Supply Chain Management Dissertation Topics

Today we see global supply chains reshaped by shortages, AI adoption, and new sustainability rules—an ideal moment to choose a dissertation topic that truly matters. We at TopicSuggestions work with students on tight timelines, and we know a strong topic blends relevance, available data, and a method you can actually run. We will share a concise, original list of Supply Chain Management dissertation topics grounded in current research and practice.

Good Dissertation Topic Ideas on Supply Chain Management

We will organize them by themes—resilience and risk, digital and analytics, sustainability and ESG, procurement and supplier relationships, logistics and network design, and sector-specific angles like healthcare or retail—so you can scan and match quickly. We will keep it practical and student-friendly, so you can pick a topic, scope it well, and start writing with confidence.

1. We engineer “Algorithmic Compassion” in HR: allocating support resources with fairness-aware AI

– We ask whether algorithmically prioritizing Employee Assistance Program access, mental health days, and flexible shifts improves well-being and performance relative to manager-only discretion.
– We test how we can encode distributive and procedural justice into allocation models without amplifying existing inequities.
– We measure how we calibrate transparency levels to sustain trust without enabling gaming or stigma.
– We examine how we detect and mitigate empathy gaps when algorithms mediate care-related decisions.

2. We integrate carbon budgets into performance management: Carbon-Conscious HR contracts

– We evaluate how we embed personal and team carbon targets (travel, facilities, data usage) into goal-setting without penalizing role constraints.
– We estimate how we trade off carbon reductions versus output metrics and how we design incentives that avoid greenwashing.
– We test how we disclose carbon-linked rewards to maintain perceptions of fairness across job families.
– We analyze how we model rebound effects when remote work shifts emissions elsewhere.

3. We create micro-discretionary time marketplaces: trading 15-minute time credits across teams

– We ask how we price micro-time credits to balance urgency, equity, and project interdependencies.
– We test whether we can reduce burnout and increase flow states by letting employees swap micro-time across time zones.
– We examine how we prevent exclusionary patterns (e.g., senior staff capturing prime time) and ensure accessibility for caregivers.
– We measure how we govern strategic hoarding or arbitrage of time credits.

4. We build “Shadow Skill Graphs” from workflow exhaust to power internal mobility

– We investigate how we infer latent skills from code commits, documents, tickets, and meeting transcripts while preserving privacy.
– We test whether we can improve mobility speed and diversity without entrenching past opportunity gaps.
– We evaluate how we communicate probabilistic skill inferences to managers and employees to avoid labeling harm.
– We model how we update and validate skill edges over time to reflect learning and decay.

5. We prototype consent-weighted people analytics that adapt to employee privacy preferences

– We ask how we design analytics that weight opt-in data to minimize bias and maintain statistical validity.
– We test how we structure incentives for data sharing that are ethical and non-coercive.
– We measure how we explain model performance and uncertainty under varying consent levels to stakeholders.
– We examine how we negotiate organizational risk when critical subgroups opt out.

6. We orchestrate “Crisis-Swarm Staffing” for rolling disruptions using swarm intelligence

– We evaluate how we match micro-assignments to volunteers in minutes while respecting role criticality and fatigue limits.
– We test how we embed fairness, safety, and skill progression into swarm allocation rules under stress.
– We measure how we predict volunteer burnout and design recovery buffers without degrading responsiveness.
– We analyze how we institutionalize lessons learned into standard operating procedures post-crisis.

7. We treat the right-to-disconnect as a stochastic intervention: just-in-time nudges and hazard of burnout

– We model how we randomize timing and intensity of disconnect nudges to estimate causal effects on burnout hazard rates.
– We test how we tailor nudges by chronotype, role, and caregiving status without inducing backlash.
– We measure how we balance after-hours responsiveness needs with well-being across global teams.
– We examine how we sustain adherence once novelty effects fade.

8. We design neurodiversity-first job crafting in mixed-reality (XR) offices

– We test how we let employees co-create sensory, spatial, and interaction parameters in XR to improve outcomes.
– We evaluate how we train managers to interpret XR interaction cues without misattribution bias.
– We measure how we integrate XR ergonomics with accommodations policies and legal frameworks.
– We analyze how we scale bespoke XR setups while safeguarding data from biometric inference.

9. We run pay experimentation sandboxes with employee veto power

– We ask how we co-design A/B tests of pay transparency, skill-based pay, or bonuses where employees can veto inclusion.
– We test how we maintain internal equity and external competitiveness amid experimental variation.
– We measure how we communicate uncertainty and reversibility of pay pilots to preserve trust.
– We examine how we use experimental results to inform durable pay architecture without path dependence.

10. We evaluate AI-mediated mentorship triads: mentor–mentee–model collaboration

– We test how we structure triads where an AI coach augments mentor guidance with personalized micro-challenges.
– We measure how we prevent dependency on AI feedback while preserving human relationship quality.
– We evaluate how we allocate triads to reduce sponsorship gaps for underrepresented employees.
– We analyze how we attribute developmental outcomes among human and AI contributors in performance reviews.

11. Designing a Federated Learning–Blockchain Framework for Trustworthy Micro‑Supplier Selection

We propose to investigate how federated learning combined with blockchain can create privacy‑preserving, tamper‑evident supplier trust scores for micro‑suppliers.
Research questions:
– How can we design a federated learning protocol that aggregates performance metrics from multiple buyers without exposing raw transactional data?
– How does anchoring federated model updates on a permissioned blockchain affect auditability, latency, and incentive alignment?
– What trust score formulations best predict future reliability for micro‑suppliers in volatile markets?
We will prototype the federated learning pipeline and a lightweight smart contract ledger, run simulation experiments on synthetic and partner firm data, and validate predictive performance and stakeholder acceptance via workshops and pilot deployments.

12. Perishability‑Aware Dynamic Routing with Real‑Time Demand Elasticity Feedback

We aim to create routing algorithms that adapt delivery priorities based on real‑time consumer demand elasticity and product perishability.
Research questions:
– How does integrating demand elasticity signals (e.g., price sensitivity, time‑of‑day demand) change routing priorities for perishable goods?
– What trade‑offs emerge between minimizing spoilage, delivery cost, and lost sales under different elasticity regimes?
– How robust are elasticity‑aware routing policies to noisy or delayed demand information?
We will build a simulation environment, implement reinforcement learning and stochastic optimization policies, calibrate models using grocery retailer telemetry, and run sensitivity analyses and field A/B tests where feasible.

13. Circular Economy Contracts: Shared Liability Mechanisms Between OEMs and Remanufacturers

We investigate contractual designs that share liability, information, and revenue between original equipment manufacturers (OEMs) and remanufacturers to scale product circularity.
Research questions:
– Which contract structures (revenue‑sharing, buyback, performance bonds) best incentivize quality returns and efficient remanufacturing?
– How does asymmetric information about return quality affect optimal contracting and secondary market prices?
– What role do third‑party insurers or blockchain‑verified provenance records play in reducing transaction costs?
We will formulate game‑theoretic models, solve for equilibria under asymmetric information, and validate with case studies and interviews with OEMs, remanufacturers, and recyclers.

14. Human‑AI Teaming for Exception Handling in Autonomous Warehouses

We explore how human operators and AI controllers can jointly manage exceptions (e.g., mispicks, jammed conveyors) in highly automated warehouses.
Research questions:
– What interface designs and information cues maximize rapid, accurate human intervention while minimizing cognitive load?
– How should responsibility and control switch between AI and humans to optimize throughput and safety?
– What training regimes improve human trust and calibration when interacting with adaptive AI controllers?
We will conduct controlled lab studies with warehouse workers using AR/HMI prototypes, develop human‑in‑the‑loop evaluation metrics, and deploy pilot tests in operational warehouses.

15. Multi‑Tier Carbon Attribution and Trading across Complex Supply Networks

We propose methods to attribute, measure, and enable trading of carbon credits across multi‑tier supply networks with overlapping responsibilities.
Research questions:
– How can we allocate upstream emissions across downstream products and firms in networks with shared processes and co‑products?
– What market mechanisms enable efficient trading of carbon reduction obligations across tiers while preventing double‑counting?
– How does supply‑network topology affect price formation and incentives for decarbonization?
We will develop attribution algorithms, design market mechanisms (auctions, bilateral trades), simulate networked markets, and test with industrial data and policy stakeholder feedback.

16. Resilient Sourcing under Geoeconomic Fragmentation: A Game‑Theoretic Approach

We study sourcing strategies when suppliers face varying degrees of access restrictions due to geoeconomic policies (sanctions, tariffs, data localization).
Research questions:
– How do firms optimally diversify sourcing when supplier regions face probabilistic access shocks tied to geopolitical variables?
– What are equilibrium procurement behaviors in industries where countries impose retaliatory or exclusionary supply rules?
– How effective are contractual clauses (force majeure, geo‑clawbacks) in mitigating supply disruption risk?
We will build stochastic game models, parameterize scenarios with historical trade and sanctions data, and recommend procurement policies validated through agent‑based simulations.

17. Micro‑Logistics Platforms for Informal Urban Retailers: Pricing, Scheduling, and Network Design

We examine platform designs that coordinate micro‑deliveries to informal urban retailers (e.g., kiosks, street vendors) in developing cities.
Research questions:
– What pricing and scheduling mechanisms maximize platform uptake while ensuring last‑mile viability for micro‑retailers?
– How should micro‑depot locations and vehicle mix be optimized given high demand variability and constrained infrastructure?
– What social and economic impacts arise from formalizing micro‑logistics through digital platforms?
We will combine field surveys, randomized field experiments with platform pilots, and spatial‑optimization modeling to derive operational and policy recommendations.

18. Supply Chain Privacy Budgets: Quantifying Information Disclosure Trade‑Offs in Collaborative Forecasting

We introduce the concept of a privacy budget for supply chain partners sharing forecasts and demand signals, quantifying utility gain versus competitive risk.
Research questions:
– How can we measure the marginal value of different granularities of forecast sharing for supply chain performance?
– What privacy budget allocations between partners maximize joint profits while limiting competitive intelligence leakage?
– How do differential privacy mechanisms change operational decisions and inventory outcomes?
We will formalize a privacy‑utility optimization framework, simulate multi‑firm forecasting games, and test differential privacy schemes with synthetic and anonymized industrial datasets.

19. Adaptive Multi‑Modal Sourcing under Energy Price Volatility and Electrification Targets

We analyze sourcing strategies that dynamically switch transport and production modes in response to energy price shocks and corporate electrification goals.
Research questions:
– What decision rules optimally trade off higher short‑term costs versus long‑term emissions reductions when energy prices are stochastic?
– How do electrification subsidies and infrastructure rollouts alter supply network topology and modal choices?
– What are the systemic resilience implications of synchronized modal shifts across industries?
We will construct stochastic dynamic programming and agent‑based models, calibrate with energy and freight price histories, and perform scenario analyses for policymakers and corporate planners.

20. Emotion‑Aware Demand Sensing: Leveraging Social Media Affective Signals for Rapid Demand Shocks

We explore whether real‑time affective signals from social media (e.g., sentiment intensity, emotional contagion) improve detection and forecasting of rapid demand shocks (positive or negative).
Research questions:
– How predictive are emotion‑laden social signals for short‑horizon demand surges compared with traditional topic‑based signals?
– What preprocessing and bias‑mitigation steps are necessary to avoid spurious correlations and demographic distortions?
– How should forecasting models incorporate affective uncertainty into inventory and pricing decisions?
We will develop emotion‑detection pipelines, integrate signals into nowcasting models, evaluate on event‑driven retail datasets, and validate operational impact via back‑testing and limited real‑world pilots.

21. Blockchain-enabled dynamic yield allocation in perishable goods networks

We frame a topic that explores tokenized yield credits to allocate shrinking inventory across multi-echelon perishable supply chains.
Research questions: We ask how dynamic tokenization of perishable units affects freshness-based allocation, how smart contracts can enforce cross-border yield transfers under varying regulatory latency, and how token incentives change upstream production decisions.
We outline a method where we design smart-contract prototypes, simulate multi-agent perishability and settlement delays, and run controlled industry pilots to measure spoilage reduction and settlement efficiency.

22. AI-driven anticipatory logistics for climate-induced supply disruptions

We investigate anticipatory logistics systems that fuse high-resolution climate forecasts with logistics planning to pre-position inventory and reroute shipments.
Research questions: We ask how near-term climate model uncertainty should be weighted in logistics decisions, what counterfactual scenario generation best informs pre-positioning, how cost-benefit compares to traditional contingency planning, and what organizational barriers limit adoption.
We propose combining climate-model ensembles, agent-based logistics simulation, and qualitative interviews with practitioners to build decision rules and validate them on historical disruption events.

23. Micro-fulfillment hub orchestration using human-robot collaborative scheduling

We study scheduling algorithms that jointly optimize human and robot tasks in dense city micro-fulfillment hubs under stochastic demand.
Research questions: We ask how collaborative schedules should adapt to robot degradation and human fatigue, what safety and productivity trade-offs emerge, how dynamic slot pricing influences inbound flow, and how resilience to sudden demand spikes can be guaranteed.
We recommend building a discrete-event simulator fed by operational logs, implementing reinforcement-learning-based schedulers, and conducting A/B field trials in partner hubs to measure throughput, safety incidents, and labor satisfaction.

24. Circular supply chain contracts with embedded take-back credits and variability sharing

We examine contractual mechanisms that internalize returns uncertainty by issuing take-back credits and sharing variability between buyers and remanufacturers.
Research questions: We ask how credits should be priced under uncertain return quality, how variability-sharing clauses affect production smoothing and inventory levels, what contract forms balance incentives for collection, and what legal structures enable enforceability.
We plan to develop game-theoretic contract models, test contracts in behavioral lab experiments, and analyze archival data from pilot circular programs to refine contract parameters.

25. Quantum-inspired optimization heuristics for real-time multimodal routing

We explore quantum-inspired heuristics (e.g., QAOA-like classical approximations) to solve large-scale, stochastic multimodal routing problems in real time.
Research questions: We ask whether quantum-inspired heuristics offer consistent solution quality improvements over classical metaheuristics under stochastic travel times, what latency and compute trade-offs exist for real-time deployment, and how these heuristics scale with network size and constraints.
We propose designing hybrid heuristic algorithms, benchmarking on real multimodal transport datasets with injected uncertainty, and profiling compute/energy costs to assess operational viability.

26. Ethical data governance frameworks for cross-firm demand sensing ecosystems

We develop governance frameworks that enable collective demand sensing while protecting competitive data and consumer privacy across loosely coupled firms.
Research questions: We ask what minimal data-sharing primitives achieve demand-sensing gains, which privacy-preserving aggregation techniques preserve utility, how to design accountability and audit mechanisms among firms, and how such frameworks align with emerging regulation.
We will combine formal privacy-utility analysis, stakeholder workshops to elicit governance preferences, and pilot implementations that use differential privacy and secure multiparty computation to validate performance.

27. Adaptive supplier ecosystem design using evolutionary network analysis

We analyze supplier ecosystems as evolving networks and design interventions that steer evolution toward robustness and flexibility.
Research questions: We ask what micro-level tie-formation rules predict system-level resilience, which suppliers act as keystone nodes whose failure triggers cascades, how intentional rewiring (partnerships, diversification) changes evolutionary trajectories, and which early-warning indicators predict maladaptive lock-in.
We plan to apply longitudinal network inference to historical procurement and performance data, run evolutionary network simulations under shock scenarios, and co-design interventions with procurement teams for field testing.

28. Demand-supply alignment in gig-economy last-mile using reputation-weighted matching

We assess reputation-weighted matching mechanisms that align demand and delivery capacity in gig last-mile platforms while preserving fairness.
Research questions: We ask how reputation-weighting affects reliability and response times, whether it introduces systemic bias against new or marginalized couriers, how dynamic pricing interacts with reputation signals, and what matching rules optimize societal and platform objectives.
We intend to model matching algorithms, run agent-based simulations with heterogeneous courier profiles, and partner with a platform to A/B test matching variants and measure delivery performance and equity metrics.

29. Predictive maintenance marketplaces: pricing, competition, and information asymmetry

We study digital marketplaces that match fleet owners with predictive-maintenance providers who sell anomaly signals and intervention services.
Research questions: We ask how the quality and exclusivity of predictive signals affect pricing and competition, how information asymmetry between signal sellers and buyers shapes contract formats, what reputation systems mitigate adverse selection, and how marketplace design affects fleet-level downtime.
We will build market-design models, simulate marketplace dynamics under varying signal reliabilities, conduct lab experiments on contracting behaviors, and analyze transaction data from pilot marketplaces.

30. Supply chain resilience scoring using multisource satellite and IoT anomaly fusion

We propose a resilience scoring methodology that fuses satellite imagery, AIS/telemetry IoT feeds, and procurement data to predict near-term supplier outages and operational fragility.
Research questions: We ask how to weight heterogeneous anomaly signals to produce meaningful resilience scores, how early the fused signal predicts actual disruptions, how scores influence procurement decisions, and what false-positive/negative trade-offs are acceptable to buyers.
We will develop data-fusion models (statistical and ML), validate predictions against recorded outages in multiple industries, and pilot a procurement decision-support tool to measure changes in sourcing choices and realized resilience.

31. Decentralized reputation-led procurement networks for SMEs

We investigate: 1) How does a decentralized (blockchain-enabled) reputation mechanism change SME supplier selection and negotiation power? 2) How does reputation granularity affect network resilience and fraud reduction? 3) What governance rules best balance transparency and privacy for SMEs? We will design a tokenized reputation prototype, run agent-based simulations of market outcomes, and pilot the system with a cluster of SMEs to collect transaction and interview data for mixed-methods evaluation.

32. Adaptive inventory policies using edge-AI for last-mile cold chains

We ask: 1) Can on-device AI at distribution points reduce spoilage while preserving energy and connectivity budgets? 2) How do adaptive policies respond to microclimate variability and delivery disruptions? 3) What are the cost–benefit thresholds for edge versus cloud approaches? We will build RL-based controllers deployable on low-power edge hardware, run hardware-in-the-loop simulations with real sensor streams, and conduct controlled field trials with logistics partners to measure spoilage, latency, and energy consumption.

33. Embedding social-value metrics into multi-tier carbon accounting

We probe: 1) How can social-value indicators (livelihoods, health, gender equity) be operationalized alongside scope 1–3 emissions across tiers? 2) Does incorporating social metrics change procurement and supplier development decisions? 3) What methodological adjustments are needed for verifiable multi-tier reporting? We will co-develop an accounting framework with NGOs and firms, pilot it on three multi-tier supply chains, and use mixed-methods (LCA extension, interviews, econometrics) to assess decision impacts.

34. Quantum-inspired optimization for real-time dynamic routing under uncertainty

We explore: 1) Do quantum-inspired heuristics (e.g., QAOA-inspired metaheuristics) outperform classical benchmarks in near-real-time routing with stochastic travel times? 2) What problem sizes and uncertainty patterns favor quantum-inspired approaches? 3) How do solution stability and interpretability affect operations adoption? We will implement quantum-inspired algorithms, benchmark them against state-of-the-art heuristics on synthetic and field-sourced datasets, and conduct sensitivity analysis to characterize performance envelopes.

35. Platform-mediated crowd-sourced reverse logistics for e-waste collection

We examine: 1) Which platform design features (dynamic pricing, social proof, gamification) maximize collection rates and data quality? 2) How do local informal-collector networks interact with formal platform actors? 3) What are the environmental and economic trade-offs of decentralizing reverse logistics? We will develop a minimum-viable platform, run randomized experiments on pricing and user-interface variants in urban neighborhoods, and combine transaction analytics with ethnographic study of collectors.

36. Human–AI collaboration interfaces for supplier risk assessment

We investigate: 1) Which explanation styles (counterfactuals, feature-attribution, example-based) improve procurement managers’ trust and decision accuracy? 2) Can interface interventions mitigate common cognitive biases in supplier risk judgments? 3) What organizational workflows optimize human-AI throughput in multi-tier risk monitoring? We will prototype alternative interfaces, run controlled lab experiments with procurement professionals using eye-tracking and decision-quality metrics, and validate findings through in-situ pilots.

37. Tokenized incentive structures for traceability in smallholder agricultural supply chains

We ask: 1) Do micro-token incentives tied to verifiable trace events increase data completeness and farmer compliance? 2) How do token economics affect market access and price realization for smallholders? 3) What governance and technical constraints impede scaling? We will co-design a lightweight token scheme with cooperatives, implement a pilot with mobile data collection, evaluate behavioral responses via an RCT, and model long-run market implications econometrically.

38. Resilience metrics for intermodal freight under climate shock cascades

We probe: 1) What composite metrics best capture fragility and recovery capacity of intermodal networks facing cascading climate shocks? 2) How do investment prioritizations change when using cascade-aware versus single-shock metrics? 3) Which policy levers most cost-effectively increase network resilience? We will build a cascade-capable network stress-testing tool (spatially explicit), calibrate it with historical shock/traffic data, and run counterfactual investment scenarios using optimization and cost-benefit analysis.

39. Ethical-sourcing AI: fairness-aware procurement algorithms

We examine: 1) How can fairness constraints (geographic equity, small-supplier inclusion) be operationalized in procurement optimization without unacceptable cost inflation? 2) What unintended supplier behaviors emerge when fairness-aware algorithms are adopted? 3) Which auditing protocols ensure algorithmic sourcing decisions meet ethical claims? We will formulate constrained optimization models, simulate market reactions using behavioral agent models, and implement field audits in collaboration with public-sector procurers.

40. Biosecurity-aware global procurement strategies for critical medical supplies

We ask: 1) How should firms balance cost, lead-time, and contagion-risk when sourcing critical medical inputs across borders? 2) What early-warning signals in supply-chain data predict biosecurity vulnerabilities? 3) Which diversification and stockpiling strategies minimize expected disruption under plausible pandemic scenarios? We will integrate epidemiological contagion modules with multi-echelon procurement models, perform scenario-based optimization and robust decision-making analyses, and validate recommendations through stakeholder workshops with health-system buyers.

41. Blockchain-enabled decentralized carbon-credit traceability across multi-tier supply chains

We propose research questions: How can we design a permissioned blockchain and data governance model to attribute scope 3 emissions at SKU-level across multiple supplier tiers? How can we integrate IoT sensor telemetry and validated third-party audits to minimize greenwashing risk? What incentives and contractual clauses will motivate upstream suppliers to share high-fidelity emissions data?
We outline how we will work on it: We will develop a prototype permissioned ledger and smart-contract templates, integrate IoT data ingestion pipelines in a laboratory testbed, run agent-based simulations of supplier behavior under alternative incentive schemes, and conduct case-study pilots with one focal manufacturer and its tier-1/2 suppliers to evaluate data quality, cost, and behavioral change.

42. AI-driven anticipatory reverse logistics for end-of-life consumer electronics

We pose the research questions: How can we forecast returns and product end-of-life flows at fine temporal and spatial granularity using transactional, usage and warranty data? How can we jointly optimize anticipatory pickup routing and refurbishment/redistribution decisions under capacity constraints? What business models align retailer incentives with circular outcomes?
We outline how we will work on it: We will build and validate machine-learning models for return probability using retailer and warranty datasets, formulate mixed-integer programs for anticipatory routing and processing prioritization, perform computational experiments on synthetic and vendor-supplied data, and run a field pilot with a retail partner to measure cost, lead time, and environmental impacts.

43. Quantum-inspired stochastic inventory optimization under correlated disruption risks

We ask: How does modeling disruption risk as correlated, systemic events change optimal multi-echelon inventory policies compared to independent-shock models? Can quantum-inspired optimization (e.g., simulated annealing mapped from quantum annealers) scale to large non-convex inventory problems and yield better resilience-cost trade-offs?
We outline how we will work on it: We will formulate correlated-disruption stochastic inventory models, implement quantum-inspired solvers and classical baselines, benchmark performance on realistic supply-chain topologies derived from industry datasets, and perform sensitivity analysis on correlation structures and mitigation strategies (redundancy, safety stock, lead-time diversification).

44. Circular-economy-enabled multi-tier supplier financing using returned product-as-collateral

We ask: How can we structure financing instruments where returned products (or their parts) serve as collateral across multiple supplier tiers? What valuation, legal, and operational frameworks are required to make such financing bankable for SMEs? What impact does product-as-collateral financing have on upstream working capital and reuse rates?
We outline how we will work on it: We will design contract structures and valuation methodologies, create a credit-risk model incorporating return-rate uncertainty and refurbishability, run agent-based financial simulations to test cash-flow impacts on suppliers, and pilot the approach with a trade finance partner and an OEM managing refurbished parts.

45. Human–AI hybrid negotiation protocols for procurement in volatile markets

We ask: How should procurement workflows be redesigned so human buyers and autonomous negotiation agents jointly reach better price, quality and resilience outcomes under high volatility? What trust, explainability and override mechanisms are necessary to ensure adoption and ethical outcomes?
We outline how we will work on it: We will co-design hybrid negotiation protocols, implement autonomous bargaining agents with interpretable decision logs, conduct controlled laboratory experiments with procurement professionals simulating volatile market scenarios, and measure efficiency, fairness, and user trust metrics, followed by a pilot in a buyer organization.

46. Interoperability standards of digital twins and their impact on multi-modal logistics resilience

We ask: To what extent do interoperability gaps among digital-twin standards (data models, APIs, semantic layers) reduce the ability of multi-modal logistics networks to respond to disruptions? Which minimal standard extensions yield the largest resilience gains?
We outline how we will work on it: We will map existing digital-twin specifications across port/rail/truck/air domains, construct an emulated multi-modal network of interoperable and non-interoperable twins, run disruption scenarios to measure decision latency and coordination loss, and develop recommendations and reference adapters for minimal interoperable interfaces validated with industry stakeholders.

47. Design of micro-fulfillment networks using autonomous cargo drones for urban vertical delivery

We ask: What is the optimal spatial distribution of micro-fulfillment nodes and vertical drone corridors in dense urban settings to minimize delivery time, energy use and congestion externalities? How do regulatory airspace constraints and rooftop infrastructure availability change network design?
We outline how we will work on it: We will combine GIS-based demand modeling, vehicle energy and payload models for drones, and mixed-integer nonlinear optimization to design hybrid drone+ground micro-fulfillment networks, perform Monte Carlo scenario analysis under different regulatory regimes, and validate with a constrained pilot simulation using city-scale demand data.

48. Ethical algorithm audits for allocation fairness during acute supply shortages

We ask: How can we operationalize ethical algorithm audits for automated allocation systems (e.g., ventilators, vaccines, food aid) to detect disparate impacts on vulnerable communities? What audit metrics, data requirements and stakeholder-engagement processes produce actionable remediation?
We outline how we will work on it: We will develop an audit framework combining fairness, transparency and robustness metrics, apply it to historical allocation algorithms through synthetic counterfactuals and real-world datasets, facilitate participatory audits with affected communities and decision-makers, and produce a playbook for algorithmic remediation and policy adoption.

49. Edge-computing architectures for real-time decision-making in perishable cold-chains

We ask: How can edge-computing nodes execute low-latency anomaly detection, dynamic routing and microclimate control to reduce spoilage in distributed cold-chain networks? What trade-offs exist between edge model complexity, energy consumption and decision quality?
We outline how we will work on it: We will design lightweight ML models for anomaly detection deployable on edge hardware, prototype distributed decision protocols for rerouting and temperature adjustments, run lab and field experiments on perishable shipments to measure spoilage reduction and latency, and analyze energy-cost-resilience trade-offs.

50. Platform cooperatives for integrating smallholder farmers into global supply-chain finance and traceability

We ask: How can platform-cooperative governance and shared data trusts enable smallholder farmers to access supply-chain finance and traceability services while preserving agency and equitable value capture? What technical, legal and economic designs ensure sustainability at scale?
We outline how we will work on it: We will co-design platform-cooperative structures with farmer organizations, build a minimal traceability and collateralization tech stack (data trusts, access controls), simulate economic outcomes under alternative revenue-sharing rules, and conduct a regionally scoped pilot to measure finance uptake, price realization and trust metrics.