Price-sensitive Demand Research Articles

Supply Chain Optimization Considering Fluctuations in Price Sensitive Demand

Supply Chain Optimization Considering Fluctuations in Price Sensitive Demand

A stochastic inventory model with price-sensitive demand, restricted shortage and promotional efforts

This paper is attempt to develop a stochastic inventory model with quadratic price-sensitive demand. Objective function is developed by incorporating promotional efforts to boost the market demand, preservation technology to reduce the rate of deterioration, proportionate shortage time and partial backloggings. The proposed work is to generalise the stochastic demand with different probability distributions and their comparisons. The objective is to find the optimal price, optimal replenishment, and optimal preservation technology investment while optimizing the total profit per unit time. In the case of partial backlogging and lost sale, we deduced the optimal replenishment schedules for respective price and preservation technology cost. Also, we shown analytically and graphically that the total profit per unit time is a concave function with respect to per unit time, price, and preservation cost. The theoretical implications have been validated by useful results and numericals. Also, we examine the impact of various parameters for the best course of action. The conclusions drawn from the assessment might be useful for managerial purposes.

Optimal pricing policy for deteriorating items with continuous compounding under price-sensitive demand and shortages

Optimal pricing policy for deteriorating items with continuous compounding under price-sensitive demand and shortages

Effect of inflation on EOQ model with multivariate demand and partial backlogging and carbon tax policy

The concept of green inventory systems is very important for economic growth and development in the era of sustainable development. There is a special need for green inventory systems to identify and manage perishable products since spoilage and deterioration can lead to significant losses of items, which negatively affect the satisfaction of consumers. As perishable products decay continuously (such as vegetables, fruits, milk, juices, frozen foods, baked foods), their demand is adversely affected as well as customers' purchasing decisions. The more realistic assumption is a price-sensitive demand. As well as deterioration rates, perishable products have an expiration date-dependent deterioration rate. Further, inventory holding, and the deterioration of perishables contribute significantly to carbon emissions when operating the inventory system. A carbon tax policy is more flexible and effective when it comes to reducing carbon emissions due to its environmental conscious nature. We develop two sustainable inventory procedures for perishable items based on a practical scenario in which the buyer has a limited storeroom. So, to achieve sustainability goals, a model for inventory management for perishable products based on expiration dates is presented in this paper. We distinguish between two inventory schemes: (i) one that allows shortages and fractional backlogs, and (ii) one that does not allow shortage. In both schemes, both the decay rate and demand function show an upward trend against storage time. Since the decay rate increases with storage time, it is assumed that the cost of storing items is linearly related to storage time. numerical examples along with a real-life case study are presented to validate the inventory schemes after several decision-making findings have been derived.

A vendor-buyer supply chain model with price, warranty and green sensitive demand under greening cost, warranty cost and revenue sharing contract

A vendor-buyer supply chain model with price, warranty and green sensitive demand under greening cost, warranty cost and revenue sharing contract

Integrated optimisation of pricing, manufacturing, and procurement decisions of a make-to-stock system operating in a fluctuating environment

ABSTRACT Manufacturers experience random environmental fluctuations that influence their supply and demand processes directly. To cope with these environmental fluctuations, they typically utilise operational hedging strategies in terms of pricing, manufacturing, and procurement decisions. We focus on this challenging problem by proposing an analytical model. Specifically, we study an integrated problem of procurement, manufacturing, and pricing strategies for a continuous-review make-to-stock system operating in a randomly fluctuating environment with exponentially distributed processing times. The environmental changes are driven by a continuous-time discrete state-space Markov chain, and they directly affect the system's procurement price, raw material flow rate, and price-sensitive demand rate. We formulate the system as an infinite-horizon Markov decision process with a long-run average profit criterion and show that the optimal procurement and manufacturing strategies are of state-dependent threshold policies. Besides that, we provide several analytical results on the optimal pricing strategies. We introduce a linear programming formulation to numerically obtain the system's optimal decisions. We, particularly, investigate how production rate, holding cost, procurement price and demand variabilities, customers' price sensitivity, and interaction between supply and demand processes affect the system's performance measures through an extensive numerical study. Furthermore, our numerical results demonstrate the potential benefits of using dynamic pricing compared to that of static pricing. In particular, the profit enhancement being achieved with dynamic pricing can reach up to 15%, depending on the problem parameters.

Food products pricing theory with application of machine learning and game theory approach

ABSTRACT Demand for perishable food is sensitive to product prices and is affected by the prices of similar or alternative products. While brand loyalty influences the demand for products, determining a reasonable price requires a precise pricing strategy. In this paper, a pricing model for perishable food is presented in which the brand value of the product and the price of other manufacturers as competitors are considered. To this end, this study first predicts the price of competitors using a combination of optimized Neural Networks and presents an optimized model using a Genetic Algorithm. This algorithm combines a Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and a Genetic Algorithm (GA). The proposed model is then used to merge with a game-theory model for the pricing of perishable foods. In this game-theory model, pricing approaches are developed based on identified prices of competitors. In the coordination contract game-theory model, Multi Retailer- one Supplier and Price-sensitive demand of Perishable product are developed with and without quantity discount contract. Obtained results indicate that independent procurement provides retailers with higher profit, while lower profit will be presented when coordination is not considered. Also, with coordination, the ordering cycle increases, and the ordering frequency decrease.

Bidding strategy for wireless charging roads with energy storage in real-time electricity markets

The combination of wireless charging roads and energy storage systems is a promising option for electric vehicle charging because of their capabilities in mitigating range anxiety of electric vehicle drivers. Wireless charging road operators can purchase electric energy by submitting price-sensitive demand bids in real-time electricity markets. Efficient bidding strategies are crucial to minimizing the energy costs for providing wireless charging services. In this study, we first propose a composite statistical model based on graph signal processing and linear regression to forecast the future locational marginal prices (LMPs) in a power network. Then an estimate of future electric load on each wireless charging road is derived by simulating its traffic flow using a point queue-based traffic flow model. An efficient price-sensitive bidding strategy for each individual wireless charging road is developed based on its LMP forecast, wireless charging load estimate, and a model predictive control framework. Our numerical example shows that the proposed price-sensitive demand bidding strategy reduces the electric energy cost for operating a wireless charging road with an energy storage system by 6% compared to a baseline bidding strategy.

Sustainable Economic Production Quantity (SEPQ) Model for Inventory Having Green Technology Investments - Price Sensitive Demand with Expiration Dates

Sustainable Economic Production Quantity (SEPQ) Model for Inventory Having Green Technology Investments - Price Sensitive Demand with Expiration Dates

A Deteriorating Inventory Model under Overtime Production and Credit Policy for Stock- and Price Sensitive Demand Function

This paper develops an overtime production model for demand rate to be a function of price and stock-level. Companies manufacturing rate fluctuates with the change in stock-level and demand rate. To control the deterioration up to some extent, the system introduces preservation technology investment. The article permits a two-level credit policy for flexible financing. The model calculates feasible profit value under preservation technology investment, production period and selling price.  Conclusively, a sensitivity analysis related to different inventory parameters is performed to study the dependency of optimal values on parameters.

Optimal inspection and advance payment policy for deteriorating items using differential evolution metaheuristic

The deterioration of items such as fruits, eatables, etc. may influence the adjacent units and intercept the supply. In many circumstances, the existence of a spoiled unit accelerates the deterioration process. The degradation effect can be mitigated by removing the affected unit. This research investigation explores an impact of frequent inspection on the spoiling items in an inventory system and can be used as a step forward to reduce food wastage due to mutual spoilage. In many business transactions, the retailer has the ability to make judgments regarding advance payment. The present work attempts to develop a lot-sizing model for price-sensitive demand under the premise of partial advance payment and deterioration rate as a function of inspection frequency. We suggest an optimal prepayment strategy from the retailer’s point of view. A mixed-integer programming problem is formulated and solved by using a genetic algorithm (GA) and differential evolution (DE) algorithm based on different mutation strategies. It is established that DE performs better in comparison to GA. Based on some statistical measures, we have selected the mutation scheme which is most suitable for the proposed problem. The inventory-related measures are established for the proposed problem and used to present a numerical simulation and optimal inventory policy. The numerical findings show that the ideal inspection and prepayment strategy boost profitability by reducing spoilage. The managerial insights are discussed that may be beneficial in decision-making for inspection and advance payment.

Who benefits from air service agreements? The case of international air cargo operations

Air service agreements between countries spell out diverse provisions, such as reciprocal capacity allocations. We analyze how such agreements, which regulate total capacity in markets, affect total welfare and its distribution. We consider the interactions between two competing cargo airlines, their end customers, logistics service providers—who serve as intermediaries between the former two agent types—as well as the policy makers. Accounting for demand uncertainty, we model this interaction as a two-stage game. In the first stage, the policy makers coordinate equal capacity allocations to the two asset providers, whereas in the second stage, the asset providers compete over prices in the spot market. Solving the model, we characterize the pricing strategies employed by the two competing capacity-constrained asset providers in the spot market when facing price sensitive demand from end customers. We further analyze the corresponding capacity decisions.We then compare this coordinated competitive setting with a collaborative setting where the two asset providers can (virtually) merge and act as a monopoly. We find that, compared with the monopoly setting, the coordinated duopoly results in lower capacity, lower profits to the asset providers, larger benefits to both end customers and logistics service providers, and overall lower total welfare. These results suggest that policy makers shall hold an open attitude towards a higher level of cooperation among the asset providers. We carry out robustness checks to verify our insights hold with three, rather than two, asset providers, with different demand distributions, when capacity is costly and when asymmetric capacities are allowed in the spot market.

Optimal selection of supply chain financing programmes for a financially distressed manufacturer

Availability of working capital is a challenge for the smooth functioning of any supply chain, particularly when the manufacturer is financially distressed. This gives rise to the issue of selection of optimal supply chain finance programmes. Addressing the limited working capital issue of a financially distressed manufacturer, we construct in this study a Stackelberg game model of a dual-channel supply chain and a multi-echelon supply chain, as an extension, to analyze the effects of reverse factoring and non-reverse factoring (external financing and trade credit) supply chain finance programmes on supply chain profit under price-sensitive demand. We also analyze the impacts of hybrid reverse factoring (an extension of the reverse factoring programme) on supply chain profit. We find that the supply chain finance programme with reverse factoring brings higher profits to the manufacturer and retailer than the other programmes. However, when the market size is sufficiently large, the trade credit programme generates a higher total supply chain profit than the other programmes. We also find that the retailer and manufacturer prefer reverse factoring and non-reverse factoring to hybrid reverse factoring, even though the total supply chain profit is almost equal in both cases. We also provide a case study to illustrate the model outcomes.

Sustainable production inventory model with greening degree and dual determinants of defective items

In the world of environmental scarcity, customers are keen to invest in a supply chain player who makes environmentally sound choices. Owing to this, the present study develops a production inventory model with demand dependent on greening degree and price. The production process is faulty and thereby some fraction of defective items gets piled up along with the perfect products. Efficient management of defectives is indeed crucial in achieving sustainable development goals. Thus, the proposed study develops two models to handle the fraction of defective goods. The first model considers the salvaging of the defectives, which is quite applicable to the goods that are not suitable for any further dispensation. The second model considers the reworking option, which is pragmatic for most of the reworkable inventory goods. A sustainability assessment of both models is done and findings suggest the better performance of the second model. Numerical analysis is presented to impart the validity of the proposed study. A comprehensive sensitivity analysis is also done to demonstrate model features. The developed study is inspired by the recent advancements in the global supply chains in terms of greening levels. The key contribution lies in the framework of decision-making of various manufacturers and analyzing the impact of greening degree and pricing. With dual maneuver options for the defective items, the problem throws thought-provoking insights into the strategies of manufacturers and their vital decisions. Lingo and Mathematica software are used to solve the developed model. The present study contributes to the burgeoning field of sustainable supply chains by incorporating green and price-sensitive demand, carbon emissions, and dual options for handling defectives.

Pricing strategy and order quantity allocation with price-sensitive demand in three-echelon supply chain

A key objective of this paper is to investigate the supply chain coordination frameworks to minimize the double marginalization effect in the case of price-sensitive demand. This paper investigates the impact of pricing strategy and ordering/production policy on the performance of a three-layer supply chain by using the Salop spatial model. The Nash Game approach determines the best price and order quantity to locally maximize the expected profits of the individual members in the decentralized supply chain. The total profit of the centralized structure is used as the benchmark to validate the channel member’s decision on the selling price and order quantity. A numerical analysis has been conducted to confirm the theoretical results.

Trade Credit Policies for Supplier, Manufacturer, and Retailer: An Imperfect Production-Inventory System with Rework

In this study, we developed a trade credit policy for a three-layer supply chain consisting of a supplier, a manufacturer and a retailer. We propose an optimal production rate and selling price for the manufacturer and the retailer under an imperfect production system. The suggested coordination policy optimizes the profit of each supply chain member. Two models were formulated for two real-life strategies respectively. The first one is a collaborative (integrated) system and the second one is a Stackelberg leadership system. Both strategies were analyzed for various credit periods, respectively offered by the supplier to the manufacturer, by the manufacturer to the retailer, and by the retailer to the customers, by considering price-sensitive demand and a certain replenishment rate. Finally, we concluded which strategy will be better for inventory management under the given restrictions in the form of propositions. The concavity property for the net profit function was established with respect to the selling price and the production rate, which was also described graphically and analyzed by numerical examples.

Capacity Investment and Process Efficiency at Flexible Firms

When firms are endowed with volume flexibility, capacity investment may influence the subsequent production process via affecting the structure of production cost. Yet, the strategic interaction between capacity and production decisions has not been adequately addressed. In this paper, we consider two firms serving one market under price sensitive and uncertain demand. Firms incur costs to build capacity and produce. The firm’s capacity affects production cost through its influence on process efficiency, while the specific effects on the two firms differ. We establish a two-stage game-theoretical framework to characterize the problem and obtain two firms’ equilibrium capacity and production decisions. The results show that the firm whose process efficiency is more prone to improving as capacity expands will invest in more capacity and achieve a more efficient process, provided that production is not overly labor and material intensive. However, its competitor will spin off capacity and suffer profit reduction. Moreover, the firms are encouraged to scale up capacity investment to achieve a more efficient process in an expanding and more volatile market.

Multi-objective modelling of sustainable closed-loop supply chain network with price-sensitive demand and consumer’s incentives

Closed-loop supply chains (CLSCs) are essential for maximising the value creation over the entire life cycle of a product. The design of these networks is increasing due to growing online businesses and rising sustainability awareness. This study develops a multi-objective optimisation model for sustainable CLSC network problem considering supply chain’s inherent complexity (multi-echelon, multi-product, multi-mode and multi-period nature) along with price-sensitive demand, consumer’s incentives and different quality levels of product. The proposed model seeks to optimise total cost and carbon emissions generated by production, distribution, transportation, and disposal activities. A two-stage algorithm, through the integration of the Non-Dominated Sorting Genetic Algorithm (NSGA-II) and Co-Kriging approach is utilised to determine the trade-off between costs and carbon emissions in the CLSC network. Data collected from a leading European household appliance company was used to analyse and interpret the developed model. The results show that the proposed two-stage approach provides robust outcomes and is computationally less expensive than the epsilon constraint approach. The study evidences the positive effects of incentive pricing on returned goods in the reverse logistics network and provides multiple trade-off solutions for supply chain managers to make informed decisions.

Personalized pricing with a price sensitive demand

This paper assesses the profit and welfare effects of firms’ ability to charge personalized prices in markets where consumer demand is sensitive to price changes. In a mill pricing model, regardless of demand elasticity, personalized pricing (PP) raises consumer surplus at the expense of profits. In contrast, in a delivered pricing model, if demand is sufficiently elastic, PP boosts profits at the expense of consumer surplus and overall welfare. Moving from PP in a mill to a delivered pricing model, benefits industry profits and harms consumer surplus and welfare.

Olive oil supply chain design with organic and conventional market segments and consumers’ preference to local products

Recent market studies showed that the demand for organic and local agrifood products is increasing despite their higher prices. The agribusiness actors should therefore rethink the supply chain configuration to cope with new market trends characterized by the rise of the organic segment and the increase of consumers' preference to more local products. This study focuses on the olive oil sector and proposes a mixed-integer non-linear optimization model for the design of olive oil supply chains while incorporating organic and conventional market segments and considering, for each segment, a supply chain proximity- and price-sensitive demand. The model is developed with the collaboration of olive oil producers in the Mediterranean area. Thanks to this industrial collaboration, we account for real-world practices and constraints and apply the model to a realistic case study. We first linearize the model and show that it can be efficiently solved with commercial optimization softwares. Based on numerical experiments, we derive a series of managerial insights that are applicable to the considered case study, some of them are not intuitive. For instance, we show that an increase in consumers’ preference to more local products may lead the producer to offer products with a more global supply chain. The conventional product variety may be produced with a more local supply chain than the organic (premium) variety. Finally, offering a mix of organic and conventional varieties instead of only one variety would lead to implementing a more local supply chain.