Search Results (39)

In today’s volatile supply chain environment, organizations require flexible and collaborative procurement strategies. Swap contracts, originally developed as financial instruments, have recently been adopted to address inventory imbalances—such as the 2021 COVID-19 vaccine swap between South Korea and Israel. Despite its increasing adoption in the real world, theoretical studies on swap-based procurement remain limited. This study proposes an integrated model that combines buyer-to-buyer swap agreements with long-term wholesale contracts under demand uncertainty. The model quantifies the expected swap quantity between parties and embeds it into the profit function to derive optimal order quantities. Numerical experiments are conducted to compare the performance of the proposed strategy with that of a baseline wholesale contract. Sensitivity analyses are performed on key parameters, including demand asymmetry and swap prices. The numerical analysis indicates that the swap-integrated procurement strategy consistently outperforms procurement based on long-term wholesale contracts. Moreover, the results reveal that under the swap-integrated strategy, the optimal order quantity must be adjusted—either increased or decreased—depending on the demand scale of the counterpart and the specified swap price, deviating from the optimal quantity under traditional long-term contracts. These findings highlight the potential of swap-integrated procurement strategies as practical coordination mechanisms across both private and public sectors, offering strategic value in contexts such as vaccine distribution, fresh produce, and other critical products. Full article

Rapidly decarbonizing the electricity grid is crucial for achieving net-zero greenhouse gas (GHG) emissions by mid-century and mitigating climate change impacts. Hydropower facilities can directly support grid decarbonization; however, like all energy systems, they emit GHGs throughout their lifecycle, with reservoirs being an important source. Further research is urgently needed to improve the accounting and mitigation of hydropower reservoir GHG emissions to ensure that this technology is accurately considered in decarbonization policies and to allow project owners and energy buyers to make credible emission claims regarding this energy source. To this end, this paper reviews over seven dozen studies and emerging research to synthesize the current state of the science on reservoir GHG emission pathways as well as advancements in emission measurement tools to identify areas where further research is needed. This paper presents a research roadmap for government agencies, research institutions, and funding organizations covering four action areas: understanding and reducing uncertainties in reservoir GHG estimation and associated publicly accessible estimation tools; reducing the technical and economic barriers for reservoir managers to use GHG estimation tools; setting common standards to enable consistent monitoring, allocation, and reporting of reservoir GHG emissions; and supporting work on reservoir GHG emission mitigation strategies, including watershed-scale strategies. Progress in these areas will enable robust accounting of hydropower reservoir GHG emissions and targeted mitigation efforts to advance grid decarbonization. Full article

I show that house prices can be modeled using machine learning (kNN and tree-bagging) and a small dataset composed of macroeconomic factors (MEF), including an inflation metric (CPI), US Treasury rates (10-yr), Gross Domestic Product (GDP), and portfolio size of central banks (ECB, FED). This set of parameters covers all the parties involved in a transaction (buyer, seller, and financing facility) while ignoring the intrinsic properties of each asset and encompassing local (inflation) and liquidity issues that may impede each transaction composing a market. The model here takes the point of view of a real estate trader who is interested in both the financing and the price of the transaction. Machine learning allows for the discrimination of two periods within the dataset. First, and up to 2015, I show that, although the US Treasury rates level is the most critical parameter to explain the change of house-price indices, other macroeconomic factors (e.g., consumer price indices) are essential to include in the modeling because they highlight the degree of openness of an economy and the contribution of the economic context to price changes. Second, and for the period from 2015 to today, I show that, to explain the most recent price evolution, it is necessary to include the datasets of the European Central Bank programs, which were designed to support the economy since the beginning of the 2010s. Indeed, unconventional policies of central banks may have allowed some institutional investors to arbitrage between real estate returns and other bond markets (sovereign and corporate). Finally, to assess the models’ relative performances, I performed various sensitivity tests, which tend to constrain the possibilities of each approach for each need. I also show that some models can predict the evolution of prices over the next 4 quarters with uncertainties that outperform existing index uncertainties. Full article

Increasing supply chain uncertainty due to market volatility has heightened the need for more flexible procurement strategies. While procurement through long-term forward contracts provides supply stability and cost predictability, it limits adaptability. Option contracts offer procurement flexibility, but require additional upfront premiums. Meanwhile, the spot market enables real-time purchasing without prior commitments, enhancing flexibility but exposing buyers to price volatility. Despite the growing adoption of portfolio procurement—combining forward contracts, option contracts, and spot market purchases—the existing research primarily examines these channels in isolation or in limited combinations, lacking an integrated perspective. This study addresses this gap by developing a comprehensive procurement model that simultaneously optimizes procurement decisions across all three channels under uncertain demand and fluctuating spot prices. Unlike prior studies, which often analyze one or two procurement channels separately, our model presents a novel, holistic framework that balances cost efficiency, risk mitigation, and adaptability. Our findings demonstrate that incorporating the spot market significantly enhances procurement flexibility and profitability, particularly in environments with high demand uncertainty and price volatility. Additionally, sensitivity analysis reveals how fluctuations in spot prices and demand uncertainty influence optimal procurement decisions. By introducing a new, practical approach to portfolio procurement, this study provides managerial insights that help businesses navigate complex and uncertain supply chain environments more effectively. However, this study assumes unlimited spot market capacity and reliable suppliers, highlighting a limitation that future research should address. Full article

According to the latest data from CryptoSlam, as of November 2024, NFT sales have approached USD 7.43 billion, with trading profits exceeding USD 33.303 million. In the buyer–seller market, the potential demand for NFT transactions continues to grow, leading to rapid development in the NFT market and giving rise to various issues, such as price manipulation, counterfeit products, hacking of investment platforms, identity verification errors, data leaks, and wallet security failures, all of which have caused significant financial losses for investors. Currently, the NFT investment market faces challenges such as legal uncertainty, information security, and high price volatility due to speculation. This study conducted expert interviews and adopted a two-stage research methodology to analyze the most common risk factors when selecting NFT investments. It employed the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and the Analytic Network Process (ANP) to explore risk factors such as legal issues, security concerns, speculation, and price volatility, aiming to understand how these factors influence investors in choosing the most suitable NFT investment platform. The survey was conducted between February and June 2023, targeting professionals and scholars with over 10 years of experience in the financial market or financial research, with a total of 13 participants. The empirical results revealed that speculation had the greatest impact compared to legal issues, security concerns, and NFT price volatility. Speculation and price volatility directly influenced other risk factors, potentially increasing the risks faced by NFT investment platforms. In contrast, legal and security issues had less influence on other factors and were more affected by them, indicating a relatively lower likelihood of occurrence. Thus, investors must be cautious of short-term speculation, particularly when dealing with rare NFTs. The best approach is to set an exit price to minimize potential losses if the investment does not proceed as planned. Full article

This paper examines the issue cost-effective procurement of a commodity product when its spot (open) market prices are stochastic, contract prices are previously determined, and there are costs associated with adjusting (i.e., switching) the procurement quantities from an alternative. Spot (open) market and contract as sole modes of procurement could present risks of high magnitude and uncertainty of expenses for the buyer. To address these risks, a risk-averse buyer may consider simultaneous use of both alternatives with adjustment of the purchase quantities from both the alternatives over time. Scenarios when the switching costs depend on the relative prices of the two alternatives are considered. The problem being analytically intractable, a mixed method decision model combining analytical and computational techniques to analyze the problem is proposed. The model helps identify expected optimal contract and spot market procurement quantities with respect to unknown spot prices and known contract prices over the planned procurement horizon when procurement quantity adjustment costs are influenced by the spends. The analysis reveals that it is cost-effective to continue purchasing with an existing pattern of procurement from the two alternatives until the contract to spot market price ratio reaches a threshold level and then to change the proportion of quantity purchased from the two alternatives. Using numerical analysis, we illustrate the theoretical and managerial significance of this stickiness to continue with an existing pattern until an adjustment. Full article

Throughout this article, an alternative depreciation method for electric vehicles (EVs) is presented, addressing the challenge of information asymmetry—a common issue in secondary markets. The proposed method is contrasted with traditional models, such as the Straight-Line Method (SLM), the Declining Balance Method, and the Sum-of-Years Digits (SYD) method, as these classic approaches fail to adequately consider key factors such as mileage and secondary aspects like battery degradation and rapid technological obsolescence, which critically impact the residual value of used EVs. The presented approach employs an adverse selection model that incorporates buyers’ and sellers’ perceptions of vehicle quality from the information recorded on e-commerce platforms, improving the depreciation estimation. The results show that the proposed method offers greater accuracy by leveraging asymmetric information extracted from web portals. Specifically, the method identifies a characteristic intersection point, marking the moment when the model aligns most closely with the data obtained through traditional methods in terms of precision. The analysis through the density of price estimations by vehicle model year indicates that, beyond 1.8 months, the proposed model provides more reliable results than traditional methods. The proposed model allows buyers to identify undervalued assets and sellers to obtain a fair market value, mitigating the risks associated with adverse selection, reducing uncertainty, and increasing market transparency and trust. It fosters equitable pricing between buyers and sellers by addressing the implications of adverse selection, where sellers—possessing more information about the vehicle’s condition than buyers—can dominate market transactions. This model restores balance by ensuring fairer valuation based on vehicle usage, primarily addressing the lack of critical data available on e-commerce platforms, such as battery certifications, among others. Full article

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. Full article

Our paper was inspired by the recent Society 5.0 initiative of the Japanese Government which seeks to create a sustainable human-centric society by putting to work recent advances in technology. One of the key challenges in implementing Society 5.0 is providing trusted and secure services for everyone to use. Motivated by this challenge, this paper makes three contributions that we summarize as follows: Our first main contribution is to propose a novel blockchain and smart contract-based trust and reputation service design to reduce the uncertainty associated with buyer feedback in marketplaces that we expect to see in Society 5.0. Our second contribution is to extend Laplace’s Law of Succession in a way that provides a trust measure in a seller’s future performance in terms of their past reputation scores. Our third main contribution is to illustrate three applications of the proposed trust and reputation service. Here, we begin by discussing an application to a multi-segment marketplace, where a malicious seller may establish a stellar reputation by selling cheap items, only to use their excellent reputation score to defraud buyers in a different market segment. Next, we demonstrate how our trust and reputation service works in the context of sellers with time-varying performance due, say, to overcoming an initial learning curve. We provide a discounting scheme where older reputation scores are given less weight than more recent ones. Finally, we show how to predict trust and reputation far in the future, based on incomplete information. Extensive simulations have confirmed the accuracy of our analytical predictions. Full article

The rapid growth of the sharing economy has propelled shared mobility to the forefront of the public’s attention. Continuous advancements in autonomous driving technology also bring new opportunities and challenges to the shared mobility industry. This study comprehensively analyzes the impact of using land-based autonomous vehicles (AVs) to provide shared mobility services, utilizing SWOT analysis (Strengths, Weaknesses, Opportunities, and Threats), PESTLE analysis (Political, Economic, Social, Technological, Legal, and Environmental), and Porter’s Five Forces (the bargaining power of suppliers, the bargaining power of buyers, threats of new entrants, substitutes, and rivalry). The findings reveal that AVs can provide improved shared mobility services by increasing transportation safety, reducing emissions, reducing costs, enhancing traffic efficiency, and increasing customer satisfaction as well as the profitability of shared mobility services. However, challenges such as technological and policy uncertainties, safety concerns, high initial costs, inadequate public communication infrastructure, and the absence of standardized regulations can hinder the widespread adoption of AVs. The benefits are also restricted by the low market penetration rate of AVs. To promote AVs in the shared mobility market, this study also provides implications for AV stakeholders tailored to the evolving shared mobility market dynamics. Full article

We report estimates of supply and demand elasticities for houses (i.e., non-strata properties) in three geographic locations of Sydney. In the Inner Ring of Local Government Areas (LGAs)—those closest to the Central Business District (CBD)—our estimates indicate that the supply curve for houses is perfectly inelastic. This finding allows us to condition on the stock of houses and estimate the corresponding Inner Ring demand curve using ordinary least squares. In the Middle and Outer Rings—where the supply curve for houses has positive elasticity—we use instrumental variables to estimate the demand curve for houses. For all three locations, we obtain theoretically reasonable point estimates of standard demand elasticities, although the degree of uncertainty surrounding the Outer Ring estimates is relatively large. Averaging across the three regions of Sydney, the price elasticity of demand for houses is −1.3, cross-price elasticity with units is 1.1, and income elasticity is 2.1. Based on our elasticity estimates, only in the Outer Ring are any of the direct burdens of stamp duty born by buyers (about 40 percent). Full article

Despite having a strong production capacity for fresh coconut, Indonesia has a certain deficiency in coconut processing, particularly coconut oil products, which is reflected in the declining market demand rates. The skyrocketing price of palm kernel oil (PKO) had been observed to impact the shifting demand for coconut oil. The cross-price competition with PKO was estimated to uncover the potential market demand for Indonesian coconut oils, especially during the periods of price discrepancies between 2020 and 2022. Thus, our study aimed to analyze the Indonesian coconut oil and palm kernel oil (PKO) market relationship as the markets reacted during the period of price volatility. This study is essential for Indonesian market evaluation, as both commodities are considered to be perfect substitute goods and are similar substances that contain high levels of lauric acid called “lauric oils”. We deployed an ARDL analysis utilizing secondary data from 1964 to 2022, focusing on the cross-price elasticity between coconut oil and PKO prices with the addition of prominent concerned variables. In the long-term estimations, the observational results indicated that the coconut oil and PKO prices had distinctive impacts on Indonesian coconut oil exports of −1.85% and 1.88%, respectively. In the short-term estimations, we found inverse values: the coconut oil price had positive impacts in the short-term period of 1.29% (D1.) and 2.35% (LD.), while the PKO had a negative impact on Indonesian coconut oil exports of −2.17%. This indicated that a PKO price reduction would increase the demand for Indonesian coconut oil exports due to the PKO price volatility and uncertainty perceived by the buyers in the short term. Our study also observed that rival producers, such as the Philippines, had a negative impact (−1.80%), representing the intense competition between Indonesia and the Philippines. Therefore, the Indonesian government has to elevate its coconut oil competitiveness to acquire the potential to expand the market and compete with other major coconut-producing countries. Full article

Biogas can be regarded as a dispatchable renewable source when changing into the demand-oriented operation mode (DO), thus could be used for complementing with solar and wind power in distributed energy system (DES) as a substitute for chemical energy storage. However, if the DO is implemented in regional DES, uncertainties are emerged caused by the complex interest interaction between the seller and the buyer groups formed by the biogas plant and the DES’s dispatching center, thus making the development trend of DO unknown. In this context, this study explored the diffusion law of DO in regional DES by establishing a mathematical model based on an evolutionary game between the two major stakeholders, during which the evolutionarily stable strategy (ESS) was deduced for understanding their strategy selections, and then the dynamic diffusion trend was simulated by the system dynamics via a case example. Finally, the sensitivity analysis of parameters is carried out and the optimal policy instruments are proposed according to the main influencing factors. The study revealed that when the DES can realize monetized returns from socio-environmental benefits, the adoption of DO becomes more feasible. Importantly, the revenue generated from electricity sales, by the dispatching center when they do not utilize biogas, emerged as the most critical parameter influencing the ultimate outcomes. The limitations of this research and modeling are discussed to lay a foundation for further improvement. Full article

With changes in demand and the emergence of new distribution channels, consumer-centric buyer’s markets for many products have been formed. The platform supply chain has been continuously optimized and upgraded. Supply chain leaders have moved downstream to the end of the supply chain. The operational value has been further enhanced. The corresponding systematic construction of the platform supply chain has become an important driving force for future development. The model in this paper is different from the traditional supply chain contract model, which mainly focuses on suppliers or demand. In order to meet the requirements of fresh-keeping services and the goal of revenue sharing, we integrate the production and circulation characteristics of fresh produce into the design of a contract model. In this paper, a revenue-sharing contract model of the fresh produce supply chain is constructed based on the core position of retailers, the uncertainty of the market size, and the consideration of a fresh-keeping service. The model is mainly composed of the core retailer and the supplier. Through further numerical analysis, we verify the effectiveness of the revenue-sharing contract model in supply chain coordination. We also analyze the change trends in the optimal retail price, optimal freshness level, and optimal order quantity caused by changes in both the fresh-keeping service capacity and the revenue-sharing coefficient. The results show that after changing these two parameters, the supply chain can achieve coordination under the specified parameter values. The changed parameters will also lead to certain change trends in the optimal retail price, optimal freshness level, and optimal order quantity, and will have a corresponding impact on the stability of supply chain operation. This research provides a relevant theoretical and empirical basis for a fresh produce supply chain contract model with retailers at the core position. We also provide guidance and reference for optimizing the supply chain management mode and improving the overall operational efficiency of the fresh produce supply chain. Full article

Fundamentally, newborn items that are used commercially, such as chicken, fish, and small camel, grow day by day in size and also increase their weight. The seller offers a credit policy to the buyer to increase sales for a particular growing item (fish), and in this paper, it is assumed that the buyer accepts the policy of the trade credit. In this paper, the buyer acquires the newborn items (fish) from the seller and then sells them when the newborn items have increased their size and weight. From this point of view, the present paper reveals a fuzzy-based supply chain model that includes carbon emissions and a permissible delay in payment for defective growing items (fish) under the effect of learning where the demand rate is imprecise in nature and is treated as a triangular fuzzy number. Finally, the buyer’s total profit is optimized with respect to the number of newborn items. A numerical example has been presented for the justification of the model. The findings clearly suggest that the presence of trade credit, learning, and a fuzzy environment have an affirmative effect on the ordering policy. The buyer should order more to avoid higher interest charges after the grace period, which eventually increases their profit, while at the same time, it is also beneficial for the buyer to order less to gain the benefit of the trade credit period. The fuzziness theory controls the uncertainty situation of inventory parameters with the help of a de-fuzzified method. The lower and upper deviation of demand affects the total fuzzy profit. The effect of learning gives a positive response concerning the size of the order and the buyer’s total fuzzy profit. This means that the decision-maker should be aware of the size of the newborn items, rate of learning, and trade credit period during the supply chain because these directly affect the buyer’s total fuzzy profit. The impact of the inventory parameter of this model is presented with the help of sensitivity analysis. Full article