Добірка наукової літератури з теми "Newsvendor formula"

This paper investigates the impact of emergency order in a price-dependent newsvendor setting. To this end, we compare two ways handling the excess demand: the excess demand is lost and a penalty cost is incurred, or the excess demand can be satisfied by an emergency order. Which way is better depends on the emergency purchase cost [Formula: see text] in emergency-order way and the price [Formula: see text] plus penalty cost [Formula: see text] in lost-sales way. For a risk-neutral newsvendor, our results indicate that, when [Formula: see text] is not larger than [Formula: see text], the emergency order way can lead to smaller order quantity and higher expected profit. We continue to discuss the impact of newsvendor’s risk aversion and demand uncertainty on the optimal decisions of the two ways. Theoretical analysis and numerical examples indicate that when the emergency purchase cost is not high, the differentials of the optimal order quantities and expected profits will be larger as the degree of risk aversion/demand uncertainty increases. What is more, we prove that there exists a threshold value of the emergency purchase cost so that the two ways handling excess demand can obtain the same expected profit, and this threshold value increases as the degree of risk aversion decreases.

This paper studies the stochastic differential Stackelberg game in a continuous-time newsvendor problem with a return policy, in which one supplier sells products to one retailer and the two parties make the decisions sequentially to maximize their own expected profits. When the demand process is a general jump-diffusion process, we provide a general formula for the equilibrium if it exists. When the demand rate is an Ornstein–Uhlenbeck (O–U) process, we prove the existence and uniqueness of the equilibrium and find an explicit expression for the equilibrium. Computational results show that the return policy has significant impact on the Stackelberg equilibrium.

Waste reduction in retail is a fundamental problem for sustainability. Among waste reduction approaches such as recycling and donation, stock management based on demand estimation which leads to mitigate waste generation and maintain a high profit is expected to play an important role. However, demand estimation is generally difficult because fluctuations in sales are quite volatile, and stock-out leads to incomplete demand observation. Here, we propose data science solutions to estimate non-stationary demand with censored sales data including stock-outs and realize scientific stock management. Concretely, we extend a non-stationary time series analysis method based on Particle Filter to handle censored data, and combine it with the newsvendor problem formula to determine the optimal stock. Moreover, we provide a way of pricing waste reduction costs. A method to verify consistency between the statistical model and sales data is also proposed. Numerical analysis using actual Point-Of-Sales data in convenience stores shows food waste could be reduced several tenths percent keeping high profits in most cases. Specifically, in cases of foods disposed of frequently about 75% of working days, food waste decreases to about a quarter with the profit increases by about 140%. The way of pricing waste reduction costs tells new insights such as 27% waste reduction is achieved by 1% profit loss. Our method provides a practical solution for food waste reduction in the retail sector.

In this paper, we formulate a stochastic periodic-review inventory model with lead-time that considers both duration- and quantity-dependent inventory costs, and show that the optimality equation has the same format as the standard newsvendor problem with a modified demand distribution. We suggest (i) an approximation of the optimal order-up-to level, based on discretization of the inventory cost accounting, and (ii) two heuristic formulas, based on the two-moment normal approximation of the modified demand distribution, and on an approximated deterministic model. We evaluate the performances of these formulas using a Brownian motion demand process under different scenarios, and discuss their advantages and disadvantages.

PurposeUsing information systems via data mining and cluster analysis technologies, consumers' strategic behaviour can be measured, and their patience levels can be accurately described. This paper investigates the retailer's pricing and ordering policies when facing strategic consumers with different levels of patience and discusses the impacts of consumers' patience levels and proportions on retailers' maximum expected profits.Design/methodology/approachBy cluster analysing transaction data on the number of websites visited, browsing time and purchase decision time, consumers' patience levels can be obtained. The authors formulate a newsvendor model considering customers' different patience levels. Three scenarios are investigated: two segments of consumers with two different levels of patience (Scenario I), multiple segments of consumers with different levels of patience (Scenario II) and a continuum of consumers whose levels of patience follow a continuous distribution (Scenario III). Then, general formulas are deduced for retailers' optimal prices, ordering quantities and profits.FindingsUnder Scenario I, if the proportion of less patient consumers is greater (less) than a threshold, the retailer's optimal price is equal to the less (more) patient consumers' reserve price. Under Scenario II, once the proportion of fully strategic consumers exceeds a certain threshold, the retailers' optimal price is equal to the fully strategic consumers' reserve price regardless of consumers' patience levels and proportions. Under Scenario III, the retailer's pricing and ordering policies depend on the distribution of their patience level.Originality/valueFew studies have considered consumers' different levels of patience when making retailer pricing and ordering decisions. In this paper, strategic consumer behaviour is measured, and consumers' patience levels and proportions are obtained by cluster analysing consumer transaction data recorded by an information system. Three scenarios in which strategic consumers may be heterogeneous and have different patience levels are investigated. The results can guide retailer decision-making.

Purpose: The purpose of the study is to identify, design and apply a method for operational production planning. The purpose of the method is that it takes into account the respective demand patterns of the articles, which determines the choice of calculation model and that the method also takes into account the production capacity limitation set. The result can be applied as a basis for decision-making for small and medium-sized manufacturing companies that are facing an expansion. Objective: The following research question was the main objective of this study.How would an appropriate operational planning method be shaped according to the specific characteristics of the products and their diverse demand patterns in small and medium-sized manufacturing companies with capacity limiters? Method: The study is a qualitative one-case study with quantitative elements. The empirical data used in the study was collected through unstructured and semi-structured interviews where the snowball effect has been applied and also observations have been performed. Results: The study has enabled a higher degree of utilization of the capacity limitation set, by developing standardized block sizes. The first step was to identify demand patterns into which the studied articles could be categorized. The demand pattern was applied in order to be able to apply the correct calculation model to the correct article. The calculation model was then standardized by applying PoT to be able to obtain standardized block sizes. The method was illustrated in a Gantt chart to demonstrate its usefulness.
Syfte: Studiens syfte är att identifiera, utforma och applicera en metod för operativ produktionsplanering. Ändamålet med metoden är att den tar hänsyn till artiklarnas respektive efterfrågemönster vilket avgör valet av beräkningsmodell samt att metoden tar även hänsyn till produktionens kapacitetsbegränsning ställare. Resultatet kan appliceras som beslutsunderlag för små samt medelstora tillverkande företag som står inför en expansion. Forskningsfråga: Följande forskningsfråga var objektet för denna studie. Hur skulle en lämplig operativ planering formas utefter artiklarnas specifika egenskaper och deras olikartade efterfrågemönster hos små och medelstora tillverkande företag med kapacitetsbegränsningen ställare? Metod: Studien är en kvalitativ en-fallstudie med kvantitativa inslag. Den empiriska data som använts i studie än insamlad genom ostrukturerade samt semistrukturerade intervjuer där snöbollseffekten tillämpats och även observationer har utförts. Resultat: Studien har möjliggjort en högre utnyttjandegrad av kapacitetsbegränsningen ställare, genom att framta standardiserade blockstorlekar. Första steget var att identifiera efterfrågemönster som de studerade artiklarna kunnat kategoriseras in i. Efterfrågemönstret har tillämpats för att kunna använda rätt beräkningsmodell på rätt artikel. Beräkningsmodellen standardiserades därefter genom tillämpning av PoT för att kunna erhålla standardiserade blockstorlekar. Metoden illustrerades i ett Gantt-schema för att påvisa dess användbarhet.