Istanbul Airport IGA – Competitive Strategies for Air Cargo Carriers in a New Competitive Environment – A Fuzzy Multi-Criteria Approach

Recommendation: Implement a unified fuzzy multi-criteria decision framework in IGA’s cargo ecosystem to guide terminal allocation, carrier selection, and service levels. This application translates measures of cost, time, reliability, and risk into action, enabling executives to make fast, data-driven choices. Build a centralized cargo control tower and download scenario data for screens used by frontline teams, ensuring decisions feed directly into purchase planning and schedule optimization. tanräverdi-inspired routines can keep the model with current demand signals, to help executives act quickly.

The new competitive environment for air cargo at IGA calls for explicit prioritization of high-value shipments and time-critical flows. The terminal design supports scalable capacity, modular handling zones, and integrated baggage and freighter operations, creating different paths for standard and express lanes. Use the fuzzy MCDA to compare options on the following axes: throughput potential, asset utilization, service level, and cost per kilogram. This approach helps makes decisions that align with carrier strategies and airport objectives while maintaining flexibility to reallocate capacity during peak periods.

Following steps ensure a fast, repeatable cycle: (1) profile each carrier by service type and required turnaround time; (2) assign weights to highest priority factors; (3) run scenario tests that combine baggage handling, security screening timelines, and terminal dwell times; (4) convert results into concrete actions for ramp teams and executive reviews. The model supports purchase decisions for capacity blocks and equipment, with outputs that help different teams across terminal operations and tenant management.

For carriers, embed the framework in governance: quarterly reviews, monthly data exports, and an executive dashboard that shows key metrics such as capacity utilization, on-time performance, and revenue per kilogram. The approach differentiates offers for express and standard cargo while preserving flexibility to shift from dedicated blocks to shared space as demand fluctuates. Use ewra data streams and download feeds to keep the model current, and find opportunities to optimize scheduling around peak flight windows.

Operationally, invest in real-time screens at a cargo desk, empower ramp teams with an application layer that ties to bookings, and maintain a transparent atmosphere where executive decisions are visible to all stakeholders. The result is a more highest service level for critical shipments and a measurable reduction in misrouted items in the baggage and freight mix. The atmosphere among staff becomes more collaborative as data supports every pick, pack, and handoff; teams worked to validate the approach against typical peak-day conditions and saw faster purchase approvals and smoother load planning.

Modeling Cargo Competitiveness with a Fuzzy Multi-Criteria Framework at Istanbul Airport

Recommendation: Implement a fuzzy multi-criteria framework to rank cargo competitiveness at Istanbul Airport using a weighted scorecard across five criteria: freight cost per kilogram, reliability, route access, terminal throughput, and service quality. Calibrate fuzzy scales with inputs from industry experts such as wang, ji-feng, thomas, kong, and durak to ground the model in real-world judgments and operational data.

Extensive data collection feeds the model: freight volumes, on-time performance, route lists, terminal counters occupancy, handling times, and guest feedback from shippers. The assessment follows a whole-system view that connects transportation planning with terminal operations, booking processes, and cargo handling workflows within the airport system.

Structure the framework around a clear perspective that translates qualitative judgments into quantitative scores. Each criterion uses a consistent fuzzy scale (low, medium, high) with triangular or trapezoidal membership functions, enabling smooth transitions between performance bands and reducing abrupt switches in ranking as data shifts.

• Criteria and weights
• Cost efficiency: freight cost per kilogram, access charges, and value-added services;
• Reliability: on-time pickup/delivery, schedule adherence, and disruption resilience;
• Route access: number of viable direct routes, connectivity to hubs like Singapore and Munich, and transfer options;
• Terminal throughput: handling capacity, counter turnaround times, equipment availability, and berth occupancy;
• Service quality: booking accuracy, digital tracking, documentation speed, and customer support responsiveness.
• Data sources and validation
• Freight system logs, route catalogs, and terminal counters data;
• Shipper and consignee feedback captured in guest surveys and issue logs;
• Literature benchmarks from peer airports to anchor expectations;
• Comparative references to hubs such as Singapore and Munich for cross-checking route viability.
• Modeling approach
• Adopt a fuzzy TOPSIS or fuzzy weighted-sum aggregation to derive a score for each carrier-route pair;
• Apply expert calibration to set membership function shapes and criterion weights, leveraging input from ji-feng, wang, thomas, kong, and durak;
• Translate results into a ranked list that highlights winning routes and carrier combinations for Istanbul Airport.
• Calibration and governance
• Establish a governance panel with named contributors (e.g., ji-feng, wang, thomas, kong, durak) to review scales, weights, and outliers;
• Periodically refresh data feeds and re-run the model to track changes in market conditions and airport operations;
• Document the assessment procedures so counters, terminal teams, and freight partners can align actions with results.
• Implementation plan
• Phase 1: assemble data, define criteria and initial weights, and build fuzzy schemes;
• Phase 2: run a six-month pilot focusing on core routes and a subset of carriers, including connections to Singapore and Munich;
• Phase 3: publish a competitiveness dashboard for airport management, freight forwarders, and carriers, with recommended operational moves;
• Phase 4: scale the model to all routes and convert results into actionable actions at counters, terminals, and the transport system.

Practical actions arise from the model: optimize route turn times at counters, accelerate processing in the terminal, align freight orders with peak flows, and adjust service offerings to bolster competitiveness for Istanbul Airport.

Outcome targets emphasize an outstanding balance between cost discipline and throughput, with a clear path to elevating the overall freight experience for guests and partners in the network. By anchoring decisions in a reality-grounded, evidence-driven framework, stakeholders gain a transparent view of how route choices, terminal performance, and service quality drive competitive position in the cargo market.

Key Takeaways from Kadri Samsunlu’s Interview for Carrier Strategy and Terminal Operations

Implement a forecasting-driven platform using real-time inputs to serve guests and boost competitiveness across aviation and cargo networks. Using these data streams, forecast demand, coordinate between flight schedules and terminal workflows, and shorten handling times to turn uncertain periods into predictable outcomes.

Synchronize policy decisions with building plans at key hubs, especially incheon facilities, to reduce dwell times and improve handover efficiency between carriers and ground handlers. The approach relies on clear governance, aligned KPI targets, and a shared road map for april milestones.

Track safety, maintenance, meters, items, and material handling as core metrics. A focus on preventive maintenance and proper calibration reduces disruptions, using standardized procedures to raise safety standards and strengthen reliability across existing processes.

Collaborate with providers and carriersâ to build a single data model that connects chen’s operations with guests and customers. Following data-sharing agreements, the platform improves forecasting accuracy and strengthens competitiveness through shared insights and building trust with clients, turning gaps into opportunities that the team is proud of.

Embed the april forecast, exploring opportunities at existing facilities, and download an actionable checklist to guide implementation. The approach supports a million cargo movements and travel items, enabling each carrier to optimize routes, service levels, and safety compliance.

How to Use IDEAS References and Prepare Proper Citations for iGA Research

Please start by exporting IDEAS records as BibTeX and import them into your preferred reference system to seed the iGA study folder. This initial step ensures you have native metadata (title, year, DOI, author list) ready for accurate citations.

When you build a literature log, capture author names such as Tanräverdi, Chou, Mehmet, and others to verify spellings before you finalize the manuscript. This reduces recognition errors in the final references.

Access IDEAS with targeted keywords: airport, cargo, operations, aircraft, material, and security. Within each entry, note the document type (article, working paper, conference), the publication date (for example July 2020 or July 2023), and the repository’s URL or DOI. Please attach this data to the citation record in your system.

Provide a consistent mapping from IDEAS fields to your chosen style. For instance, in-text citations can follow the author-year format (Tanräverdi, 2019) or a numeric style if your group supports it. Within each method section, align the reference list to the selected style to avoid mismatch across the project.

Before drafting the literature review, create a dedicated bibliography file and share it with the project staff. A single source file helps the group maintain version control and reduces duplication when you purchase or cite multiple sources. Use tags like airport, group, staff, and project to keep entries searchable within the system.

To ensure accuracy, cross-check each entry against the original IDEAS record and, if needed, retrieve the PDF to confirm page numbers, chapter titles, and author affiliations. This step is especially important for documents published by Turkish scholars such as Mehmet or Tanräverdi who often publish in conference proceedings linked to airport research projects.

Within your manuscript, place citations close to the related discussion: methodology, data sources, or case examples from Istanbul Airport cargo operations. This approach keeps the narrative cohesive and helps readers verify claims with the cited material. Please also include a brief note on any limitations or data access constraints that accompany the IDEAS source.

If the work involves a team, establish roles: one person handles IDEAS discovery, another formats citations, and a third validates references for consistency. This division reduces errors and speeds up the July deadlines you face; thanks to a clear workflow, staff can maintain high quality without bottlenecks.

Keep a square checklist to track stages: discovery, export, verify, format, and finalize; treat the initial IDEAS export as a baby step toward a fully documented citation stream.

Lastly, acknowledge sources with a brief gratitude line in the acknowledgments section, and provide a separate list or appendix containing full references to improve recognition for contributors such as Chou group members or Tanräverdi researchers who supported the iGA study. If asked, you can share a sample citation of the iGA cargo paper on Instagram or other channels to illustrate proper attribution and engagement practices.

Practical workflow for citation integrity

Create a shared spreadsheet or repository log with columns for unique ID, author, year, title, source type, DOI, keywords, and notes. Use this log to track access dates and material status; for example, a record might note access on July 12, 2024, and whether the PDF is secured or restricted.

In the final manuscript, ensure each citation appears in a compatible format and that the reference list presents entries in the chosen order (alphabetical or numeric). For a collaborative project, consider a weekly sync with the staff to update the log and resolve any discrepancies in author spellings or publication years.

Impact of iGA’s Special Passenger Program on Cargo Flows and Carrier Collaboration

Adopt a unified hand-off framework between passenger operations and cargo teams within iGA’s Special Passenger Program to stabilize and grow cargo flows starting now.

The most immediate gains come from five interlinked points: real-time data exchange from passenger to cargo systems, predictable handoffs at transfer zones, tight carrier contact for updates, clear procedures for corrections, and a focused approach on European and regional flights.

Mehmet from management and Su-Sin lead the cross-functional quest, aligning carrier partners through regular contact and a shared info suite that supports both pax and freight teams. The approach greet s stakeholders with transparent metrics and a trackable workflow, so ships move with confidence rather than waiting in queue.

To move from plan to practice, the following measures will be deployed in 60-90 days, with baby steps that build momentum: digital data bridge; dedicated transfer waiting zones; live status screens with automatic corrections; short, five-minute handoff windows at key touchpoints; cross-dock training with focus on European routes and real-time contact protocols.

Expected outcomes include a 8-12 minute reduction in waiting times for pax-to-cargo transfers, a 5-7% uplift in cargo throughputs on European flights, and a 10% improvement in on-time transfer for high-priority freight. Dashboards watch key metrics such as on-time transfer rate, load factor, and dwell times, and these results are shared with partner airlines via direct contact channels, with info accessible to both sides.

Table 1 summarizes the measures, purposes, expected cargo flow impact, owners, and timeline.

Note: the term durak is used informally in drills to label bottlenecks, keeping the team ready to respond without disruption.

Metrics, Ratings, and Benchmarking: From 5-Star Recognition to Cargo Performance Indicators

Adopt a four-tier cargo performance KPI framework that translates 5-Star recognition into concrete, trackable indicators. Link each star level to target ranges for key cargo metrics and publish monthly scorecards for cross-functional teams.

Metrics should cover reliability, safety, asset protection, and cost efficiency. Examples: OTIF (on-time in-full) rate; damage rate; misrouted shipments; dwell time at origin and destination; handling accuracy; detention and demurrage; inventory accuracy; and cost per ton-km. Add safety indicators such as incident rate and adherence to standard handling procedures. Use four-tier classifications (Excellent, Strong, Moderate, Needs Attention) with explicit thresholds by route type and service level. Provide concrete targets per quarter, e.g., OTIF ≥ 95%, damage ≤ 0.25%, misrouting ≤ 0.2%, dwell time targets by airport, and safety incidents < 1 per 100k moves.

Apply a fuzzy multi-criteria approach to aggregate indicators. Use a pi-hui weighted scheme to assign importance to each criterion and compute a Cargo Performance Index (CPI) on a 0–100 scale. The method handles data gaps via belief intervals and upper/lower bounds. Validate with completed datasets and sensitivity tests; refresh weights monthly from the operations data feed to keep rankings credible. Build a copy of the methodology for audit and training purposes and ensure the figures are downloadable in CSV or Excel formats.

Benchmarking process: align CPI with external standards from peer airports and published literature. Create a dedicated benchmarking page with sections for route performance, cargo volume, safety events, and cost efficiency. Use literature sources and industry reports; download datasets when available; compare against four leading peers to assess competitiveness. Note data limitations such as lags, seasonal effects, and pandemic-related shocks. Document the rationale and update the report pages quarterly, with clear links to source pages and a persisted assessment log. Provide baby steps in the pilot, learning so teams learnt how to tighten limits and improve efficiency over time.

Framework and Metrics

Establish governance with KPI owners per function (operations, safety, finance) and a named sponsor at the executive level. Ensure data quality through cross-checks, redundancy, and daily reconciliation across WMS, TMS, and yard screens. Use nursing-style checklists to confirm each cargo move follows standard steps: pick, verify, label, load, and release. Maintain pi-hui and su-sin indices for safety and reliability, with clear targets that feed the CPI. Make sure the recognition program ties to measurable performance and not just reputational signals; the goal is to convert perception into tangible improvements in operations and safety.

Provide access privileges so relevant teams can view and modify data; publish pages with each KPI, its owner, thresholds, and recent completed assessments. Ensure safe download of data and reports, and keep a continuous improvement mindset to avoid silos. Build belief in the framework by sharing quick wins and documented cases where minor changes yielded outsized gains in on-time performance and damage reduction.

Benchmarking, Data Quality, and Implementation

Implement a two-phased rollout starting with four routes to establish baselines and test the calibration of the CPI. Use a 90-day pilot to validate data sources, including screens from WMS and TMS, and to verify that the four-tier classifications align with observed outcomes. Track completed actions, assess data gaps, and iterate on the weighting scheme so teams can rely on a robust assessment. Use download-ready reports and pages to share results with stakeholders, and ensure the copy of the methodology is accessible for audits and training.

In practice, couple external benchmarking with internal targets. Compare OTIF, damage, and dwell time against peers and literature benchmarks; adjust the four-tier thresholds to reflect route complexity and seasonal demand. Monitor competition levels and adjust privileges for data access to protect sensitive information while enabling collaboration. Track progress against the stated goal of improving cargo reliability, safety, and cost efficiency, and translate CPI improvements into real business advantages such as faster processing, lower detention costs, and higher competitiveness in a crowded market. Ensure the process remains resilient during pandemic shocks by maintaining contingency data paths and learnt insights for quicker recovery.