Variasi Kota Bengkulu. The recording and assessment methods of inventory, specifically for car seat covers, rice noodle carpets, and android head units at Carnova Variasi Kota Bengkulu over a 12-month period from May 2023 to April 2024, will be compared with the Financial Accounting Standards for Micro, Small, and Medium Entities (SAK EMKM) no.9 of 2018. Data collection was done through documentation and research analysis using comparative descriptive analysis. The results were in line with the recording method recommended by SAK EMKM regarding inventory. This is because Carnova Variasi Kota Bengkulu recorded inventory at the time of purchase and sale into detailed purchase and sales reports leading to a perpetual recording method system. Additionally, Carnova Variasi Kota Bengkulu used stock cards as a means of controlling its inventory. However, there are still some discrepancies such as not journalizing at the time of purchase and sale. The inventory assessment system implemented by Carnova Variasi Kota Bengkulu is the weighted average valuation method, which complies with SAK EMKM regarding inventory. This can be seen in the determination of the cost of goods sold taken from the average inventory price by dividing the total purchase price by the existing inventory. However, the inconsistency lies in the fact that Carnova Variasi Kota Bengkulu did not prepare financial statements, resulting in the absence of inventory presentation in the balance sheet.

Inventory pricing model under different carbon emission policies

Considering carbon emission in inventory management model is a hot topic in current academic circles. Studying inventory pricing can provide managers with better managerial insights. Economic order quantity model mainly solves the inventory management problem of demand determination. However, due to the complex market environment in real life, it is difficult to meet the conditions of constant demand. In addition, with the enhancement of low-carbon awareness of enterprises, the study of pricing model based on low-carbon policies has also attracted the attention of a large number of scholars. The robust optimization theory is applied, the inventory pricing model of demand fluctuation under carbon cap, carbon tax and carbon cap-and-trade policies is considered, the robust equivalence form of the problem is studied. The profit comparison under different policies is given. Numerical experiments show that the carbon cap-and-trade policy can better control the carbon emission of enterprises.

Multi-level and refined forest resources asset inventory price system construction

Multi-level and refined forest resources asset inventory price system construction

Integrated Pricing and Inventory Control for Perishable Products, Taking into Account the Lack of Backlog and Inventory Management Policy by the Seller

Abstract Recently, utilizing appropriate inventory control policy and determining the optimal selling price for various goods has been the main topic of scientific and industrial research. Inventory management policy 1 by the seller is one solution that improves the chain’s performance by creating coordination between members of the supply chain. The current study attempts to devise an integrated model of inventory pricing and control under the inventory management policy by the seller for perishable goods with shortages is considered. The purpose of presenting the model is to determine the optimal price, the optimal repayment time, and the order size, in order to maximize the profit. To acquire those optimal values, the profit functions of the buyer and the seller are taken into account. Given the results acquired, it is demonstrated that at any cost, the repayment time is unique and optimal. It is concluded that with the optimal recovery time available, the objective function is a concave function of price, and its optimal value is available. Furthermore, utilizing the inventory management policy by the seller could be a proper means to reducing retailer costs while raising their profit.

Modelling retail inventory pricing policies under service level and promotional efforts during COVID-19

Most supply chain and production systems faced multiple manufacturing and delivery challenges during COVID-19 and transformed their supply chain for improved customer service. These challenges are mostly related to stocking and managing the inventory flow throughout the supply chain (from manufacturer to end consumer). Due to the COVID-19 travel and movement restrictions, inventory reorganisation is necessary for fulfilling consumer demand with adequate service facilities. The safety and serviceability of inventory consumption is the primary concern of many retail grocery stores and consumers. Maintaining the supply of groceries items during and post COVID-19 time without disruption is a real operational and policy challenge. Therefore, this research tries to solve an inventory pricing mechanism and retailer's profit under the optimal service level and retailers' promotional efforts. The proposed optimisation model is validated in the grocery retail sector. The grocery retail market situation is modelled when the demand for the grocery product (which may be essential items) and selling price depending on the investment in item promotional effort and consumer serviceability. The retail grocery store's investment in the product promotional efforts, such as awareness of the item availability and no-contact delivery which, may attract consumers. Therefore, the proposed inventory consumption is modelled with an optimisation problem to maximise the store profit with the optimal investment in promotional activities and service facilities to the consumers and maintain an optimal replenishment cycle. The optimisation model is tested with three different cases (no investment in promotional efforts, no investment in service facility, and investment in both) of investment to maximise the retailer's profit and stock availability. The optimality results depicted that investment in promotional efforts and service facility givens higher profit to the retailer. The proposed optimisation model's policy implications would help grocery retail store managers to develop operational strategies for maximising profit with the optimal service level and promotional efforts.

Numerical solving of generalized Black-Scholes differential equation using deep learning based on blocked residual connection

Option pricing in the financial derivatives market is worth studying. When the volatility and the rate of return are not fixed in Blacks-Scholes (BS) equation, the analytical form of the solution is difficult to be obtained. In this paper, we propose deep learning based on blocked residual connection method (DLBR) for the numerical solving of BS equations. The validity period and inventory price are the inputs of the network, while the option price is the solution. Neurons on the multiple hidden layers are processed in blocks and each block is connected to the residuals. The boundary conditions are constructed as a part of the numerical solution and the loss function is minimized to obtain the optimal network parameters. The DLBR method has strong applicability and overcomes the shortcomings of extreme learning machines. Three experiment calculated the numerical solution of the BS equation about European options and promoted the option pricing model. Compared with the existing algorithms such as finite element method and physics-informed neural networks (PINN) method, the obtained numerical solution has higher precision and smaller error, which shows the feasibility and superiority of the proposed method.

Dynamic Pricing with Money‐Back Guarantees

Money‐back guarantees (MBGs), which allow customers to return products that do not meet their expectations, are widely used in the retail industry. In this study, we study a retailer's MBG policy with dynamic pricing of limited inventory. A key decision for the retailer is to decide whether to offer MBGs. When the product can be returned instantly, we find that the optimal MBG policy is a simple threshold policy: given the inventory level, it is optimal to offer an MBG if and only if the remaining selling time is longer than a threshold. Moreover, the threshold is decreasing with the inventory level. We also address the problem of dynamic pricing with positive return times. Due to the complexity, we analyze the associated fluid model, which has an infinite number of constraints. We consider a series of relaxations that have a nested structure and use the Lagrangian approach to explicitly solve these relaxed problems. This allows us to develop an iterative approach that is guaranteed to solve the fluid model in finite iterations. Our numerical analysis shows that the deterministic solution is asymptotically optimal for the stochastic system.

Managerial Regret and Inventory Pricing

In this paper, we study a regretful seller’s problem of selling a fixed number of goods over a finite and known time horizon. The seller engages in counterfactual thinking to compare her selected price with other forgone alternatives. If a forgone alternative (ex post) generates a better outcome than the selected one, then the seller experiences regret. We characterize the pricing decision of a regretful seller and find that, although regret leads the seller to set a price that is lower than that set by an unbiased seller, the regretful seller employs decision policies whose structure is similar to those of the unbiased seller: the price decreases with the remaining inventory and increases with the time-to-go. Interestingly, we find that the seller who has a greater number of goods does not necessarily receive greater revenue. This paper was accepted by Manel Baucells, behavioral economics and decision analysis.

Volatility Spillover between Stock Returns and Oil Prices during the Covid-19 Pandemic in ASEAN

This study points to increase global monetary integration as a result of rising volatility spillovers. As a result, analyzing volatility spillovers for international areas that expand and improve through the usage of inventory returns and oil prices is critical. The EGARCH model is used to explore the Volatility Spillovers of oil agencies in five ASEAN international areas during the Covid-19 Pandemic. To assess the interrelationships of the ASEAN stock index as well as the path of volatility, data were acquired from five global sites with very large volatility spillovers, namely Indonesia, Malaysia, Singapore, Thailand, and Vietnam. The findings show that this search is critical for ASEAN traders as well as Filipinos. Furthermore, because accurate forecasting of volatility spillover in global equity markets is required to reduce portfolio risk, this search has a substantial and viable significance.

Inventory Pricing Strategy Considering Product Returns for Deteriorating Items with Multi-Variate Dependent Demand

Inventory Pricing Strategy Considering Product Returns for Deteriorating Items with Multi-Variate Dependent Demand

Inventory pricing strategy considering product returns for deteriorating items with multi-variate dependent demand

Inventory pricing strategy considering product returns for deteriorating items with multi-variate dependent demand

An Optimal Inventory Pricing and Ordering Strategy Subject to Stock and Price Dependent Demand

This article considers the deterministic optimal control problem of profit maximization for inventory replenished at a variable rate and depleted by demand which is assumed to vary with price and stock availability. Optimal policies for the inventor, product order rate and price are derived using the maximum principle. Bounds on the maximum price possible are also derived.

Optimal Ordering Policy for Retailers with Bayesian Information Updating in a Presale System

In this study, we investigate inventory allocation and pricing strategies for retailers by incorporating demand information into the issue of inventory allocation during the presale period. In a presale system, retailers offer presale goods at a price lower than the retail price. By offering products at a discount, retailers may attract additional demand. In addition, this system enables retailers to reduce the uncertainty of market demand and establish a strategy for inventory allocation based on the results of presales. A Bayesian approach was employed to analyze and update demand information, and inventory allocation was formulated as a newsvendor problem to determine the optimal policy that maximizes retailer profit. A numerical analysis was conducted to validate the effectiveness of the proposed strategy. Results suggest that the proposed strategies can support retailers by more accurately predicting demand and achieving higher profits with less inventory. Furthermore, retailers can experience greater benefits from risk-averse customers than from risk-neutral customers.

Implementation of Rfid in Inventoy Management

Radio frequency detection has been one of the most developed and anticipated technologies in recent years. RFID is an automated framework for identification that uses RFID tags or navigation systems for remote storage and retrieval of data. RFID is perhaps the most exponentially evolved supply chain management technology. They are also used to integrate different processes that are part of daily operations and to optimize transport of goods across the production process. RFID facilitates real-time tracking of production, inventory prices and demand. On the other hand, the RFID solution is actually costly, unstandardized; manufacturers who undertake sequential projects are less in number and are dissatisfied with privacy concerns. This article explores the benefit and drawbacks of these labels in inventory management.

Managerial Regret and Inventory Pricing

In this paper, we study a regretful seller’s problem of selling a fixed number of goods over a finite and known time horizon. The seller engages in counterfactual thinking to compare her selected price with other forgone alternatives. If a forgone alternative (ex post) generates a better outcome than the selected one, the seller experiences regret. We characterize the pricing decision of a regretful seller and find that, although regret leads the seller to set a price that is lower than that set by an unbiased seller, the regretful seller employs decision policies whose structure is similar to those of the unbiased seller: the price decreases with the remaining inventory and increases with the time-to-go. Interestingly, we find that the seller who has a greater number of goods does not necessarily receive greater revenue.

Two warehouse dispatching policies for perishable items with freshness efforts, inflationary conditions and partial backlogging

Application of freshness preservation technologies in case of perishable inventory management can play a vital role in mitigating the losses that occur due to deterioration of perishable inventory, typical perishable inventory includes Agri-fresh products like fresh fruits, fresh meat, fresh vegetables, seafood and packed foods. In case of perishable inventory price as well as freshness both decide the demand, so supplying a fresh product at a competitive price creates more customer satisfaction and makes a firm profitable. This paper develops two warehouse dispatching policies, first in, first out (FIFO) and last in, first out (LIFO) for perishable items taking deterioration into account. Demand varies as a function of price and freshness keeping effort. As the price increases and freshness keeping efforts decreases, demand decreases, and vice versa. Stockout is allowed, and during the stockout period as the waiting time increases backlogging rate decreases exponentially. Inflation is considered while calculating different costs. The objective of this paper is to determine the optimum price per unit item and lot size to maximize the profit. The sensitivity of the models is examined through the design of experiment. The present models are applicable for perishable items that start losing their quality immediately after arriving into the system.

Information on Income, Dividend Policy and the Impact of Inflation on Stock Prices

This lookup ambitions to determine the have an impact on of income information, dividend coverage and inflation on inventory prices via the ride of organizations on the LQ45 index of the Indonesian Stock Exchange from 2017 to 2019. Several previous studies on stock fees have proven extraordinary results. Therefore, every other learn about is needed to re-evaluate the stock fee theory. The populace of this study is the LQ45 index of 45 companies. In order to obtain the 3-year commentary period (2017 to 2019) and 57 remark effects of 19 pattern companies, the sampling approach used is a purposeful sampling method. Research facts comes from sample businesses that can be downloaded from the Indonesian Stock Exchange website, and inflation information can be downloaded from the internet site of the Central Bureau of Statistics. The facts analysis techniques used are descriptive statistical analysis and more than one regression analysis. The first facts analysis technique completed is descriptive facts and classical speculation testing. The a couple of regression evaluation continues with speculation testing. The consequences of this study partly show that, as shown via the t-test of zero <0.05, data about profits and dividend coverage will affect the stock price, while the t-test of 0.726 <0.05 indicates that inflation will now not affect the inventory price. The end result of this study is that, at the identical time, earnings information, dividend coverage and inflation will all have an effect on the inventory price, while trying out zero <0.05 (5%).

Dynamic Nonlinear Pricing of Inventories over Finite Sales Horizons

Nonlinear pricing is frequently used by fast-fashion retailers (e.g., Zara). Discounts of the form “Buy 2, get 20% off” or “Buy 3 and save 30%” are becoming common. As revealed in a recent study by Gallego et al. (2019), nonlinear dynamic pricing can significantly improve a company’s revenue, by as much as 30%–90%, especially when inventories are high and the relative value of the marginal unit is neither too low nor too high, a situation that is common in fast-fashion retailing. They find that a single quantity discount threshold can capture most of the benefits of nonlinear pricing. The simplicity of this policy validates observed practice and concentrates managerial efforts to dynamically selecting the threshold and the depth of the discount.

Analysis and optimisation of perishable inventory with stocks-sensitive stochastic demand and two-stage pricing: A discrete-event simulation study

ABSTRACT This paper studies an important real-life perishable inventory management problem, which has not been properly addressed by the existing research. In the problem setting, a number of agents are renting spaces/shelves from a landlord, the inventory capability of each agent is limited to the rent space, and agents are risk-averse due to their budget. The objective is to maximize the profits of the agents/retailers while minimizing waste and improving customers’ shopping experience. To approach the problem, we develop a discrete-event simulation model to determine the optimum inventory scheme for displaying perishables. The proposed simulation model can show sales trajectories with respect to various features and estimate profit curves. Our model improves profits by choosing the right replenishment threshold and price discount. This study has implications to agents/retailers who deal with inventory scheduling and pricing problems for perishables under stochastic conditions.

Behavioral Modeling of Inventory Sharing Among Decentralized Retailers: An Experimental Study

In recent years, inventory sharing, in addition to the traditional within-firm model, also appears in the sharing-economy model where different firms can share their inventory via an internet platform. Two prominent issues, shaping the behaviors of inventory sharing systems, are the commitment of the transfer price and the commitment to share. In this paper we investigate, through a behavioral lens, the decisions of transfer price, order quantity and the resulting supply chain efficiency in different transshipment commitment scenarios. In particular, we consider two transfer price commitment settings (ex-ante or ex-post, depending on whether or not the inventory transfer price is committed before demand realization) and two sharing commitment rules (automatic or voluntary, depending on whether or not inventory sharing are pre-committed). Our experimental results suggest that individuals set transfer prices much lower, and order much less, than what Nash equilibrium predicts. We also find substantial treatment effects where rational model can not explain. The magnitude of disparities relative to the Nash equilibrium prediction appears to be most substantial in the situation where transfer price is set ex ante and inventory sharing is voluntary. Motivated by these observations, we develop a behavioral model that incorporates quantal response equilibrium and fairness concerns. Empirical analysis indicates that our model provides a compelling explanation of the behavior observed in the data. This study provides implication on the design of the commitment rules in decentralized inventory sharing platforms.