Search Results (361)

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

Our regression analysis documents that energy policies to promote renewable energy development, as well as hydroelectric imports from Canada, lead to short-run reductions in average electricity prices (also known as merit-order effects) throughout the Northeast United States. Changes in the reliance upon renewable energy in one of the Northeast’s three interconnected electricity markets will impact wholesale prices in the other two. The retirement of a 1000 MW nuclear plant can increase prices by about 9% in the Independent System Operator of New England market and 7% in the New York Independent System Operator market in the short run at reference hubs, while also raising prices in neighboring markets. Some proposed large-scale off-shore wind farms would not only lower prices in local markets at the reference hubs modeled but would also lower prices in neighboring markets. Full article

Green and low-carbon development of supply chains represents a practical approach to addressing climate change and enhancing corporate competitiveness. From the perspective of the relationship between policy subsidies and channel power structures, this paper constructs Stackelberg game models under four different scenarios to conduct theoretical analyses of the optimal strategies, supported by numerical simulations. The research findings reveal the following. (1) Under the product subsidy policy, the enhancement of consumers’ green preference will lead to a green premium, and in the case of the technology subsidy policy, consumers’ green preference will inhibit wholesale prices and retail prices. However, there is a threshold in the manufacturer-led case, and a “green premium” is also claimed when this threshold is exceeded. (2) The effects of the product subsidy policy and the green technology level subsidy policy on prices are opposite, where an increase in the product subsidy will increase the wholesale price and retail price, while an increase in the green technology level subsidy will reduce the wholesale price. The technology subsidy policy has a more significant effect on the promotion of green technology. (3) The power of supply chain channels will directly affect corporate profits, and the leader of the supply chain often has higher profits. Compared with product subsidies, technology subsidies can inhibit the channel power of retailers. Full article

This study examines how contract structures influence coordination and innovation incentives in dual-channel telecommunications supply chains. We consider a setting where a mobile network operator (MNO) supplies services both directly to consumers and indirectly through a mobile virtual network operator (MVNO), which competes in the retail market. Using a game-theoretic framework, we evaluate how different contracts—single wholesale pricing, revenue sharing, and quantity discounts—shape strategic decisions, particularly in the presence of investment spillovers between parties. A key coordination problem emerges from the externalized gains of innovation, where one party’s investment generates value for both participants. Our results show that single wholesale and revenue sharing contracts often lead to suboptimal investment and profit outcomes. In contrast, quantity discount contracts, especially when combined with appropriate transfer payments, improve coordination and enhance the total performance of the supply chain. We also find that innovation led by the MVNO, while generally less impactful, can still yield reciprocal benefits for the MNO, reinforcing the value of cooperative arrangements. These findings emphasize the importance of contract design in managing interdependence and improving efficiency in decentralized supply chains. This study offers theoretical and practical implications for telecommunications providers and policymakers aiming to promote innovation and mutually beneficial outcomes through well-aligned contractual mechanisms. Full article

This paper studies a supply chain comprising a supplier, a third-party remanufacturer (TPR), and a retailer. The retailer sells both genuine and remanufactured products (i.e., Model O). Leveraging information advantages, the retailer may engage in brand deception by mislabeling remanufactured products as genuine to obtain extra profits (i.e., Model BD). AI-powered anti-counterfeiting technologies (AIT) (i.e., Model BA) and revenue-sharing contracts (i.e., Model C) are considered countermeasures. The findings reveal that (1) brand deception reduces (increases) sales of genuine (remanufactured) products, prompting the supplier (TPR) to lower (raise) wholesale prices. The asymmetric profit erosion effect highlights the gradual erosion of profits for the supplier, retailer, and TPR under brand deception. (2) The bi-interval adaptation effect indicates that AIT is particularly effective in industries with low adaptation resistance. When both the relabeling rate and industry adaptation resistance are low (high), Model BA (Model O) achieves a triple win. (3) Sequentially, when the industry adaptation resistance is low, AIT can significantly improve total profits, consumer surplus (CS), and social welfare (SW). Compared to Model BD, revenue-sharing offers slight advantages in CS but notable disadvantages in SW. Full article

This paper examines arbitrage opportunities available in one of the largest wholesale electricity markets in the world, the Midcontinent Independent System Operator (MISO) electricity exchange. While prior research suggests that market efficiency on the exchange has increased over time, this study reveals that historical pricing information can still be used to generate positive returns. I find that a trading rule based on prior spot and forward prices generates statistically and economically significant risk-adjusted returns across the entire MISO footprint. These returns may in part be explained by the relatively small number of financial traders in the market and the ability of generation owners to exercise market power. Full article

The Greek islands have been blessed with excellent wind potential, with hundreds of sites featuring annual average wind velocity higher than 8–10 m/s. Due to specific regulations in the legal framework, some GWs of wind parks have been submitted since the late 2000s by a small number of large investors in the Greek islands, favoring the creation of energy monopolies and imposing serious impacts on natural ecosystems and existing human activities. These projects have caused serious public reactions against renewables, considerably decelerating the energy transition. This article aims to summarize the legal points in the Greek framework that caused this distorted approach and present the imposed potential social and environmental impacts. Energy monopolies distort the electricity wholesale market and lead to energy poverty and a low standard of living by imposing higher electricity procurement prices on the final users. The occupation of entire insular geographical territories by large wind park projects causes important deterioration of the natural environment, which, in turn, leads to loss of local occupations, urbanization, and migration by affecting negatively the countryside life. Serious concerns from the local population are clearly revealed through an accomplished statistical survey as well as a clear intention to be engaged in future wind park projects initiated by local stakeholders. The article is integrated with specific proposed measures and actions toward the rational development of renewable energy projects. These refer mainly on the formulation of a truly supportive and just legal framework aiming at remedying the currently formulated situation and the strengthening of the energy communities’ role, such as through licensing priorities, funding mechanisms, and tools, as well as additional initiatives such as capacity-building activities, pilot projects, and extensive activation of local citizens. Energy communities and local stakeholders should be involved in the overall process, from the planning to the construction and operation phase. Full article

The purpose of this research is to investigate the possibilities of electricity consumption-associated cost reduction in buildings owned by a medium-sized logistics company in Latvia (A_LV), which is a part of the larger international business ecosystem (A). The company is not using all of its facilities for its own business needs, some of them are rented out, and therefore the possibility of impacting electricity consumption in rented out buildings is limited. During the research, mixed-type approaches combining qualitative and quantitative research methods and data analysis were employed, where the quantitative methods helped to analyze the company’s electricity consumption and cost changes in different time periods, while the qualitative methods were used in a literature review. As primary data sources, A_LV’s internal electricity consumption reports and invoices for electricity payments were used, along with publicly available data on electricity consumption in Latvia and wholesale market price fluctuations. Although A_LV has numerous areas of electricity consumption optimization, this research is limited to few of them—lighting system optimization, energy management and automation applications, forklift charging regime adjustments, and choice of electricity retailer and tariff plan. Full article

Accurate electricity wholesale price (EWP) forecasting is crucial for advancing sustainability in the energy sector, as it supports more efficient utilization and integration of renewable energy by informing when and how it should be consumed, dispatched, curtailed, or stored. However, high fluctuations in EWP, often resulting from demand–supply imbalances typically caused by sudden surges in electricity usage and the intermittency of renewable energy generation, and unforeseen external events, pose a challenge for accurate forecasting. Incorporating local temporal information (LTI) in time series, such as hourly price changes, is essential for accurate EWP forecasting, as it helps detect rapid market shifts. However, existing methods remain limited in capturing LTI, either relying on point-wise input sequences or, for fixed-length, non-overlapping segmentation methods, failing to effectively model dependencies within and across segments. This paper proposes the Local-Temporal Convolutional Transformer (LT-Conformer) model for day-ahead EWP forecasting, which addresses the challenge of capturing fine-grained LTI using Local-Temporal 1D Convolution and incorporates two attention modules to capture global temporal dependencies (e.g., daily price trends) and cross-feature dependencies (e.g., solar output influencing price). An initial evaluation in the Australian market demonstrates that LT-Conformer outperforms existing state-of-the-art methods and exhibits adaptability in forecasting EWP under volatile market conditions. Full article

The objective of this study is to analyze the current situation of out-of-home catering (OHC) in Germany concerning the use of regional organic food using a case study; we also aim to determine the potential and challenges that exist in increasing the proportion of regional organic food in OHC. The food purchasing data from three canteens of the company were analyzed concerning regionality, seasonality, and organic share. The companies’ employees were asked about their willingness to pay and their attitude towards regional organic food using an online questionnaire. A price comparison between organically and conventionally grown food was carried out with food wholesalers’ product price lists. The study confirms the potential to increase the share of regional organic food in OHC. With their private purchasing behavior, eating habits and willingness to pay a surcharge for organic quality in the company restaurants, the consumers confirm that they support an increase in the regional organic share. Regional organic food could be purchased from (organic) wholesalers. However, the study also shows that the cost of sourcing organic food is on average 50% higher than that of conventional food and that this price markup is the main reason for consumers not buying organic food. Full article

The rapid development of the new energy vehicle (NEV) industry has led to concerns about battery quality and the transparency of green recycling, causing uncertainty among consumers. Many firms adopt blockchain technology to solve this problem, but blockchain adoption will bring privacy leakage risk to consumers. A Stackelberg game model of a three stage NEV supply chain is constructed to examine the impact of adapting blockchain on strategic decisions of supply chain participants. We consider a setting in which a battery supplier provides batteries to a NEV manufacturer, and a third-party recycler recovers retired batteries for a NEV manufacturer. We explore the influence of consumers’ green recycling preferences on the decisions of NEV supply chain members in three scenarios: not adopting blockchain traceability (NB), adopting blockchain with forward traceability (FB), and adopting blockchain with forward–reverse traceability (DB). We find that NEV supply chain members are more likely to adopt forward–reverse traceability under certain conditions. Moreover, the adoption of blockchain drives the battery supplier and NEV manufacture to increase wholesale price and retail price, especially under forward–reverse traceability. In addition, when consumers exhibit strong preferences for green recycling, third-party recyclers are more willing to invest in blockchain-based recycling due to its ability to enhance the accuracy and credibility of recycling data. Full article

Systemic therapy (ST) and transarterial radioembolization (TARE) are widely used treatments for advanced-stage hepatocellular carcinoma (HCC). This study quantified the significant variability in treatment costs for unresectable HCC from payer and provider perspectives. An Excel-based price analysis model was developed to estimate the prices of ST and TARE over a 21-month time horizon using 2015–2021 data. Median prices were calculated from Medicare Average Sales Price (ASP), provider Wholesale Acquisition Cost (WAC), and Average Wholesale Price (AWP). Sensitivity analyses evaluated price fluctuations associated with a ±10% variation in treatment duration. ST prices demonstrated marked variability across perspectives, with the median ASP at $175,625, WAC at $198,719, and AWP at $262,892. However, TARE prices were stable, ranging from $21,594 to $24,052. Sensitivity analyses revealed that treatment duration variation resulted in price changes of $35,000–$50,000 for ST, compared with ~$5000 for TARE. The variability in ST pricing was driven by treatment duration and drug-specific pricing mechanisms, particularly immunotherapy-based regimens, which accounted for the higher cost range. Conversely, TARE’s consistent pricing is attributed to standardized procedural costs. Substantial variability exists in ST prices compared with the consistent costs of TARE, underscoring the economic advantage of TARE in appropriate clinical contexts. Full article

Wholesale market prices of agricultural products, being essential to the daily lives of consumers, are closely tied to living standards and the overall stability of the agricultural market. The use of a single model to predict nonlinear and dynamic agricultural price time series often results in low accuracy due to suboptimal use of available information. To address this issue, this paper proposes a combined residual correction-based prediction method. Initially, the sparrow search algorithm (SSA) is used to optimize the penalty factors and kernel parameters of support vector regression (SVR) and the input weights and hidden layer biases of the extreme learning machine (ELM), thereby improving the convergence rate and predictive accuracy of these models. Subsequently, the induced ordered weighted averaging (IOWA) operator is applied to determine the weight vectors for the SSA-SVR and SSA-ELM models, reducing the fluctuating prediction accuracies of individual models at different times. Finally, the residuals of the generalized regression neural network (GRNN) model are forecasted using a combined residual correction method that integrates SSA-SVR and SSA-ELM based on the IOWA operator, refining the GRNN’s forecast outcomes. An empirical analysis was performed by comparing the results of nine individual forecasting models on monthly pork prices in Beijing. The findings indicate that the SSA-SVR, SSA-GRNN, and SSA-ELM models outperformed the SVR, GRNN, and ELM models in terms of forecasting accuracy, respectively. This improvement is attributed to the parameter optimization of the SVR, GRNN, and ELM models through the SSA. The proposed model also showed superior forecasting accuracy compared to the nine individual models. The results confirm that the proposed model is an effective tool for predicting agricultural product prices and can be applied to forecast prices of other agricultural products with similar characteristics. Full article

The huge penetration of renewable energy sources poses several challenges for the function of electricity markets, such as increased price volatility and massive curtailments. This paper investigates the current structure of the wholesale electricity market in Greece under the Target Model guidelines. Our analysis put under scrutiny the formation and function of both spot and balancing markets by highlighting key challenges and reforms. Empirical evidence reveals that the domestic market is currently in accordance with the European Target Model; however, the anticipated benefits in terms of more competitive prices are not evident yet. The oversupply of electricity accompanied by low demand that is apparent in the Greek market points to the rapid participation of storage units in the system. The paper provides a detailed description of the recent support mechanism to facilitate the integration of BESS into the system. Eventually, this is anticipated to reduce price volatility and smoothen the price curves. Full article

The development of renewable energy in the power industry plays a crucial role in mitigating environmental degradation. The renewable energy (RE) consumption system and green certificate trading market are significant in promoting renewable energy adoption, while energy storage technology has advanced substantially to address power supply instability. Against this backdrop, this study employs a Stackelberg game approach to construct a power supply chain model, with generation companies as leaders and retail companies as followers, examining energy storage cost-sharing mechanisms and retailers’ renewable energy investment decisions. Key findings include the following: (1) a higher RE consumption ratio reduces wholesale prices, power stability, electricity demand, and retailers’ renewable investment; (2) when the energy storage cost coefficient exceeds a threshold, higher green certificate prices increase retailers’ renewable investment; (3) beyond the threshold, a higher RE consumption ratio incentivizes retailers to invest in renewables; (4) proportional cost sharing enhances renewable investment by approximately 15% and maximizes supply chain profits. The study provides decision-making insights for power companies and policy references for governments. Full article