Supply chain challenges and recommendations for international development agriculture projects: an application of the FGD-fuzzy Delphi approach

The paper addresses the overlooked issue of supply chain management in international development (ID) agriculture projects, which operate in complex environments characterized by political, legal, cultural, organizational, economic, and environmental challenges. Donor influence, dual decision-making, and incompatible regulations further complicate procurement and supply chain processes. Because projects have fixed timelines and budgets, fragmented supplier networks and long lead times intensify supply chain complexity, and project mismanagement (e.g., delayed initiation, poor design, unclear objectives, inadequate risk analysis, and weak monitoring and evaluation) can lead to failure. Effective, holistic project supply chain management is therefore essential for optimizing value and achieving project goals and broader socio-economic development outcomes in host countries. The study targets Bangladesh’s public-sector agriculture projects—a vital sector employing over 40% of the workforce—where procurement and supply chain inefficiencies often delay projects and trigger cost and schedule overruns. The research aims are: (i) to determine the most significant supply chain challenges for public-sector ID agriculture projects in Bangladesh, and (ii) to identify and rank the most important recommendations to overcome these challenges, so practitioners can allocate limited resources efficiently and ensure value for money.

The literature distinguishes project supply chain management from product supply chains due to projects’ unique, time- and budget-constrained nature and unfamiliar tasks. Project-based industries often face fragmented supplier networks and low coordination. A conceptual model positions project supply chains as two linked chains—planning (stakeholder focus; time/resource management; procurement/supplier focus) and delivery (supply management; conversion; handover/closure)—with cross-cutting components (systems/procedures; regular review; quality/performance) that relate to identifying and managing supply chain challenges. For developing countries’ public-sector ID agriculture projects, added stakeholders (government, donors, vendors, beneficiaries) and donor-imposed processes increase complexity, yet academic focus on agriculture project supply chains is scant. To build a challenge framework, the study synthesizes limited Bangladesh-specific literature and broader procurement/supply chain studies, grouping challenges into sponsor-related (e.g., incompatible donor rules; complex fund disbursement; coordination issues), project management-related (e.g., resource scarcity, weak planning), supply chain-related, contextual (e.g., political influence, social/cultural issues, natural disasters), and institutional (e.g., bureaucracy, corruption) categories. This review underpins a primary challenge list later refined via FGD and expert validation.

An exploratory, multi-method design combined: (1) literature review to form an initial challenge list; (2) two sequential focus group discussions (FGDs) with industry experts to identify key challenges and develop recommendations; (3) expert panel validation (four supply chain experts/academics) of FGD outputs; and (4) fuzzy Delphi method (FDM) to rank recommendations for each challenge. Experts were purposively and via snowball sampling drawn from the Department of Agricultural Extension (DAE), Bangladesh—the largest public agricultural organization—which implemented several ID projects totaling ~USD 170 million during 2020–2021. Ten practitioners with 8–15 years’ experience participated, consistent with prior FGD/FDM studies. To mitigate common method/source bias, the study separated predictor/criterion sources, improved item wording to reduce ambiguity and social desirability, introduced psychological separation, and ensured anonymity. In the FDM, experts rated recommendations using a seven-point linguistic scale mapped to triangular fuzzy numbers. Aggregation used fuzzy addition/multiplication to compute fuzzy means (L, M, U); defuzzification generated Best Non-fuzzy Performance (BNP) values to produce ranks for recommendations per challenge. Mathematical steps followed established FDM procedures (e.g., Mazlan et al., 2019), with illustrative scales and equations provided (linguistic scale, membership functions, mean aggregation, BNP calculation).

- Seventeen key supply chain challenges for public-sector ID agriculture projects were identified (Table 3): (1) delayed start of projects; (2) improper demand forecasting during project preparation; (3) donor’s incompatible rules and regulations; (4) improper procurement planning; (5) complex fund disbursement process; (6) scope creep/design change; (7) lack of contract monitoring/management mechanism; (8) natural disaster and climate change; (9) lack of logistical support; (10) political influence; (11) bureaucracy in project approval and implementation; (12) lack of competent procurement staff; (13) social and cultural grievances; (14) biological disease and pest; (15) delay of key staff hiring; (16) frequent movement/transfer of key project officials; (17) improper communication. - Compared to the primary list from literature, “lack of coordination between donors and key stakeholders” and “lack of institutional ethics” were excluded; “improper communication” was added. - For each challenge, specific recommendations were developed and ranked via fuzzy Delphi based on expert judgements (10 experts). Examples of top-ranked recommendations include: • Delayed start: implement a time-bound digitized project approval process; timely deployment of the project director; ensure timely donor fund disbursement. • Improper demand forecasting: proper negotiation with donors at appraisal/agreement; more effective feasibility and baseline studies; use updated, relevant data and involve key stakeholders. • Donor-incompatible rules: negotiate terms during preparation/agreement; simplify decision-making; clarify mutual responsibility and accountability in financial agreements. • Improper procurement planning: involve procurement professionals during planning; deploy procurement experts in planning units; establish dedicated procurement cells and capacity-building programs. • Complex fund disbursement: negotiate effective procedures with donors; plan/schedule considering disbursement hurdles; strengthen monitoring; reduce bureaucracy; minimize exchange rate gaps. • Scope creep/design change: ensure timely fund disbursement per DPP; involve experienced experts and stakeholders during initiation/preparation; timely project start; better feasibility/baseline data. • Lack of contract monitoring: integrate contract monitoring into e-GP; develop contract monitoring/governance/evaluation manuals; align TOR with project objectives; training; enhance accountability. • Natural disasters/climate: conduct Environmental Impact Assessment (EIA) at preparation; plan using climate/weather data; ensure inter-agency communication; proper monitoring. • Lack of logistical support: approve projects based on actual requirements; ensure implementing agency coordination/support; conduct proper feasibility studies; engage planning units. • Political influence: appoint experienced project directors; include all procurement categories and contract management in e-GP; carefully prepare TOR; consider transparency tools (e.g., blockchain), online M&E, and public disclosure. • Bureaucracy: implement time-bound e-filing/online correspondence and approval/revision processes; provide procurement/project management training; deploy qualified monitoring personnel; exercise delegated financial powers; online PIM&E systems. • Lack of competent procurement staff: TORs aligned with objectives; timely PD deployment; training; KPI-based incentives; performance metrics; simplify decision-making. • Social/cultural grievances: involve stakeholders in planning; negotiate with donors; conduct Social Impact Assessments; effective forecasting; agency monitoring during implementation. • Biological disease/pest: implement effective pest surveillance/control; establish/strengthen surveillance systems; plan with climate/weather data; conduct EIA. • Delay of key staff hiring: ensure timely project start; timely appointment of PD; include staff deployment schedules/qualifications in DPP; unbiased recruitment; agency accountability; shorten recruitment. • Frequent transfer of key officials: discourage transfers unless for rule violations; prepare effective TORs; ensure transparency in recruitment/transfer; review recruitment rules; consider binding agreements. • Improper communication: deploy expert/trained personnel; include communication plans; ensure timely communication; strengthen M&E communication capability; deploy knowledgeable donor desk officers; provide logistics; improve technical know-how; implement software-based communication; simplify decision-making. - Contextual note: Prior studies indicate average ID project schedule overrun of ~34.41% in Bangladesh; delays in starts contribute to this (cited).

The study demonstrates that ID agriculture project supply chains in developing countries face intertwined, context-specific, donor-driven, and environment-related challenges. By ranking targeted recommendations per challenge using fuzzy Delphi, practitioners can prioritize actions to maximize value for money under resource constraints. Addressing donor incompatibilities through upfront negotiation and simplified decision-making can reduce dual systems and delays. Strengthening planning (feasibility/baseline, stakeholder involvement, procurement expertise) mitigates scope creep and poor procurement outcomes, which are particularly harmful in season-dependent agriculture projects. Embedding contract monitoring within e-GP and instituting dedicated manuals, training, and accountability mechanisms can improve execution. Integrating EIA with climate-risk considerations, using climate/weather data, and enhancing inter-agency communication improve resilience to natural disasters, pests, and diseases. Organizational reforms (time-bound digital approvals, e-filing, training, delegated authority) reduce bureaucracy and political interference, while robust TORs, timely staffing, and limiting transfers stabilize project management capacity. Improved communication structures and capable personnel on both host and donor sides reduce miscommunication and delays. Theoretical implications include advancing project supply chain literature by integrating FGD with fuzzy Delphi to evaluate and prioritize mitigation strategies; practical implications span policy formulation, donor-host agreements, and operational guidance to enhance partnership outcomes and ensure value for money.

This study is the first to comprehensively identify and prioritize supply chain challenges and tailored recommendations for public-sector ID agriculture projects in a developing-country context, using Bangladesh as a case. Seventeen key challenges were confirmed, spanning host-context issues (e.g., political influence, bureaucracy, staffing), donor-related issues (e.g., incompatible rules, complex disbursements), joint issues (e.g., delayed start, poor planning, logistics, communication), and environment-specific issues (e.g., disasters, pests/diseases). Through FGD and expert validation, and ranking via fuzzy Delphi, the study provides actionable, prioritized strategies enabling practitioners and policymakers to allocate scarce resources effectively and improve project performance and value for money. Future work should broaden stakeholder perspectives (vendors, donors), expand sample sizes, and incorporate additional statistical validation to further enhance robustness.

- Perspectives were primarily from project professionals; future studies should include vendors, donors, and other stakeholders. - FGD and fuzzy Delphi outcomes depend on expert selection and data quality; inadequate selection/collection may bias results. - Reliance on MCDM tools and expert confirmation introduces limitations; larger samples and supplementary statistical validity checks are recommended to mitigate bias and improve reliability.