Istanbul Airport IGA – Strategie competitive per i vettori di merci aeree in un nuovo ambiente competitivo – Un approccio fuzzy multi-criterio

Raccomandazione: Implementare un framework decisionale multicriteriale fuzzy unificato nell'ecosistema cargo di IGA per guidare l'allocazione del terminal, la selezione del vettore e i livelli di servizio. Questo applicazione traduce misure di costi, tempi, affidabilità e rischio in azione, consentendo ai dirigenti di prendere decisioni rapide e basate sui dati. Costruisci una torre di controllo centralizzata per il carico e scarica dati di scenario per schermi utilizzato dai team in prima linea, garantendo che le decisioni confluiscano direttamente nella pianificazione degli acquisti e nell'ottimizzazione della programmazione. Le routine ispirate a tanräverdi possono mantenere il modello aggiornato con i segnali della domanda corrente, per aiutare i dirigenti ad agire rapidamente.

Il nuovo ambiente competitivo per il trasporto aereo di merci presso IGA richiede un'esplicita definizione delle priorità per le spedizioni di alto valore e i flussi urgenti. Il design del terminal supporta una capacità scalabile, zone di movimentazione modulari e operazioni integrate di gestione dei bagagli e dei cargo, creando percorsi diversi per corsie standard ed express. Utilizzare il metodo MCDA fuzzy per confrontare le opzioni sui seguenti assi: potenziale di throughput, utilizzo delle risorse, livello di servizio e costo per chilogrammo. Questo approccio aiuta a prendere decisioni che si allineano con le strategie dei vettori e gli obiettivi dell'aeroporto, mantenendo al contempo la flessibilità necessaria per riallocare la capacità durante i periodi di picco.

Seguente steps garantiscono un ciclo rapido e ripetibile: (1) profilare ogni vettore per tipo di servizio e required inversione time; (2) assign weights to più alto fattori di priorità; (3) eseguire test di scenario che combinano bagaglio gestione, tempistiche dei controlli di sicurezza e tempi di permanenza al terminal; (4) convertire i risultati in azioni concrete per i team di rampa e dirigente recensioni. Il modello supporta acquisto decisioni per blocchi di capacità e attrezzature, con output che aiutano diversi team attraverso terminale operazioni e gestione dei tenant.

Per i vettori, integrare il framework nella governance: revisioni trimestrali, esportazione mensile dei dati e una dashboard esecutiva che mostri metriche chiave come l'utilizzo della capacità, la puntualità e il ricavo per chilogrammo. L'approccio differenzia le offerte per il carico espresso e standard, preservando al contempo la flessibilità di passare da blocchi dedicati a spazio condiviso in base alla domanda fluttua. Utilizza flussi di dati ewra e scarica feed per mantenere aggiornato il modello, e trova opportunità per ottimizzare la programmazione intorno alle fasce orarie di picco dei voli.

Dal punto di vista operativo, investi in tempo reale schermi presso un banco merci, fornire ai team di rampa un applicazione livello che si lega alle prenotazioni e mantenere un'atmosfera trasparente in cui dirigente le decisioni sono visibili a tutte le parti interessate. Il risultato è un più più alto livello di servizio per le spedizioni critiche e una riduzione misurabile degli articoli erroneamente instradati nel mix di bagagli e merci. atmosfera tra il personale diventa più collaborativo poiché i dati supportano ogni prelievo, imballaggio e passaggio di consegne; i team lavorato per convalidare l'approccio rispetto alle tipiche condizioni dei giorni di punta e ha visto più velocemente acquisto approvazioni e pianificazione del carico più fluida.

Modellazione della competitività del carico con un framework fuzzy multi-criterio all'aeroporto di Istanbul

Raccomandazione: implementare un framework fuzzy multi-criterio per classificare la competitività del carico all'aeroporto di Istanbul utilizzando un scorecard ponderato su cinque criteri: costo di trasporto per chilogrammo, affidabilità, accesso al percorso, produttività del terminal e qualità del servizio. Calibrare le scale fuzzy con input da esperti del settore come wang, ji-feng, thomas, kong e durak per basare il modello su giudizi del mondo reale e dati operativi.

Un'ampia raccolta di dati alimenta il modello: volumi di merci, puntualità, elenchi di percorsi, tasso di occupazione dei terminal, tempi di movimentazione e feedback dei clienti da parte degli spedizionieri. La valutazione segue una visione d'insieme che collega la pianificazione dei trasporti con le operazioni del terminal, i processi di prenotazione e i flussi di lavoro di gestione delle merci all'interno del sistema aeroportuale.

Struttura il framework attorno a una prospettiva chiara che traduce giudizi qualitativi in punteggi quantitativi. Ogni criterio utilizza una scala fuzzy coerente (basso, medio, alto) con funzioni di appartenenza triangolari o trapezoidali, consentendo transizioni fluide tra le bande di prestazioni e riducendo bruschi cambiamenti nella classifica quando i dati cambiano.

• Criteri e pesi
• Efficienza dei costi: costo del trasporto per chilogrammo, costi di accesso e servizi a valore aggiunto;
• Affidabilità: ritiro/consegna puntuali, rispetto dei tempi e resilienza alle interruzioni;
• 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.