Quantity Discounts Research Articles

Optimizing Supply Chain Design under Demand Uncertainty with Quantity Discount Policy

In typical business situations, sellers usually offer discount schemes to buyers to increase overall profitability. This study aims to design a supply chain network under uncertainty of demand by integrating an all-unit quantity discount policy. The objective is to maximize the profit of the entire supply chain. The proposed model is formulated as a mixed integer nonlinear programming model, which is subsequently linearized into a mixed integer linear programming model and hence able to obtain a global solution. Numerical examples in the manufacturing supply chain where customer demand follows normal distributions are used to assess the effect of quantity discount policies. Key findings demonstrate that the integration of quantity discount policies significantly reduces total supply chain costs and improves inventory management under demand uncertainty, and decision makers need to decide a balance level between service levels and profits.

Cognitive Bias Sunk Cost in Marketing

The sunk cost cognitive bias, or sunk cost fallacy, is a human tendency to continue with: an investment, make a decision, business, couple or project based on the resources that have been invested, instead of making a current evaluation of the results. future benefits and costs. The objective of the research was to analyze the impact of sunk cost cognitive bias on customer decisions and its application in neuromarketing strategies. The specific objectives were: (i) Evaluate how sunk cost influences the perception of the value of products or services. (ii) Identify marketing strategies that exploit the sunk cost bias. (iii) Propose strategies to apply the sunk cost bias in marketing. The results show that the sunk cost bias significantly influences the perception of the value of products and services, affecting purchasing decisions, depending on the educational level, gender, and income level that influence the continuity in the use of products and services. due to previous investment of time or money; When purchasing expensive items customers feel obligated to continue using paid more even if it is not useful and used in full. The continuity in prepaid subscriptions, the perception of discounts for large quantities as a way to make better use of money, the probability of participating in loyalty programs for the accumulated rewards, the preference for payments and flexible cancellation, create a valuable perception of products they have previously invested in, the preference for repairing expensive items, and the perception of loss from a previous investment. It is concluded that the sunk cost cognitive bias significantly influences the decisions and behavior of a vast majority of customers, and how companies can use this information to design effective marketing campaigns.

IMPACT OF QUANTITY DISCOUNT ON PURCHASING COSTS IN BLENDING TECHNOLOGIES

Blending technologies are important in many sectors of industry, but are most prevalent in the chemical and food industries. They are playing an increasingly important role in the world economy despite the spread of electromobility. Nowadays, in addition to the technological aspects, there is a growing need to look at the logistical aspects, as logistics related costs account for a significant part of the cost of blending technologies. In this research work, the results of the analysis of the impact of quantity discounts, an important aspect of procurement activities related to blending technologies, are presented. A mathematical model is presented that can be used to investigate the impact of quantity discounts of components on profit and product quality. Based on scenario analyses carried out based on the mathematical model, it is demonstrated that the quantity discount can have a significant impact not only on total cost and profit, but also on the quality of the finished blended product.

A decision support system for sustainable supplier selection problem: Evidence from a radiator manufacturing industry

Supplier selection is the basis of a successful supply chain. It is also a key factor in improving the competitiveness of an organization. Being a complex process, the supplier selection plays an important role in upgrading the supply chain. The purpose of this research is to present an integrated approach based on fuzzy best-worst method (BWM) and failure mode and effects analysis (FMEA) for sustainable supplier selection considering seasonal quantity discounts and supplier risk for a radiator manufacturing company. Therefore, as the aim implies, this study addresses how organizations develop a sustainable supplier selection framework by integrating linear programming and decision-making in presence of seasonal discounts. To this end, fuzzy BWM and FMEA were employed to calculate weighting factors for different selection criteria and evaluate the supplier risk, respectively. Then, suppliers were ranked using the so-called technique for order of preference by similarity to ideal solution (TOPSIS). Finally, the discount type was examined by the LP-metric method. Results of the fuzzy BWM showed that the customer satisfaction was the criterion of highest priority, followed by long-term relationship, and then pollution control, among the total of 13 criteria considered in this work. Outputs of the TOPSIS referred to the supplier 3 as the top-ranked supplier, followed by supplier 6 and then supplier 1.

An integrated optimization model for procurement and production lot sizing and scheduling problems

Abstract Lot sizing is a prevalent issue within manufacturing companies, where determining the optimal procurement and production lot sizes is crucial for maximizing profits. This problem has become more complex, given that numerous suppliers can provide the same raw materials with different prices and quantity discount schemes. A company should also determine optimal carriers to deliver materials to the company’s warehouse. In a manufacturing process, the company should determine the optimal production lot size and its schedules. In this paper, a model was developed to solve simultaneously procurement and production lot sizing, as well as production scheduling problems. The model encompasses multiple suppliers offering quantity discounts, aiming to maximize company profit by accounting for various costs, including procurement, production, inventory, and quality costs. A case study is taken from a company producing noodles and its related derivative products to illustrate the application of the model. Based on the optimization results, the company obtained a total profit of IDR. 14,656,550,000 or $950,921.30 (the exchange rate of $1 at IDR. 15,413). The sensitivity analysis results show that the objective function is sensitive to changes in the purchase cost, sale revenue, and discount rate parameters. The decision variables for accepted product demand, product quantity, and the starting and completion time of product family are only sensitive to changes in certain parameters. Meanwhile, the decision variables for product inventory, product backlog, raw material inventory, and purchased raw material quantity are sensitive to the changes in all the analyzed parameters.

The discount consolidation effect: How brands can present quantity discounts more effectively

AbstractHow should brands present quantity discounts to increase consumer appeal? We propose that, compared to distributed presentations that spread a discount across units (e.g., buy two, get 30% off on each unit), consolidated presentations that place the discount on a single unit (e.g., buy two, get 60% off on the second unit) can lead to a higher purchase likelihood. Four studies demonstrate this “discount consolidation effect” across a variety of consumption contexts, product categories and price levels. The studies show that this effect occurs because compared to consolidated presentations of a quantity discount, distributed presentations can lead to less favorable perceptions about the quality of the discounted product and about the savings offered. We also identify an important boundary condition, illustrating that when the discount offered is substantively small in size, the effect is attenuated. These findings add to the theoretical literature and offer actionable managerial insights.

Deterring the Gray Market: Product Diversion Detection via Learning Disentangled Representations of Multivariate Time Series

A gray market emerges when some distributors divert products to unauthorized distributors/retailers to make sneaky profits from the manufacturers’ differential channel incentives, such as quantity discounts. Traditionally, manufacturers rely heavily on internal audits to periodically investigate the flows of products and funds so as to deter the gray market; however, this is too costly given the large number of distributors and their huge volumes of orders. Owing to the advances in data analytics techniques, the ordering quantities of a distributor over time, which form multivariate time series, can help reveal suspicious product diversion behaviors and narrow the audit scope drastically. To that end, in this paper, we build on the recent advancement of representation learning for time series and adopt a sequence autoencoder to automatically characterize the overall demand patterns. To cope with the underlying entangled factors and interfering information in the multivariate time series of ordering quantities, we develop a disentangled learning scheme to construct more effective sequence representations. An interdistributor correlation regularization is also proposed to ensure more reliable representations. Finally, given the highly scarce anomaly labels for the detection task, an unsupervised deep generative model based on the learned representations of the distributors is developed to estimate the densities of distributions, which enables the anomaly scores generated through end-to-end learning. Extensive experiments on a real-world distribution channel data set and a larger simulated data set empirically validate our model’s superior and robust performances compared with several state-of-the-art baselines. Additionally, our illustrative economic analysis demonstrates that the manufacturers can launch more targeted and cost-effective audits toward the suspected distributors recommended by our model so as to deter the gray market. History: Accepted by Ram Ramesh, Area Editor for Data Science & Machine Learning. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72031001, 72301017, 72371011, and 72242101]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0155 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0155 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Strategic supplier selection and order allocation for sustainable development of small and medium-sized enterprises: insights from a case study

Purpose Efforts to implement supplier selection and order allocation (SSOA) approaches in small and medium-sized enterprises (SMEs) are quite restricted due to the lack of affordable and simple-to-use strategies. Although there is a huge amount of literature on SSOA techniques, very few studies have attempted to address the issues faced by SMEs and develop strategies from their point of view. The purpose of this study is to provide an effective, practical, and time-tested integrated SSOA framework for evaluating the performance of suppliers and allocating orders to them that can improve the efficiency and competitiveness of SMEs. Design/methodology/approach This study was conducted in two stages. First, an integrated supplier selection approach was designed, which consists of the analytic hierarchy process and newly developed measurement alternatives and ranking using compromise solution to evaluate supplier performance and rank them. Second, the Wagner-Whitin algorithm is used to determine optimal order quantities and optimize inventory carrying and ordering costs. The joint impact of quantity discounts is also evaluated at the end. Findings Insights derived from the case study proved that the proposed approach is capable of assisting purchase managers in the SSOA decision-making process. In addition, this case study resulted in 10.89% total cost savings and fewer stock-out situations. Research limitations/implications Criteria selected in this study are based on the advice of the managers in the selected manufacturing organizations. So the methods applied are limited to manufacturing SMEs. There were some aspects of the supplier selection process that this study could not explore. The development of an effective, reliable supplier selection procedure is a continuous process and it is indeed certainly possible that there are other aspects of supplier selection that are more crucial but are not considered in the proposed approach. Practical implications Purchase managers working in SMEs will be the primary beneficiaries of the developed approach. The suggested integrated approach can make a strategic difference in the working of SMEs. Originality/value A practical SSOA framework is developed for professionals working in SMEs. This approach will help SMEs to manage their operations effectively.

Performance of Quantity Discount Contract Under Supply and Demand Disruptions

Performance of Quantity Discount Contract Under Supply and Demand Disruptions

Friends or Foes? The Effect of IoT Platform Entry Into Smart Device Market Under Quantity Discount Pricing Contract

Friends or Foes? The Effect of IoT Platform Entry Into Smart Device Market Under Quantity Discount Pricing Contract

A detailed study of a prominent dark web fentanyl trafficking organization

ABSTRACT Overdose deaths in North America have soared, primarily because of the spread of illegally manufactured fentanyl. This paper uses detailed qualitative and transaction-level data to analyse an early and prominent dark web fentanyl-selling operation. The data record the date, drug, quantity, and selling price for 5,589 transactions comprising 872,659 items sold for a little over $2.8 million through AlphaBay. Findings include that the organisation sustained an impressive sales growth rate of approximately 15% per week, compounded. Increasing order sizes by a factor of ten reduced the price per pill by approximately 25% for Oxycodone and 50% for Xanax. Those steep quantity discounts imply large price markups when selling further down the distribution chain. Such high growth rates and price markups suggest that it might be difficult to constrain supply by shutting down individual organisations, since any remaining organisations may be able to quickly grow to fill unmet demand.

Determining the Transaction Value on the Annual Rebates or Quantity Discounts - A Review on the Supreme Court Decision 2018du7714 -

Determining the Transaction Value on the Annual Rebates or Quantity Discounts - A Review on the Supreme Court Decision 2018du7714 -

A New Fuzzy Stochastic Integrated Model for Evaluation and Selection of Suppliers

In light of the rapid rate of change and unforeseen occurrences seen in the realms of technology, market dynamics, and the wider business landscape, there is a growing need for the inclusion of uncertainty and risk factors in the realm of supply chain planning. Supplier evaluation and selection (SES) is a major strategic decision area where the impact of uncertainty and risk can be more proactively dealt with. A review of extant literature reveals that there is a strong need for developing practitioner-oriented and more comprehensive frameworks and models to mitigate both the capability- and performance-related risks, in the context of SES decisions. This paper presents an integrated model to support SES decisions involving quantity discounts and multiple planning periods under stochastic conditions. The proposed model employs the Fuzzy Analytical Hierarchy Process (FAHP), Fuzzy Evaluation Based on Distance from Average Solution EDAS (EDAS-F), and fuzzy stochastic goal programming (FSGP) to effectively address the above requirements. A case study from a garment manufacturing industry is used to demonstrate the efficacy of the proposed model. The findings of the study provide confirmation that the suggested FSIM has the ability to provide substantial advantages in the context of making choices related to quantity discounts in SES. The proposed FSIM model incorporates the use of FAHP and EDAS-F techniques to effectively reduce the number of suppliers to a manageable level, taking into consideration capability-based risks. Additionally, fuzzy stochastic goal programming (FSGP) is employed to mitigate performance-based risks, enabling the selection of suppliers and the allocation of orders among them. The paper contributes to the literature by proposing a comprehensive framework to solve the SES problem, considering certain practical situations faced by organizations.

Exploitation of material consolidation trade-offs in multi-tier complex supply networks

While consolidation strategies form the backbone of many supply chain optimisation problems, exploitation of multi-tier material relationships through consolidation remains an understudied area, despite being a prominent feature of industries that produce complex made-to-order products. In this paper, we propose an optimisation framework for exploiting multi-to-multi relationship between tiers of a supply chain. The resulting formulation is flexible such that quantity discounts, inventory holding, and transport costs can be included. The framework introduces a new trade-off between tiers, leading to cost reductions in one tier but increased costs in the other, which helps to reduce the overall procurement cost in the supply chain. A mixed integer linear programming model is developed and tested with a range of small to large-scale test problems from aerospace manufacturing. Our comparison to benchmark results shows that there is indeed a cost trade-off between two tiers, and that its reduction can be achieved using a holistic approach to reconfiguration. Costs are decreased when second tier fixed ordering costs and the number of machining options increase. Consolidation results in reduced inventory holding costs in all scenarios. Several secondary effects such as simplified supplier selection may also be observed.

Hedonic Decomposition of Beer Prices: Consumer Ratings and Quantity Discounts

A hedonic regression framework is proposed for evaluating the determinants of beer prices and consumer ratings. This study specifically addresses the endogeneity problem associated with the impact of consumer ratings on beer prices using a set of beer sensory and chemical characteristics as instrumental variables in the estimation procedure. The results suggest that beer prices tend to be influenced by consumer ratings and the objective characteristics of beers, while consumer ratings tend to be influenced by the sensory and chemical characteristics of beers. Also, limited evidence is found for the use of quantity discounts by beer producers.

A Two-Storage Inventory Model with Trade Credit Policy and Time-Varying Holding Cost under Quantity Discounts

Background: What are the cumulative influences of pricing, promotions of commodities, order size-based discount opportunities, and alternative warehousing scenarios on effective decision-making concerning inventory management? It is observed that the prices and promotion of products influence the demand rate. The shortage can be partially backlogged, and the backlogging rate depends on the waiting time. Also, discount and trade credit facilities may be available when purchasing items. This paper describes a novel inventory control model regarding optimal warehousing decision-making scenarios. Methods: This paper includes the facts in its hypothesis and examines the overall impact of the mentioned issues on profitability. The inventory carrying scheme associated with the proposed model consists of both rented and owned warehouse facilities in which the cost increases linearly with time. The numerical and visual simulation succeeds the mathematical approach to analyze the proposed inventory model in Mathematica software. Results: The results show that a price hike enhances profit despite the negative impact on demand creation. Also, promotion frequency favors profitability, suppressing the corresponding costs. Another managerial intuition is revealed through the numerical result that the stock should be held in a rented warehouse when deterioration in the owned warehouse increases, despite the cost of a rented warehouse. Conclusions: Besides several mentioned management insights, this study includes several existing models as particular cases and tackles challenges in the analytical optimization approach. This study leads toward the consequences of future research scopes with industry-based raw data.

Review Presentation of Different Economic Order Quantity (EOQ) Models and Their Application

In the context of inventory management, this review presentation offers a thorough overview of several Economic Order Quantity (EOQ) models and their real-world uses. It explores the fundamental EOQ model and broadens to incorporate models that account for perishable items, quantity discounts, and scarcity prices. The talk also looks at the many sectors in which these models are used to optimize order amounts, save costs, and improve operational efficiency. Businesses may improve their inventory control strategies, realize considerable cost savings, and increase performance by making educated decisions based on a thorough grasp of the various EOQ models and their practical implementations.

A multi-product and multi-period supply chain network design problem with price-sensitive demand and incremental quantity discount

Demand is an important factor in supply chain network design (SCND). Among the many factors that affect demand, the impact of price cannot be underestimated. This study, for the first time, provides enterprises with direct multi-period pricing and demand decision solutions in SCND by simultaneously considering the impact of price-sensitive demand and incremental quantity discounts on the network. In this way, a mixed-integer nonlinear programming model is established based on the relationships between price and demand functions to maximize the overall profit of the supply chain. Due to the nonlinear characteristics of the model, this study proves the existence of the optimal solution of the problem and the feasibility of implementing the following two algorithms by analyzing the nature of the problem. The first algorithm is based on the second-order cone programming (SOCP) method, and the second algorithm is based on the outer-approximation method. The results of numerical experiments at different scales show that the SOCP method can obtain the optimal solution for small and medium-scale experiments. In contrast, the outer-approximation method can find approximate optimal solutions within a reasonable time for large-scale experiments. The results confirm that considering incremental quantity discount can significantly enhance the profitability of supply chain networks in long-term planning. Finally, some future research directions in the field of SCND are discussed.

On the EOQ Models with Advertisement-Price-Dependent Demand and Quantity Discount with Expiration Date Under Shortage

On the EOQ Models with Advertisement-Price-Dependent Demand and Quantity Discount with Expiration Date Under Shortage

An adaptive GLNPSO method for inventory replenishment supply chain problem with multiple-warehouse policy and budget consideration

This paper presents an efficient computation tool for a multiple-warehouse inventory replenishment lot-sizing problem with supplier selection, quantity discounts, and budget constraint. The cost model of the considered inventory replenishment issue is formulated as a mixed-integer nonlinear programming (MINLP) problem. With the proposed MINLP problem being an NP-hard problem, the MINLP cost model was optimized by using a novel global–local neighbor particle swarm optimization algorithm (TPAGLNPSO), inspired by two-phase self-adaptive inertia weight and improved constraint-handling technique. Apart from introducing the velocity index to switch the search scheme from exploration to exploitation, the constraint-handling technique was utilized to guide and guarantee the particle search toward undiscovered regions in the solution space. The stability analysis for the proposed novel TPAGLNPSO was also conducted using the stochastic process theorem. The corresponding parameter selection ranges were obtained based on the convergent conditions. Finally, eight industry cases were provided as examples to evaluate the performance of the proposed TPAGLNPSO method, after which a sensitivity analysis for the MINLP cost model was performed. The results demonstrate the competitive performance of the proposed TPAGLNPSO optimizer in terms of solution quality (with near-optimal results) than the optimal solutions obtained from Lingo software and computational effort (CPU time). Such findings can provide practitioners marginal insights into the effective management of the supply chain inventory replenishment problem.