Buyer Demand Research Articles

Channel coordination with price discount mechanism under price‐sensitive market demand

AbstractThis paper considers a two‐echelon supply chain with single vendor single buyer for trading a single product. The buyer's demand has been assumed to be price‐sensitive. We have assumed three‐level inspection at the vendor's end in order to maintain good quality of the items delivered to the buyer. We have developed models for both decentralized and centralized scenarios and have determined the optimal solution using the basic concepts of analytic geometry and algebra. In addition, we have proposed a price discount mechanism, where the vendor has provided discounts on the purchase cost to the buyer against the buyer's (increased) order quantity. Finally, in order to illustrate and validate the proposed model, a numerical example and sensitivity analysis is carried out, which has provided some important managerial implications. It has been observed from the numerical study that the proposed price discount policy coordinates the supply chain and has improved the profitability of the supply chain and its members.

Funding New Ventures with Digital Tokens: Due Diligence and Token Tradability

We study funding mechanisms for new ventures based on cryptographic tokens enabled by blockchain technology, such as initial coin offerings (ICOs). The infrastructure built around blockchain allows for efficient trading of these tokens. Thus the due diligence process can be crowdsourced beyond the potential early adopters, as favorable assessments of the venture can be leveraged with speculative token purchases exceeding the buyer's individual demand. We develop a model for an entrepreneur considering digital tokens to finance a new venture, focusing on token tradability and broader crowdsourcing of due diligence. We then compare funding via digital tokens with funding from traditional financing sources like venture capital as well as with pre-sale crowdfunding with non-tradable rewards. We find that tradable digital tokens are more attractive when there is higher uncertainty about market demand, which is frequently the case when developing applications of new technologies, such as blockchain-based platforms. In such cases crowdsourcing due diligence benefits from the information contained in the private valuations of the early potential adopters. Token tradability leverages that information and increases the amount that can be financed, thus enabling new ventures with higher development costs. The increased funding comes at the cost of a lower digital token price and lower total profit for the entrepreneur, but may still be preferable to the alternatives, if such alternatives are available at all. This work makes a contribution to the emerging literatures on digital cryptographic tokens and on crowdfunding new ventures.

Optimal control of procurement policy optimization with limited storage capacity

This research aims to minimize the cost of inventory procurement, i.e raw material procurement. Companies must be able to determine when to order supplies, when to use inventory in the warehouse, and when to postpone buyers demand based on prices, demand, and limited storage capacity. Therefore, the optimal control theory is used to find the optimal solution of inventory procurement problem by combining JIT (just-in-time), warehousing, and backlogging procurement policy. The necessary conditions of Pontryagin’s Maximum Principle (PMP) and Karush-Kuhn-Tucker (KKT) are met in order to obtain the optimal raw material procurement cost. The Hamiltonian is locally optimal along a singular arc. A numerical example is given to compare the generated optimal policy and simple procurement strategy. The cost of inventory procurement that is combining JIT, warehousing, and backlogging policies is more optimal than the cost of inventory procurement that just applies JIT procurement policy. The inventory procurement problem with JIT, warehousing, and backlogging policies can be divided into two cases, i.e. zero final stock and non-zero final stock. The cost of inventory procurement with zero final stock is more optimal than non-zero final stock.

Investigating carbon emissions in a production-inventory model under imperfect production, inspection errors and service-level constraint

This paper discusses an integrated inventory model for vendor-buyer system with stochastic demand and service-level constraint. The buyer's demand follows a normal distribution and the lead time depends upon lot size. The buyer uses a periodic review policy to manage the inventory level. An imperfect production processes and inspection errors are considered in the model. Carbon emissions and energy impacts which are generated from transportation activity and production activity are also included in the model. An incentive and penalty policy are adopted to reduce the carbon emissions generated from supply chain system. The model contributes to the current literature by allowing the inclusion of imperfect production, inspection errors, carbon emissions and setup cost reduction. The objective of the model is to determine the review period, setup cost and the number of deliveries so that the total cost incurred has the minimum value. An iterative procedure is suggested to solve the model. A numerical example and sensitivity analysis are performed to illustrate the application of the model and to show the impact of the changes of key parameters on model behaviour.

A mixed integer linear programming model for dynamic supplier and carrier selection problems

The dynamic supplier selection problem (DSSP), which occurs when no supplier can fulfil the buyer's total demand due to various limitations, has received attention from academics. However, to the best of our knowledge, there is no publication that addresses the DSSP and carrier selection simultaneously. Given that products are sourced from different suppliers, the choice of carrier is important in order to satisfy demand at the minimum total cost. In this paper, we present a model that integrates the DSSP and carrier selection under uncertain delivery performance; that is, a portion of orders are not delivered on time and a portion of delivered goods are imperfect and hence are rejected by the buyer. We propose a mixed integer linear programming (MILP) model and apply it to a case where a buyer is sourcing multiple items from multiple suppliers and the decision has to be made on a multi-period planning horizon. A numerical example has been tested to show the effectiveness of the model. We assume that backlogging is allowed and the effect of demand fluctuation is analysed.

A mixed integer linear programming model for dynamic supplier and carrier selection problems

The dynamic supplier selection problem (DSSP), which occurs when no supplier can fulfil the buyer's total demand due to various limitations, has received attention from academics. However, to the best of our knowledge, there is no publication that addresses the DSSP and carrier selection simultaneously. Given that products are sourced from different suppliers, the choice of carrier is important in order to satisfy demand at the minimum total cost. In this paper, we present a model that integrates the DSSP and carrier selection under uncertain delivery performance; that is, a portion of orders are not delivered on time and a portion of delivered goods are imperfect and hence are rejected by the buyer. We propose a mixed integer linear programming (MILP) model and apply it to a case where a buyer is sourcing multiple items from multiple suppliers and the decision has to be made on a multi-period planning horizon. A numerical example has been tested to show the effectiveness of the model. We assume that backlogging is allowed and the effect of demand fluctuation is analysed.

Solving a continuous periodic review inventory-location allocation problem in vendor-buyer supply chain under uncertainty

In this work, a mixed-integer binary non-linear two-echelon inventory problem is formulated for a vendor-buyer supply chain network in which lead times are constant and the demands of buyers follow a normal distribution. In this formulation, the problem is a combination of an (r, Q) and periodic review policies based on which an order of size Q is placed by a buyer in each fixed period once his/her on hand inventory reaches the reorder point r in that period. The constraints are the vendors’ warehouse spaces, production restrictions, and total budget. The aim is to find the optimal order quantities of the buyers placed for each vendor in each period alongside the optimal placement of the vendors among the buyers such that the total supply chain cost is minimized. Due to the complexity of the problem, a Modified Genetic Algorithm (MGA) and a Particle Swarm Optimization (PSO) are used to find optimal and near-optimum solutions. In order to assess the quality of the solutions obtained by the algorithms, a mixed integer nonlinear program (MINLP) of the problem is coded in GAMS. A design of experiment approach named Taguchi is utilized to adjust the parameters of the algorithms. Finally, a wide range of numerical illustrations is generated and solved to evaluate the performances of the algorithms. The results show that the MGA outperforms the PSO in terms of the fitness function in most of the problems and also is faster than the PSO in terms of CPU time in all the numerical examples.

The Politics of Upgrading in Global Value Chains: The Case of Rwanda’s Coffee Sector

Two parallel tracks of research on economic transformation in developing countries have operated at a distance from each other over the last two decades. A global track – global value chains/global production networks (GVC/GPNs) – has focused on the increasing interconnectedness of global trading networks and has overlooked the role of the state and the explanatory power of domestic political economy. Meanwhile, a domestic track – including literature on developmental states, industrial policy and political settlements – has tended to take a methodologically nationalist perspective to examine economic transformation in developing countries, with limited reflections on external economic and political pressures. This paper contributes to an emerging stream of literature that examines how the domestic and global scales influence how developing country governments and firms tackle the challenge of economic upgrading. By combining insights from the political settlements and GVC/GPNs literature, this paper examines the Rwandan government’s attempt at upgrading its coffee production to enter specialty coffee markets. It shows how the existing GVC/GPNs literature makes an important contribution to describing how multipolar governance influences the pathways for economic upgrading in Rwanda’s coffee sector, but that even where access is granted, benefits are captive to the demands of international buyers, and gains for some have not translated across the sector. Insights from the political settlements literature showcase how domestic politics influences who benefits from insertion to GVC/GPNs and how the unequal provision of opportunities affects political stability.

Determining optimal uptime considering an unreliable machine, a maximum permitted backorder level, a multi-delivery plan, and disposal/rework of imperfect items

To remain competitive in unstable business environments, production managers of contemporary manufacturing firms must simultaneously achieve some important operating goals while planning batch fabrication. These goals may include ensuring high product quality, satisfying buyer demand with timely deliveries and acceptable stock availability, lowering cost, and keeping the batch fabrication process undisruptive. In production planning, permitting shortage backordering is an effective policy to reduce stock holding cost, given that stock availability meets an acceptable level of customer’s satisfaction. In addition, in an actual manufacturing environment, random equipment breakdowns and imperfect items are inevitable due to unpredictable issues in manufacturing processes. This study develops a decision support model to explore a fabrication uptime problem considering an unreliable machine, a maximum permitted backorder level, a multi-delivery plan, and disposal/rework of imperfect items to assist manages with the aforementioned operating goals. With the help of mathematical modeling, an optimization method, and a solution-seeking algorithm, the proposed precise model is capable of not only determining the optimal fabrication uptime policy, but also revealing the individual and joint impact(s) of various key system parameters on the problem, thus facilitating managerial decision-making.

The failure of eco-forestry as a small-scale native forest management model in Papua New Guinea

Deforestation and forest degradation are problems common to many tropical countries, including Papua New Guinea (PNG). These problems are often a result of the environmentally unsustainable logging practices of industrial logging companies. Beginning in the 1990s, six organizations attempted to mitigate the deforestation and forest degradation occurring in PNG by facilitating small-scale native forest management by Indigenous forest landowners. All six organizations utilized an ‘eco-forestry’ approach, involving selective harvesting of timber combined with the milling of timber by Indigenous forest landowners using portable sawmills. The lumber produced was sold into local and international markets as sustainable certified under Forest Stewardship Council (FSC) principles. The use of portable sawmills was also intended to provide the landowners with a greater financial return compared to the timber royalty payments they could receive from logging companies. This study used a literature review and interviews with key informants from the eco-forestry organizations and the PNG Forest Authority to assess the effectiveness of variants of the portable sawmilling model. We found that each of the six organizations were unsuccessful in developing a financially viable model for small-scale native forest management by Indigenous forest landowners in PNG. All the Indigenous landowners were unable to continue their portable sawmill operations once the donor funding of the eco-forestry organizations ceased. In addition, the operators of portable sawmills struggled to produce lumber that met the quality and quantity demands of buyers, who ultimately ceased purchasing the lumber. Furthermore, the Indigenous landowners struggled to adhere to the FSC principles, resulting in a loss of FSC certification. The study identifies a need for a new small-scale native forest management model in PNG. We recommend that future research involve collaboration with private sector businesses and professionally trained operators to inform the development of a small-scale forest management model which is financially profitable while also adhering to the principles of eco-forestry.

All‐units discounts as a partial foreclosure device

AbstractWe investigate the strategic effects of all‐units discounts (AUDs) used by a dominant firm in the presence of a capacity‐constrained rival. Due to the limited capacity of the rival, the dominant firm has a captive portion of the buyer's demand for the single product. As compared to linear pricing, the dominant firm can use AUDs to go beyond its captive portion by tying its captive demand with part of the competitive demand and partially foreclose its small rival. When the rival's capacity level is well below relevant demand, AUDs reduce the buyer's surplus.

Critical success factors of public–private partnership projects: a comparative analysis of the housing sector between Malaysia and Nigeria

ABSTRACTAlthough public–private partnership (PPP) strategy has gained popularity as an alternative strategy in the delivery of public works projects, its application has come with mixed results. Numerous studies have attributed the success of PPP projects to contextual peculiarities of different administrative settings. This comparative study uses case study approach to analyse the similarities and differences of the critical factors that influence the success of PPP housing projects in Malaysia and Nigeria. The study reveals that while ‘equitable risk allocation’, ‘stable political system’, and ‘reputable developer’ are the most critical critical success factors (CSFs) in the case of Nigeria, ‘action against errant developer’, ‘consistent monitoring’, and ‘house buyer's demand’ are the most critical factors that influenced the success the PPP housing project in Malaysia. The findings widen the understanding of the contextual issues that dictates the relative importance of CSFs of PPP in developing countries.

Feeding Children, Communities, and Souls

Feeding Children, Communities, and Souls

Peningkatan Kapasitas Dan Kualitas Pengolahan Produk Hasil Laut Di Surabaya Untuk Menembus Pasar Ekspor

In an effort to exploit Indonesia's rich marine potential of a variety of fish and given the large contribution of the growth of the fishery sector to Indonesia's gross domestic product, the seafood processing industry needs to improve the competitiveness of its products in order to increase the value of sea-based exports which in turn will improve the welfare of the community. SMEs engaged in the processing of seafood have not utilized science and technology in the production process so that found obstacles in the speed and fulfillment of export demand to neighboring countries. There are many work errors so shipping the product does not meet the standard of demand. The length of time the SMEs in fulfilling the buyer demand is caused by the length of the production process into the product ready to send. This work error results in the quality of the delivered product not meeting the health standards and the buyer's request. The ergonomic sorting table provides 2% reduction in work errors and the provision of wash tables designed to reduce work fatigue and the use of heating ovens to replace the sunlight function of the Cucumber products can increase the employee's working speed from 4 days to 2 days to prepare the product to send .

Envy-Free Pricing in Large Markets

We study the classic setting of envy-free pricing, in which a single seller chooses prices for its many items, with the goal of maximizing revenue once the items are allocated. Despite the large body of work addressing such settings, most versions of this problem have resisted good approximation factors for maximizing revenue; this is true even for the classic unit-demand case. In this article, we study envy-free pricing with unit-demand buyers, but unlike previous work we focus on large markets: ones in which the demand of each buyer is infinitesimally small compared to the size of the overall market. We assume that the buyer valuations for the items they desire have a nice (although reasonable) structure, that is, that the aggregate buyer demand has a monotone hazard rate and that the values of every buyer type come from the same support. For such large markets, our main contribution is a 1.88-approximation algorithm for maximizing revenue, showing that good pricing schemes can be computed when the number of buyers is large. We also give a ( e ,2)-bicriteria algorithm that simultaneously approximates both maximum revenue and welfare, thus showing that it is possible to obtain both good revenue and welfare at the same time. We further generalize our results by relaxing some of our assumptions and quantify the necessary tradeoffs between revenue and welfare in our setting. Our results are the first known approximations for large markets and crucially rely on new lower bounds, which we prove for the revenue-maximizing prices.

'Meet Me Halfway': The Value of Bargaining

Bargaining is an important pricing mechanism, prevalent in both online and offline markets. However, there is little empirical work documenting the costs and benefits of bargaining, primarily due to the lack of real-world bargaining data. We leverage rich, transaction-level bargaining data from a major online platform and supplement it with primary data to quantify the costs and benefits of bargaining for sellers, buyers, and the platform. We do this by building a structural model of buyer demand and seller pricing decisions while allowing for the existence of bargaining initiation cost, loss-of-face cost, and price discrimination. Using our results, we perform three policy simulations to quantify the importance of not distinguishing between no-bargain and failed-bargain transactions, ignoring the loss-of-face cost, and not allowing bargaining. These simulations provide rich details on how the various costs of bargaining impact our understanding of buyer and seller behavior and transaction outcomes. Banning bargaining, in particular, benefits the buyer and the platform greatly, but only has a modest benefit for sellers. Finally, we show that our results are robust to our assumptions and replicate in another product category.

Mechanisms to Induce Buyer Forecasting: Do Suppliers Always Benefit from Better Forecasting?

In this study, we consider a supplier's contract offerings to a buyer who may obtain improved forecasts for her demand over time. We investigate how the supplier can take advantage of the buyer's better forecasts and what kind of contracts he should offer to the buyer in order to maximize his profits. We model a natural forecast evolution where the buyer can obtain a more accurate forecast closer to the selling season. We assume there is information asymmetry between the buyer and the supplier at all times in that the buyer understands her demand better than the supplier. Three types of contracts that the supplier can offer are considered: (1) one where a contract is offered before the buyer has a chance to obtain improved forecasts, (2) one where a contract is offered after the buyer has obtained improved forecasts, and (3) a contingent (dynamic) contract which offers an initial contract to the buyer before she obtains improved forecasts, followed by a later contract (contingent on the initial contract) offered after improved forecasts have been obtained. We consider two scenarios: (1) where the supplier is certain that the buyer can obtain more accurate forecasts over time, and (2) where the supplier is uncertain about the buyer's forecasting capability (or forecasting cost). In the first scenario, we show that among the three types of contracts, the contingent contract is always the most profitable for the supplier. Furthermore, using the contingent contract, the supplier always benefits from higher accuracy of the buyer's demand forecasts. In the second scenario, we explicitly model the supplier's level of certainty about the buyer's capability of obtaining better forecasts, and explore how the supplier can design contracts to induce the buyer to obtain better forecasts when she is capable.

Perception Portfolio Problem in Property Investment Appraisal - Case of Energy Infrastructure Assets

Energy facility presence can be understood as an intangible property characteristic. However, little research has been conducted on its importance in the context of property asset pricing. Existing appraisal approaches for energy facility presence are often grounded on the intuition that house buyers demand a financial compensation for living near such a facility, implicit price. This study provides an overview of the existing empirical literature on the property value impact of nuclear, wind, biomass, and biogas energy facilities. Evidence suggests that general statements concerning the implicit price of energy facility presence may not hold true because this presence is a location-specific phenomenon.

English

This article is based on the buyer’s general behavior with the objective of identifying the consumption characteristics of minimally processed vegetables. A descriptive approach was used to understand how buyers make their decisions on the day-to-day, with a non-probabilistic sample of 328 questionnaires employed at the largest circulation areas of the seven administrative regions of Campo Grande (MS), Brazil. The data was analyzed using parametric and nonparametric statistical techniques in order to understand the perceptions and demands of buyers during the decision-making process of purchasing vegetables. The key results show strong evidence of the consumption of vegetables, especially from elder buyers (66.7% every day). The vegetables are selected using the perception of appearance and price, while buyers indicate that the product brand is less relevant. It stood out that the higher the consumption, the greater consumer awareness about the quality, certification and food safety requirements of vegetables. It was demonstrated that buyers demand a greater product quality and food safety, and are willing to pay more for it; however, they report that they do not read labels or product information. The managerial implications present empirical information relevant to vegetable sellers and the inclusion of marketing strategies.   Key words: Vegetable consumption, certification of vegetables, consumer behavior, origin of vegetables.

A robust ordering strategy for retailers facing a free shipping option.

Free shipping with conditions has become one of the most effective marketing tools available. An increasing number of companies, especially e-businesses, prefer to offer free shipping with some predetermined condition, such as a minimum purchase amount by the customer. However, in practice, the demands of buyers are uncertain; they are often affected by many factors, such as the weather and season. We begin by modeling the centralized ordering problem in which the supplier offers a free shipping service and retailers face stochastic demands. As these random data are considered, only partial information such as the known mean, support, and deviation is needed. The model is then analyzed via a robust optimization method, and the two types of equivalent sets of uncertainty constraints that are obtained provide good mathematical properties with consideration of the robustness of solutions. Subsequently, a numerical example is used to compare the results achieved from a robust optimization method and the linear decision rules. Additionally, the robustness of the optimal solution is discussed, as it is affected by the minimum quantity parameters. The increasing cost-threshold relationship is divided into three periods. In addition, the case study shows that the proposed method achieves better stability as well as computational complexity.