# Operations Frameworks > Operations management frameworks for the MGT4897 DXB report — the Weighted Factor Model for location and expansion decisions, capacity planning and gap analysis, capacity cushions and bottleneck logic, and quality management approaches (TQM, Six Sigma, SERVQUAL, lean, service recovery) applied to airport operations. Covers which framework answers which task, the primary citation for each, and how to avoid the descriptive-application trap. Use for tasks 4, 5 and 6. - Skill: `faaz17/operations-frameworks` (Agent Skill) - Install (CLI): `npx skillmds@latest add faaz17/operations-frameworks` - Raw SKILL.md: https://api.skillmd.com/api/skills/faaz17/operations-frameworks/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: Faaz17 (https://skillmd.com/u/faaz17) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/faaz17/operations-frameworks --- # Operations frameworks — applied to airport operations Tasks 4, 5 and 6 are **40 of 100 marks**. Each is a framework application. The difference between a Pass and a Distinction on all three is the same difference: does the framework **drive a decision**, or does it just get described? ## The descriptive-application trap | Descriptive (Pass) | Critical (Distinction) | |---|---| | "A weighted factor model assigns weights to factors and scores options." | "Capital cost is weighted at only 0.10 because Dubai Airports has already committed USD 35bn to DWC; the marginal cost of acceleration is small against a sunk programme, so the factor that would dominate a greenfield decision is close to irrelevant here." | | "TQM is a holistic approach to quality." | "DXB's quality problem is variance at peak, not mean performance. TQM's continuous-improvement machinery addresses the mean; SPC on immigration queue times addresses the variance, which is what a 95-million-passenger throughput actually strains." | | "Capacity gap analysis compares demand to capacity." | "On the pre-COVID basis, demand crosses the 115m design capacity in 20xx; on the post-COVID basis it crosses in 20xx. The eight-year spread between those two dates, not the point estimate, is what the DWC transition timetable has to absorb." | **Test every framework paragraph:** if it were deleted, would any recommendation in the report change? If not, cut it and use the words elsewhere. ## Task 4 — the Weighted Factor Model Also called factor rating or weighted scoring. Standard location-decision method in every operations management text. ### Method 1. Identify the factors that matter to **this** decision 2. Assign each a weight; weights sum to 1.00 3. Score each option on each factor against a stated scale — state it (1–10 or 1–5) and say what the endpoints mean 4. Weighted score = Σ (weight × score) for each option 5. Compare, then **discuss critically** ### What the brief requires, exactly - **At least two** of: expand DXB terminals · accelerate the DWC transition · develop a new regional hub in another emirate or GCC location - **A minimum of six factors**, each justified - Weightings justified and *"reflecting Dubai Airports' specific strategic priorities, not generic ones"* - Scores and weighted scores calculated - **Critical discussion** — *"Do not simply report the highest score"* — covering trade-offs, risks, and **how the forecast results shaped the recommendation** ### Making the weights Dubai-specific This is where the 20 marks are won or lost. A generic weighting set is worth little. Tie each weight to a documented Dubai Airports fact: | Factor | Why it might be weighted high or low **here** | |---|---| | Land availability | DXB sits inside built-up Al Garhoud with no room to extend runways; DWC has open desert. This asymmetry is the whole case for DWC and deserves a heavy weight | | Capital cost | Weighted *down*, because the USD 35bn DWC commitment is already announced — the decision is about pace, not whether to spend | | Operational disruption risk | Weighted *up*: DXB is Emirates' single global hub. A botched transition breaks a network, not just an airport | | Transport connectivity | DXB is metro-connected and 5 km from the CBD; DWC is roughly 40 km out. Weighs against acceleration until the Blue Line and road links complete | | Proximity to population centres | Dubai's population growth under the 2040 Urban Master Plan is directed southward, which shifts this factor over the horizon — a factor whose weight is *time-dependent*, worth saying | | Environmental impact | Aviation emissions scrutiny, noise contours over residential Dubai, UAE Net Zero 2050 | | Airline and stakeholder readiness | Emirates and flydubai must move together; a split-hub interim is operationally expensive | | Time to capacity delivery | The forecast says when the gap opens. This factor is where task 3 enters task 4 | | Slot and runway capacity headroom | The binding constraint at DXB is movements, not terminal floor space | Choose six or more, justify each in a sentence, and cite where the justification rests on a fact rather than judgement. **The contextual details in that table are prompts, not sources.** Runway counts, distances, metro connections, master-plan directions and net-zero targets must each be verified against a citable source and logged in `02-Analysis/data-log.md` before they appear in the report. A plausible-sounding geographic fact is still a fabrication if it entered the draft unverified. ### Presenting it | Factor | Weight | Option A score | A weighted | Option B score | B weighted | |---|---|---|---|---|---| Build the table in **Excel** — the brief requires Excel-developed visuals — and reproduce it in the report with a caption and an in-text reference. ### The critical discussion Cover, in order: - **Sensitivity.** Which factor would flip the ranking if its weight moved by 0.05? If the result is robust, say so; if it hangs on one judgement call, say that instead. This single move lifts the section more than any other - **Trade-offs.** What does the winning option give up? - **Risks** of the recommendation, and what would mitigate them - **The forecast link.** Which forecast basis was used, and would the ranking change on a different basis? - **Method limits.** Weighted scoring converts judgement into numbers and can lend spurious precision to what remains a subjective ranking. Naming that is not a weakness — it is the criticality the rubric asks for ## Task 5 — capacity planning and gap analysis ### The anchor numbers - DXB **design capacity: 115 million passengers annually** (given in the brief) - 2025 actual: **95.2 million** — about 83% utilised - DWC target: **260 million** eventually, on a USD 35bn programme ### Method 1. State the **2035 forecast** and the basis it rests on 2. Gap = forecast demand − 115m design capacity. Report it in millions and as a percentage 3. Identify **when** demand crosses 115m, on each forecast basis. The crossing *date range* is more decision-useful than a single 2035 number, and shows better judgement 4. Make **at least three specific operational recommendations** ### Concepts that add rigour - **Design capacity vs effective capacity vs actual output.** Design capacity assumes ideal conditions; effective capacity nets off maintenance, weather, curfews and staffing. Effective capacity at DXB is below 115m, which makes the gap open earlier than the raw number suggests. This distinction is textbook, cheap to include, and frequently missed - **Utilisation** = actual ÷ design capacity. **Efficiency** = actual ÷ effective capacity - **Capacity cushion** = capacity − expected demand. Airports run thin cushions and pay for it in delay propagation. A cushion near zero at a hub is a resilience failure, not an efficiency win - **The bottleneck sets the capacity.** At DXB the binding constraint is likely **runway and slot capacity**, not terminal floor area — two parallel runways, and movements at 454,800 in 2025. Terminal expansion that does not relieve the runway constraint buys nothing. Identifying the true bottleneck is a strong analytical move - **Lead time.** Aviation infrastructure takes 5–10 years. A gap opening in 2032 requires a decision now — which is precisely why the COO is asking ### Making the three recommendations specific Weak: "improve efficiency", "invest in technology". Strong: recommendations that name the constraint, the intervention, the mechanism and a rough magnitude — for example, biometric processing to raise passengers per hour at immigration; schedule smoothing to shift movements off the 03:00–06:00 bank; a phased transfer of a specific carrier group to DWC by a stated date; up-gauging incentives that raise passengers per movement rather than movements themselves. Tie at least one directly to the associative model's passengers-per-movement slope. ## Task 6 — quality management The framing: DXB is operating at **record volumes** while preparing to migrate. Quality under capacity stress is the problem — not quality in the abstract. | Approach | What it contributes here | Primary citation | |---|---|---| | **TQM** | Organisation-wide continuous improvement; customer focus; employee involvement across the many agencies operating at an airport | Deming; Oakland | | **Six Sigma / DMAIC** | Variation reduction in a measurable process — baggage mishandling rate, immigration queue time. Fits airports well because the metrics already exist | Pyzdek and Keller | | **SERVQUAL** | Service quality as the gap between expectation and perception across five dimensions. Widely applied to airport service quality in the literature — a good source of the required journal references | Parasuraman, Zeithaml and Berry (1988) | | **ACI Airport Service Quality (ASQ)** | The industry's own benchmarking programme. Referencing it grounds the section in what Dubai Airports actually measures | Airports Council International | | **Lean** | Waste and queue elimination in passenger flow; the eight wastes map cleanly to terminal processes | Womack and Jones | | **SPC** | Control charts on queue times and turnaround. Directly addresses **variance**, which is what peak-hour operation strains | Montgomery | | **Service recovery** | Disruption is inevitable at 95m passengers; recovery quality drives satisfaction more than incident frequency | Journal of Service Research literature | | **Total productive maintenance** | Baggage systems, jet bridges, escalators — availability is a quality attribute when throughput is at capacity | Nakajima | ### Structuring the section Do not list frameworks. Choose **two or three** and argue why they fit *this* problem: 1. Name the quality problem capacity stress creates — variance, queue time, mishandling, staff load, the risk of degradation during the DWC transition 2. Select approaches against that problem, with reasons 3. Say how it would be measured — ASQ score, baggage mishandling per thousand, immigration queue time percentiles, on-time performance 4. Name the implementation constraint — an airport coordinates dozens of independent operators, so quality strategy is a **governance** problem as much as a technical one. That observation is the kind of insight that reads as genuine understanding ## Citing frameworks properly Cite the **originator**, not a textbook's summary: - Weighted factor / location models — Heizer, Render and Munson; Krajewski *et al.* - Capacity concepts — Slack, Brandon-Jones and Burgess; Stevenson - SERVQUAL — Parasuraman, Zeithaml and Berry (1988), *Journal of Retailing* - TQM — Deming (1986); Oakland - Six Sigma — Pyzdek and Keller - Lean — Womack and Jones Journal articles applying these to airports are plentiful and count toward the required five. See `aviation-data-sources`. ## Before submitting any framework section - [ ] Does the framework change a recommendation? If not, cut it - [ ] Are weights, scores and scales explicit and justified? - [ ] Is the table built in Excel, numbered, captioned and referred to in the text? - [ ] Is there a sensitivity or "what would change this" paragraph? - [ ] Is the method's own limitation acknowledged? - [ ] Does task 4 explicitly reference the task 3 forecast? - [ ] Is every framework claim cited to its originator?