High-speed rail connectivity and supply chain information symmetry: insights from the bullwhip effect

Improved convolutional neural networks for the bullwhip effect in supply chains

Supply Chain Management relies on human consensus in decision-making to avoid emergent problems like the bullwhip effect. Some routine consensus processes, especially those that are time-intensive, can be automated. Previously proposed supply chain automation solutions for consensus-seeking and coordination faced computational challenges, resulting in high entry barriers. Recent advances in Generative AI, particularly Large Language Model agents (LLM agents), could overcome these barriers. This paper explores how LLM agents can automate consensus-seeking in supply chains. We introduce a series of novel, supply chain-specific consensus-seeking frameworks and validate the effectiveness of our approach through a case study in inventory management, where agents that represent companies in a supply chain are able to balance selfish goals with systemic outcomes through conversation. Our results show that introducing LLM-based consensus-seeking frameworks reduces bullwhip effects. When equipped with appropriate tools, LLM agents can minimise bullwhip better than restocking policies and centralised demand approaches. Additionally, when LLM agents are handled within a negotiation framework, their behaviour converges to best practices in the supply chain literature on how to lessen the bullwhip effect. To provide a foundation for further advancements in LLM-based autonomous supply chain solutions, we open-source our code.

ABSTRACT This study examines the impact of ESG rating divergence on the supply chain bullwhip effect using data from Chinese A-share firms between 2015 and 2022. The results show that greater ESG rating divergence significantly amplifies the bullwhip effect, reflecting greater volatility in supply chain operations. Mechanism analysis reveals two key channels: reduced stability in buyer–supplier relationships and increased volatility in production input decisions. These findings highlight the real-economy implications of inconsistent ESG information and underscore the importance of reliable and comparable ESG disclosure for maintaining supply chain stability.

Background. Production line balancing is a key element in improving the capacity and stability of manufacturing systems, yet conventional practices often ignore the elastic relationship between production intervals, capacity, and resource structure. This research develops and formalizes Rubber Ball Theory, a theoretical approach that views production systems as elastic entities, where changes in one operational variable trigger compensatory responses in other variables. Aims. The objective of this research is to develop a mathematical model for interval-based production line balancing and analyze its impact on overall supply chain performance. Methods. The research methodology includes developing an interval-based line-balancing optimization model, integrating concepts of bottlenecks and elastic capacity planning, and conducting empirical testing through a manufacturing industry case study. The developed model minimizes the system interval as the primary control variable, accounting for capacity constraints, precedence relationships, and parallel machine configurations. Sensitivity analysis is performed to evaluate the system's response to changes in the target interval and the number of parallel resources. Result. The results show that emphasizing production intervals without adjusting structural capacity leads to system instability and bottleneck displacement, while an elastic approach based on Rubber Ball Theory can sustainably increase production capacity. Furthermore, this approach has been shown to improve the reliability, responsiveness, and efficiency of asset management in the supply chain and contribute to reducing the variability of production flows that trigger the bullwhip effect. Conclusion. The main contribution of this research is the provision of an integrated conceptual and mathematical framework linking production line balancing to supply chain performance through elastic management of production intervals. Implication. These findings provide theoretical and practical implications for designing more adaptive and sustainable production systems and capacity planning.

ABSTRACT The semiconductor industry, foundational to modern technology, faces critical sustainability and risk challenges arising from resource‐intensive operations, concentrated production geographies, and recurrent disruptions. Fabrication plants consume extraordinary volumes of water and energy, while geopolitical tensions, material shortages, and demand volatility continue to destabilize global supply networks. These vulnerabilities underscore the need for integrated approaches to sustainable supply chain risk management (SSCRM). This study conducts the first integrative review at the intersection of sustainable supply chain management and supply chain risk management (SCRM) in the semiconductor context. Drawing on over 1500 studies screened from UTD24 (University of Texas at Dallas) and SCM (Supply Chain Management) journal lists (2010–2024), we identify 46 seminal works and analyze them through four frameworks: ESG (Environmental, Social, Governance), Triple Bottom Line (TBL), Triple A (Agility, Adaptability, Alignment), and Triple A&R (adding Robustness, Resilience, Realignment). Our synthesis highlights six interconnected research imperatives: AI‐enabled transparency and predictive analytics, power asymmetries in buyer–supplier relationships, geopolitical sustainability trade‐offs, collaborative governance models, the bullwhip effect's impact on resilience and sustainability, and the application of Triple A&R principles to semiconductor operations. By synthesizing insights across these themes, we propose targeted research questions that bridge theoretical constructs with semiconductor‐specific operational challenges. Our findings underscore the urgency of adopting integrated SSCRM approaches to fortify the industry's resilience, innovation, and long‐term sustainability against escalating global disruptions.

Purpose This study investigates how the bullwhip effect (BWE) in pharmaceutical supply chains influences addiction issues. This study aims to identify and rank the independent factors of the BWE that can positively and negatively impact addiction. Additionally, this study seeks to determine relevant corporate and supply chain constructs and propose practical solutions for each identified factor. Design/methodology/approach This research used the Delphi method to collect feedback from 36 pharmaceutical experts from various countries and roles within the supply chain. This study ensured reliable results over three survey phases by carefully considering the number of surveys and questionnaire accuracy. Responses were measured using a five-point Likert scale, and the significance and distribution patterns were statistically analyzed. Experts also provided insights on opportunities and challenges at both corporate and supply chain levels. Findings This study explored the relationship between addiction and BWEs in the pharmaceutical supply chain, identifying opportunities and challenges at corporate and supply chain levels. This study identified 24 independent constructs, 10 related to opportunities and 14 to challenges analyzing 498 expert responses. These findings provide valuable insights for tackling challenges and using opportunities in the pharmaceutical supply chain. Originality/value This research explores the intersection between the BWE in pharmaceutical supply chains and its impact on addiction issues. This study enhances understanding of these complexities by examining specific constructs of the BWE and their correlation with addiction. This paper also identifies effective solutions for each construct, offering valuable insights for policymakers, pharmaceutical industry stakeholders and addiction treatment practitioners. This study aims to fill a critical gap in the current literature and highlights the need for further investigation into the relationship between supply chain dynamics and addiction issues.

The Bullwhip Effect (BWE) occurs when orders made to suppliers have a bigger variation than sales to the buyer. This is a major concern that businesses are working on eliminating, as it has numerous side effects, such as excessive inventory, stock-outs, insufficient production, increased costs, etc. The new study adds to the literature by demonstrating how to quantify and assess the bullwhip impact on any supply chain. The results show that when consumer demand is unstable, the BWE is magnified. This was achieved by using the proposed formula which was based on an explanation and graph of the traditional BWE. A stochastic simulation based on a case study that replicates the behavior of a generic supply chain in a real-world market was used to evaluate the formula.

The supply chain inherently possesses a high degree of complexity, which has become increasingly exacerbated due to globalization, market expansion, and the continuous evolution of customer preferences. This growing complexity may lead to asset invisibility, inefficient inventory management, or logistical mismanagement. These complications often culminate in the well-known phenomenon of the "Bullwhip Effect" (BE) within supply chains. The aim of this study is to identify the key enablers that effectively reduce the Bullwhip Effect in the automotive supply chain sector. This research is descriptive in methodology and applied in purpose. To determine the importance of critical enablers influencing the mitigation of the Bullwhip Effect, a thorough review of the literature was first conducted to identify a preliminary list of significant enablers. Subsequently, using the Fuzzy Delphi Method, the final set of influential enablers for minimizing the Bullwhip Effect in the automotive supply chain was identified. To analyze the interrelationships among the 13 foundational enablers—based on literature and data collected through questionnaires—the study employed Fuzzy Cognitive Mapping (FCM) and Interpretive Structural Modeling (ISM) to determine the most impactful enablers. FCMapper software was used for the FCM method, while Excel software facilitated the ISM approach. Based on centrality metrics within the Fuzzy Cognitive Mapping approach, five enablers were found to be critically important: information quality in the supply chain, big data, supply chain flexibility, customer relationship management, and trust in the supply chain. Additionally, business intelligence, visibility capability, supply chain agility, order volume, information sharing capability, coordination and collaboration in the supply chain, supply chain integration and transparency, and delivery time were ranked sixth to thirteenth, respectively. According to the ISM results, the following enablers were identified in order of significance as the primary factors in reducing the Bullwhip Effect in the automotive supply chain: big data, business intelligence, information sharing capability, integration and transparency, trust in the supply chain, delivery time, coordination and collaboration, visibility capability, information quality, customer relationship management, order volume, supply chain agility, and flexibility.

The physical flow in distribution networks is highly susceptible to fluctuations caused by demand uncertainty. These fluctuations contribute to variability amplification across supply chain stages, known as the Bullwhip Effect. This paper investigates the conceptualization, modeling, and optimization of Demand-Driven Distribution Resource Planning (DDDRP) as a strategic management tool to mitigate this issue. We begin with a systematic literature review, covering (1) the causes, consequences, and solutions for the Bullwhip Effect and (2) conventional flow management methods, including lean distribution and the theory of constraints. Next, we present the theoretical foundation of the DDDRP model, outlining its core principles and stages. We then propose an optimization strategy of the first stage (i.e., Buffer positioning) through mathematical modeling to enhance system performance. This model can be applied by practitioners to improve decision-making. We validate its effectiveness through a real-world case study, demonstrating significant improvements in flow stability and supply chain performance. As an emerging flow management approach, DDDRP offers a robust alternative to traditional forecast-driven methods by aligning supply with actual market demand. Its growing adoption reflects its potential to enhance agility, reduce variability, and build more resilient distribution networks.

Moroccan producers of households’ detergents suffered from cash flow bullwhip. This distortion of financial flow originated from the bullwhip effect produced during the COVID 19 pandemic. A recent study confirms that some performance attributes are correlated with the degree of exposure to the CFB. The relative significance of these performance criteria, however, is not immediately apparent. The objective of this research is to develop a multi-criteria mathematical model which will serve as a basis to assess the performance of the companies under study and to define the determinants of Cash Flow Bullwhip control using the MACBETH method. This research is conducted on a sample of Moroccan producers of household detergents. The findings indicate that the importance of financial variables is higher than that of supply-chain elements, and internal control factors. Good supply chain asset management, financial efficiency, financial liquidity, control activities, financial debt, and supply chain credibility are the main levers to control the CFB.

Purpose This study investigates how top executive tournament incentives influence supply chain efficiency and how these effects depend on supply chain risks, specifically operational risk and disruption risk. Design/methodology/approach Our sample consists of 19,669 observations from 1994 to 2023, covering 1,560 unique US firms across the manufacturing, wholesale and retail sectors. Using two-stage least squares, we empirically examine whether top executive tournament incentives, proxied by pay dispersion, are related to supply chain performance, captured by inventory efficiency and the cash conversion cycle. We further test how this relationship is moderated by operational risk, captured through the bullwhip effect and disruption risk, captured through the onset of COVID-19. Findings Firms with greater executive pay dispersion achieve more efficient supply chain performance by reducing operational slack, resulting in higher inventory efficiency and shorter cash conversion cycles. However, this positive effect weakens under high operational risk, particularly among firms experiencing pronounced bullwhip effects. Furthermore, the disruption risk triggered by COVID-19 significantly diminishes the influence of executive pay dispersion on supply chain efficiency across all firms following the outbreak of the pandemic. Originality/value This paper provides the first evidence that top executive tournament incentives are associated with improved supply chain efficiency, but primarily in relatively stable environments where reducing operational slack translates into better performance. Under heightened operational and disruption risks, however, the need for buffer inventory, cross-functional coordination, and information sharing limits the effectiveness of tournament incentives. These findings integrate economic and operations perspectives to offer a richer understanding of how executive incentives shape supply chain management.

Effective inventory management in multi-echelon supply chains is challenged by stochastic demand and uncertain lead times, which amplify variability and increase operational costs. This study presents a Markov chain framework for modeling, analyzing, and optimizing multi-echelon inventory systems under stochastic lead-time conditions. The framework represents inventory levels and lead-time states as a probabilistic transition system, enabling computation of steady-state distributions, stockout probabilities, and transient recovery times. Analytical results are validated through Monte Carlo simulations, demonstrating high fidelity between theoretical and empirical distributions. Numerical experiments quantify the impact of lead-time variability on inventory performance, revealing nonlinear increases in stockout probability and total system cost as lead-time variance grows. Multi-echelon analyses demonstrate the emergence of the bullwhip effect and highlight the effectiveness of information sharing in mitigating variability propagation across echelons. Comparative benchmarking against deep reinforcement learning (DRL) policies shows that while DRL achieves marginally lower total costs, the Markov-based approach provides superior interpretability, robustness, and computational efficiency. The study offers theoretical contributions by unifying stochastic multi-echelon dynamics and transient analysis within a tractable Markov framework. Managerially, it provides actionable insights on lead-time variance reduction, cross-echelon visibility, and hybrid analytical–learning policy design. The framework establishes a foundation for resilient and cost-effective inventory control in complex, uncertain supply-chain networks.

The complexity of modern supply chain networks requires sophisticated approaches to inventory management that can effectively handle demand uncertainty and coordinate decisions across multiple organizational levels. This paper proposes a novel hierarchical multi-agent reinforcement learning framework for dynamic inventory allocation in multi-echelon supply chains facing stochastic demand patterns. The hierarchical architecture decomposes the inventory control problem into strategic and operational decision layers, where high-level agents coordinate allocation policies across distribution networks while low-level agents optimize local replenishment decisions. The framework integrates Centralized Training with Decentralized Execution paradigm, enabling autonomous agents to learn coordinated policies through shared experience while maintaining operational independence during deployment. Experimental results demonstrate that the proposed approach achieves significant reductions in total system costs compared to traditional base-stock policies and single-agent reinforcement learning methods, while effectively mitigating the bullwhip effect in supply chains with high demand variability.

This study examines delay-induced oscillations and stability loss in a continuous-time supply chain inventory system. The focus is on a single-echelon inventory model characterized by constant customer demand and an order-up-to policy, subject to a fixed replenishment lead time. The ordering rate is influenced by the deviation of on-hand inventory from the desired target level through linear and cubic feedback terms, resulting in a scalar delay differential equation with nonlinear delayed feedback. By analyzing deviations from the target inventory level, we derive the equilibrium point and assess its local stability by examining the associated characteristic equation. For positive feedback gain, we derive an explicit expression for the critical lead time at which a Hopf bifurcation occurs. Below this threshold, the equilibrium is locally asymptotically stable, and inventory deviation diminishes to zero; when the delay surpasses the critical value, a pair of complex conjugate eigenvalues crosses the imaginary axis, leading to persistent oscillatory adjustments in the system. The linear–cubic feedback structure underscores how stronger corrective reactions to inventory discrepancies, combined with significant lead times, can amplify rather than mitigate fluctuations. These findings elucidate a clear delay–margin relationship for a simple order-up-to policy and provide a dynamic explanation for structurally induced bullwhip behavior: even with constant demand, the interaction between feedback gains and lead time can generate sustained oscillations in inventory levels. Managerial implications regarding the joint selection of feedback parameters and permissible lead times are discussed, and extensions to more realistic multi-echelon settings and stochastic demand models are proposed. Keywords: Delay differential equations, inventory control, Hopf bifurcation, bullwhip effect, delay-induced oscillations.

Mitigating climate risk in supply chains: Empirical insights from the bullwhip effect in Chinese enterprises

The Bullwhip Effect (BWE) occurs when orders made to suppliers have a bigger variation than sales to the buyer. This is a major concern that businesses are working on eliminating, as it has numerous side effects, such as excessive inventory, stock-outs, insufficient production, increased costs, etc. The new study adds to the literature by demonstrating how to quantify and assess the bullwhip impact on any supply chain. The results show that when consumer demand is unstable, the BWE is magnified. This was achieved by using the proposed formula which was based on an explanation and graph of the traditional BWE. A stochastic simulation based on a case study that replicates the behavior of a generic supply chain in a real-world market was used to evaluate the formula.

Background: Globally, in the Fast-Moving Consumer Goods (FMCG) industry, excess inventory results from the bullwhip effect. Earlier, barcode-based two-bin systems were limited by manual scanning; hence, a more responsive system is needed to align the inventory with real-time demand. Prior studies have predominantly concentrated on mitigating demand fluctuations and employed comparatively low-efficiency systems, hindering excess inventory (EI) reduction. Methods: This study proposes identifying research gaps, considering the distributor-manufacturer relationship, and developing an RFID-based modified two-bin system and mathematical model to reduce EI and control over manufacturers’ excessive cost. Results: This study tested through Python-based simulation using historical data from an FMCG manufacturer, and the proposed model achieved a reduction in 67% EI and 73% month-wise holding costs. Moreover, the integration of the Artificial Bee Colony algorithm optimizes rework rates within budget, including reworking shop-floor and holding costs, contributing to a monthly excessive cost reduction of 34–48%, alongside a corresponding 41–44% cumulative excessive cost reduction. Conclusions: Bringing significant implications on digitalized SCM, this study offers a practical and scalable solution for perishable FMCG items facing demand variability and budget constraints. Collectively, this novel perspective bridges research gaps and motivates future research for embedding trend-aligned parameters, enhancing the model’s performance through diverse SCM contexts like safety stock and backorder cost optimization.

Purpose Demand forecasting is crucial for effective operations and supply chain management, particularly at the manufacturing stage. This study aims to explore the application of Organizational Information Processing Theory in AI-driven demand forecasting, examining how AI reshapes organizational processes and addressing the enablers and challenges of its implementation. Design/methodology/approach Through action research conducted in collaboration with an Italian manufacturing company, this study developed a deep learning-based demand forecasting system. Adopting abductive reasoning, it offers a theoretical examination of the system’s impact, focusing on the interplay between AI implementation and organizational dynamics. Findings This study reveals the organizational changes needed to implement AI in demand forecasting, focusing on iterative adjustments to align information processing amid evolving and uncertain data. Key factors such as data perception, nervousness, and data unreliability affect how information is trusted and used across departments and with suppliers. Building mutual trust and shared interpretive capabilities helps overcome collaboration barriers and reduces risks such as the bullwhip effect, highlighting the importance of negotiation within the organization alongside technology adoption. Originality/value We extend organizational information processing theory by introducing new constructs that capture AI’s implementation challenges, such as data perception and nervousness. Our study shows how AI-specific factors increase information processing demands, while organizational reshipment and trust enhance capacity. This refined framework offers a novel perspective on AI adoption, emphasizing both internal and supply chain information dynamics.

We study a supply chain distribution system and investigate experimentally operations of markets where retailers can trade digital claims (tokens) on the supplier’s capacity. Subjects play the role of retailers, have heterogeneous valuations of goods, face random demands, and buy tokens on the supplier’s capacity. Following demand realization, retailers trade tokens with each other in markets implemented as double-sided, single-price, blind, batch auctions. We compare six behavioral treatments, featuring two wholesale prices and three market sizes. As expected, markets reduce leftovers and shortages. Interestingly, market-clearing prices are anchored to wholesale prices and do not signal the value of goods in large markets. Players deploy novel ordering and trading strategies that differ from the transshipment literature. We identify strategies by applying unsupervised machine learning algorithms. In one strategy, players buy a few claims and, after demand realization, use the market to satisfy it. Other players buy more claims than the maximum demand and, once demand is known, sell their excess on the market. Both strategies reduce costs from demand uncertainty but expose players to liquidity and mistakes risks. A third strategy, in which players order from the supplier initially as if expecting the market to be cleared cooperatively, is more profitable. This strategy diversifies demand and market risks. The introduction of markets causes the “pull-to-the-mean” effect and increases order variability. Thus, markets can cause the Bullwhip Effect. Retailers’ and the supply chain’s average profits are higher with markets, but suppliers with low wholesale prices suffer from lower revenues because of the pull-to-the-mean effect. This paper was accepted by Elena Katok, operations management. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.03771 .