News &amp

Audit your current charges now by compiling surcharges on these services and aligning them with the current index to spot discrepancies, alongside Transparenz as the baseline.

In oman and other markets, disclosures are rising, offering a baseline for comparison. These patterns are powered by regulator reports and supplier audits. In 2023–2024, surcharges averaged about 2.3% of the bill, with peaks near 4.8% in energy bundles and telecom surcharges averaging 1.2–3.5%.

To build resilience, deploy a dashboard powered by clean data from suppliers and regulators. Target these metrics: base price, surcharges, taxes, and fees. The data show that, over the same period, combined adjustments often fall within a 2–5% band of the base rate, with quarterly spikes tied to regulatory cycles. This information helps you keep in check unease among customers and plan for change.

Compliance steps include quarterly disclosures, audit trails, and a public digest of pricing factors. Use these actions to align pricing with Transparenz goals and to give customers clearer, consistent explanations for any adjustments; this reduces unease and strengthens trust.

Action checklist: map charges across offerings; verify surcharges against regulator caps; publish disclosures; adjust terms to cap surcharges or offer rate-neutral alternatives. Monitor results monthly, and report outcomes to stakeholders to sustain hope and tangible change.

News & Drewry launches low-sulfur surcharge cost calculator

Run the calculator today to standardise the forecast, powered by live bunkering and fuel price data, and lock in a favorable low-sulfur surcharge for the upcoming contract cycle; theres no guesswork when decisions rely on the index and port-level inputs.

• Impact across the chain: The tool quantifies how a surcharge propagates from bunkering costs to ship operators, freight payers, and customers, clarifying where margins shift.
• Index range: Surcharge indexing sits in the 55-60 range, enabling quick benchmarking against rivals and port tariffs; use this to set target rates and timing for increases.
• Regional relevance: In oman port calls, input port calls and vessel mix to reveal location-specific adjustments and avoid overcharging during lighter bunkering windows.
• Information gaps and lack: Lack of pricing transparency is common; the calculator fills information gaps by aggregating published data and advisor inputs to improve accuracy.
• Intended outcomes: The model standardises how future surcharges are assessed, enabling consistent reporting to stakeholders and clients across worlds markets.
• Beneficial for advisors and operators: Advisors can present clear, comparable figures, helping to secure beneficial contracts and reduce risk exposure.

• Port and contract planning: Use the tool to inventory port calls, bunkering options, and contract terms, then adjust chain logistics to minimize cost increases.
• Increase in transparency: The information produced supports negotiations with suppliers and port authorities, and can be used to justify pricing changes to customers.
• Future-proofing: Regularly refresh the index, monitor worlds markets, and align standardised processes to ensure ongoing consistency as the standard rises.
• 55-60 day horizon: Plan scenarios with a 55-60 day lookahead to anticipate shifts in white-label pricing and contract renewals.
• Oman port example: For ships basing in oman, simulate variations across bunkering windows to optimize schedules and reduce excess spend.

Practical implications for shippers and how to use the calculator

Run the calculator for every lane to identify the biggest exposure and then standardise inputs so the team compares results directly across the chain.

Start with current data: pull the latest survey figures, supply costs, rate cards, and vessel availability. Input timetable, service levels, and needs into the calculator to align exposure metrics across the supply chain and support standardise reporting.

The output highlights the biggest exposure by route and carrier, especially on segments with limited capacity. If theres high exposure on a lane, consider adding capacity from carriers such as drewrys or damas, and verify their scrubbers status to meet emission rules.

To reduce blind spots, combine the calculator results with white data feeds and recent survey insights, then validate against current market trends.

Operational actions include standardising carrier vetting, setting clear KPIs, and scheduling an overhaul of capacity contracts. Use alongside your existing processes and the calculator output to tune service levels and buffers.

Time horizon: plan contingencies for a 55-60 day exposure window, reserving alternative modes or lanes when the calculator flags elevated risk.

Assign Philip from ops to lead ongoing scenario checks, ensure data integrity, and maintain visibility across the chain.

For future use, ensure the calculator aligns with the intended outcomes and standardises data collection. Keep inputs current and run periodic reviews alongside broader analytics.

Share results with stakeholders alongside the survey team, and document lessons learned in the whitepaper so the supply chain can evolve together.

Inputs required: routes, vessel types, and fuel data

Recommendation: build a canonical dataset that records per-voyage details for routes, vessel types, and fuel data, enabling precise cost, emissions, and risk analysis. Fields include route_id, origin_port, destination_port, distance_nm, frequency_per_quarter, vessel_group, deadweight_tons, speed_kn, engine_type, cargo_profile, fuel_type, sulphur_level, fuel_consumption_tpd, fuel_price_usd_per_ton, price_index, bunker_supplier, port_of_bunkering, and date.

Routes: categorize by distance bands (short <3000, medium 3000-7500, long >7500 nm), seasonality windows, port-call constraints, and weather-influenced speed profiles; Vessel types: container, bulk, tanker, LNG-converted, and multipurpose; Fuel data: track low-sulphur vs high-sulphur options, LSFO, VLSFO, MGO, LNG blends, plus bunkering ports and supplier mix. Include supplier brands such as Shell and white-label partners to ensure coverage.

Information from research and the damas group advisors indicates that changes in fuel mixes drive rapid cost shifts; monitor price movements and product availability to quantify impact on the chain and margins. Maintain an index of cross-route performance to identify where one route performs better under a given fuel blend than another.

Analysis and actions: in the last quarter, the price index for low-sulphur fuels rose by over 10 percent, while high-sulphur options remained comparatively flat; map this delta to each route and vessel type to advise changes in fuel mix or routing. Use this data to prepare surge scenarios, update procurement plans, and adjust chartering decisions for cost containment and compliance.

Interpreting the output: understanding surcharge figures per leg

Verwenden Sie a calculator to isolate each surcharge per leg, then cross-check against post-imo change and scrubbers costs to create a clear window into their pricing.

Hope that advisors can align the base prices quer durch port calls, from damas route to destination, and that the full chain of costs is reflected per leg rather than as a single line item.

For each leg, the specific interpretation includes: separate bunker surcharges powered by scrubbers, port dues, and canal charges; compare each line item to the window of data; note the likely deviations and where their estimates differ.

Concerns about lack of transparency should be addressed by mapping each item to a named port and leg; use a consistent template to track the impact per leg over the course of the voyage and across the cargo chain.

Example: Leg 1: origin A to port B – bunker surcharge 18-22% (powered scrubbers), port dues 8-12%, terminal handling 2-4%, canal charges 0-3%; total per leg 28-41%. Leg 2: port B to port C – bunker surcharge 17-21%, port dues 7-11%, scrubber-related charges 1-2%, terminal handling 2-4%; total per leg 26-38%.

Modeling scenarios: price volatility, distance, and service type

Recommendation: Use a single calculator to quantify costs across routes and service types under three scenarios: baseline, volatility spike, and long-distance stretch.

Inputs cover products, cargo characteristics, and route specifics. They draw from a recent information feed including price index data, regulatory changes, and supplier terms to reflect specific conditions that affect costs.

• Products and cargo: weight, volume, handling needs, and whether scrubbers are employed on the vessel.
• Distance and routes: port-pair distance, sea distance, seasonal headwinds, and typical sailing windows.
• Service type: standard, time-definite, or dedicated service; impact on speed, schedule reliability, and logistical flexibility.
• Cost components: freight, fuel, port dues, insurance, handling, demerit and demurrage risks, and terminal charges.
• Regulation and compliance: ballast water, emissions, safety surcharges, and container or cargo-specific rules.
• Data sources: index trends, recent changes in tariffs, fuel curves, and operator terms they use for planning.

Modeling approach details:

1. Define scenarios: baseline, volatility spike (±15–25%), and longer-distance stress (larger ships or heavier cargo).
2. Populate inputs in the calculator with route distance, service type, scrubbers usage, cargo category, and regulation charges.
3. Compute total costs and unit costs per product, plus overhead allocations, for each scenario.
4. Run sensitivity tests: vary fuel prices, exchange rates, and ship utilization to expose potential concerns.
5. Flag areas of unease and concerns–especially for high-value or time-sensitive cargo–to guide risk controls.

Outputs and usage guidance:

• Include per-cargo analysis and a concise executive summary for decision-makers.
• Provide contact points for data updates and regulatory notices to keep the model aligned with changes.
• Prepare larger scenario sets to assess capacity plans and potential market expansion across worlds of shipping.

Operational tips:

1. Maintain a consistent calculator framework to ensure comparable results across routes and service types.
2. Keep information refreshed with recent regulation updates and cost data from trusted sources.
3. Share outputs with teams handling shipping, procurement, and risk management to support informed decisions.

Workflow integration: embedding the tool in sourcing and procurement

Recommendation: deploy a modular API adapter that plugs the tool into the sourcing and procurement process, start an 8-week pilot in two high-spend categories, and establish data standards before scaling.

Recent pilots show tangible outcomes: cycle time down by 15-25%, fewer questions from suppliers, and transparency gains across the window of procurement steps; as they said in reviews, found benchmarks show much benefit from standard data feeds.

Establish dsic data governance: define dsic standards for specific item taxonomy, supplier IDs, currencies; ensure found data is clean; set a window for validation; invite oman suppliers to test with a white list of vendors to accelerate learning.

Engagement: involve their advisors from sourcing and finance; appoint a partner to run the integration; align on bcos ROI and alongside their teams across their worlds.

Implementation steps: map fields, build adapters, test two-way sync, run in parallel with the current process for 2 weeks, then switch to live; ensure their teams understand the changes.

Risks and mitigation: lack of data normalization, misaligned taxonomies, limited adoption; address with training, targeted onboarding, and monthly reviews; use the window for feedback and adjust to maintain needs across oman-based suppliers and their networks.

Data freshness and coverage: when to update inputs and limits

Update inputs on a default 55-60 day cadence and trigger refreshes immediately if any core input changes by more than 4% in a month or if a recent survey indicates material shifts in bunkering and low-sulphur cost dynamics.

The model is powered by multi-source feeds and should focus coverage on three domains: low-sulphur fuel costs, shipping rates, and port-related surcharges. If current data from bcos suppliers and partner fleets shows a likely shift, refresh immediately. Use white papers and concession notices to calibrate base assumptions and to understand evolving pricing pressure across the shipping cycle.

Maintain a larger data pool to reduce noise, but constrain updates with explicit thresholds: when changes exceed 3-5% in a single input or when the 55-60 day window reveals accumulated drift, push a recalibration. This approach helps you understand threat signals without overreacting to short-lived spikes, while preserving cost visibility across their impact on the overall model.