Data Analytics in Amsterdam, Netherlands

Logistics / Supply Chain / Data Analytics / Amsterdam

A Dutch logistics conglomerate (EUR 4.2B revenue, operating across freight forwarding, warehousing, cold chain logistics, and last-mile delivery, with 14,000 employees across the Netherlands, Belgium, Germany, UK, and Nordics, handling 180M shipments annually through 85 warehouse facilities and 12 cross-dock terminals) needed a supply chain intelligence platform to transform its fragmented data landscape into competitive advantage. The company had grown through 8 acquisitions over 15 years, each bringing its own WMS (warehouse management system), TMS (transport management system), and ERP — resulting in 11 operational systems that couldn't communicate with each other. Core challenges: (1) Shipment visibility — the company handling 180M shipments annually across road, rail, sea, air, and inland waterway (the Netherlands' canal network carrying significant freight). End-to-end visibility was impossible: a shipment arriving by container at Rotterdam, transferred to barge to an inland terminal, then trucked to a warehouse for cross-docking before last-mile delivery involved 4-5 different systems, each tracking a different leg. Customers (primarily European retailers and manufacturers) asking "where is my shipment?" received answers that took 30-90 minutes because customer service had to manually check multiple systems and contact multiple operational teams. 23% of customer complaints related to inability to get shipment status — the number one complaint category. For cold chain shipments (perishable foods, flowers, pharmaceuticals — 28% of revenue), visibility gaps were not just customer service issues but regulatory compliance risks. EU food safety regulations requiring temperature monitoring throughout the cold chain — but gaps between systems meant temperature data was incomplete for 34% of cold chain shipments. (2) Warehouse utilisation — 85 warehouses with a combined 2.8M square metres of storage, but utilisation analytics existed only at the individual warehouse level (each running its own WMS). Company-wide utilisation averaged 72% — but this masked enormous variation: some warehouses at 95%+ capacity (causing service failures), while others sat at 45% (destroying profitability). The company couldn't optimally route inventory because it didn't have a unified view of available capacity. Seasonal demand (Dutch retail has pronounced peaks around Sinterklaas/December, Easter, and summer) created periodic capacity crises that were managed through expensive emergency overflow arrangements — temporary warehouse space at 3x normal cost. The cold chain network was particularly inefficient — 18 cold storage facilities operating independently, with no analytics to optimise the temperature-controlled supply chain as a network rather than individual buildings. (3) Transport cost optimisation — the company spending EUR 1.8B annually on transport (own fleet of 2,200 vehicles plus contracted carriers). Route planning was done by local dispatch teams using experience and local knowledge rather than analytical optimisation. Cross-border transport (Netherlands-Belgium, Netherlands-Germany routes comprising 42% of road transport) involved different toll systems, different driving regulations, and different customs requirements post-Brexit for UK shipments — each adding complexity that local dispatchers handled through learned patterns rather than optimised algorithms. Empty running: 28% of kilometres driven empty (industry average: 25%) — each percentage point of empty running representing approximately EUR 5M in wasted fuel, driver time, and vehicle depreciation. Intermodal optimisation: the Netherlands' unique inland waterway network offering 40-60% cost savings over road for certain routes, but the company using barges for only 8% of eligible shipments because the analytical comparison between road, rail, and waterway was never done systematically. (4) Customer profitability blindness — the company knowing total revenue per customer but not true profitability. Cost allocation was done annually through a simplified activity-based costing model that assigned average costs rather than actual costs. Result: the company's top 20 customers (representing 45% of revenue) included 3 customers that were almost certainly unprofitable — but without accurate per-shipment costing, this couldn't be proven. Customer contract negotiations were based on volume commitments and market rates rather than actual cost-to-serve analytics — leaving money on the table with complex customers whose operations required disproportionate resources. (5) Sustainability reporting — the company facing increasing pressure from customers, investors, and regulators to report Scope 1, 2, and 3 carbon emissions. EU CSRD (Corporate Sustainability Reporting Directive) requiring auditable sustainability data from 2025. But emissions data was as fragmented as operational data — own fleet emissions tracked through fuel cards, contracted carrier emissions estimated from industry averages, warehouse energy consumption tracked per-facility without normalisation, and cold chain refrigerant emissions not tracked at all. The company's sustainability report was based on estimates and extrapolations rather than measured data — insufficient for CSRD audit requirements.

We built a supply chain intelligence platform over 14 weeks — creating a unified data layer across the conglomerate's 11 operational systems and providing decision-grade analytics for operations, commercial, and sustainability teams. (1) Unified shipment visibility: end-to-end tracking across all transport modes. Data integration layer: connecting all 11 operational systems (6 different WMS platforms, 3 TMS platforms, 2 ERPs) through a real-time data integration layer — not replacing the systems but extracting and normalising shipment events from each into a unified data model. Each shipment receiving a single tracking identity that persisted across all legs, modes, and systems — the container arriving at Rotterdam, the barge movement to the inland terminal, the warehouse receipt, the cross-dock processing, and the last-mile delivery all visible as one continuous shipment journey. Real-time tracking: GPS data from the own fleet (2,200 vehicles), EDI messages from contracted carriers, port/terminal events from Rotterdam and other ports, and warehouse events from WMS systems — all feeding the unified tracking platform with sub-hourly updates. Customer portal: self-service shipment tracking for the top 200 customers — replacing the 30-90 minute phone-and-email process with instant visibility. API integration for customers who wanted tracking data in their own systems. Cold chain compliance: continuous temperature monitoring unified across all cold chain legs — IoT sensor data from refrigerated trailers, cold storage facilities, and cross-dock areas consolidated into a single temperature history per shipment. Automated alerting when temperature excursions occurred, with corrective action tracking for food safety compliance. GDPR-compliant data handling — tracking focused on shipments and assets, not individuals. (2) Network optimisation: warehouse and transport intelligence. Warehouse capacity intelligence: unified view of capacity across all 85 facilities — real-time utilisation, available space by type (ambient, chilled, frozen, dangerous goods), and demand forecasting by facility. Seasonal demand: predictive models forecasting capacity requirements 6 weeks ahead based on historical patterns, customer forecasts, and market signals — enabling proactive capacity management rather than reactive overflow. Network optimisation: analytical determination of optimal inventory placement across the warehouse network — "this customer's product should be stored in Tilburg rather than Venlo because Tilburg has 30% excess capacity, is closer to the primary delivery zones, and reduces transport cost by EUR 0.08 per unit." Cold chain network: the 18 cold storage facilities optimised as a network — temperature-controlled capacity balanced across the network, with overflow routing that maintained cold chain integrity. Transport optimisation: route optimisation engine incorporating real-time traffic, toll costs, driver hours regulations (EU tachograph rules), cross-border customs requirements, and vehicle capacity — replacing local dispatcher experience with data-driven routing. Intermodal analytics: systematic comparison of road, rail, and inland waterway for every eligible shipment — identifying which shipments should move by barge (lower cost, lower emissions) versus road (faster, more flexible). The analytics identifying 24% of road shipments as barge-eligible (versus the 8% actually using barges). Empty running reduction: network-level matching of delivery runs with pickup opportunities — "this truck delivering to Rotterdam can pick up from a nearby customer for the return journey rather than running empty." (3) Customer profitability analytics: true cost-to-serve intelligence. Activity-based costing: per-shipment costing engine calculating actual cost for each shipment — transport cost (distance, mode, vehicle type), warehouse cost (space, handling, value-added services), customs and compliance cost, and overhead allocation based on actual resource consumption rather than averages. Customer P&L: monthly customer profitability statements showing: revenue by service, actual cost by activity, contribution margin, and profitability trend. Updated with each month-end close rather than annually. The analytics revealing that 3 of the top 20 customers were indeed unprofitable — one losing EUR 4.2M annually due to complex handling requirements, frequent expedited shipments, and below-market contract rates negotiated before acquisition. Contract analytics: data-driven contract negotiation support — "this customer renewal at current rates will lose EUR 1.8M over the contract term. A 12% rate increase on warehousing and a surcharge for expedited shipments would bring the customer to 6% margin, which is below average but acceptable." Pricing optimisation: analytics-driven pricing for new business — accurate cost-to-serve estimates based on shipment characteristics, enabling competitive pricing on profitable business and disciplined pricing on complex business. (4) Sustainability intelligence: measured emissions for CSRD compliance. Scope 1 emissions: own fleet emissions calculated from actual fuel consumption data (fuel cards), vehicle efficiency profiles, and refrigerant usage — accurate to the individual vehicle and individual trip. Scope 2 emissions: warehouse energy consumption normalised by facility size, temperature zone, and utilisation — identifying which facilities were energy-efficient and which needed improvement. The cold chain warehouses consuming 4x energy per square metre versus ambient — making energy efficiency in cold chain the highest-impact sustainability opportunity. Scope 3 emissions: contracted carrier emissions calculated from actual shipment data (distance, vehicle type, load factor) rather than industry averages — providing customer-specific Scope 3 data that major customers increasingly required for their own sustainability reporting. Carbon accounting: CSRD-compliant emissions reporting with audit trail — every emission figure traceable to source data (fuel purchase, energy meter, carrier invoice). Emissions dashboard showing company-wide emissions with drill-down by business unit, customer, route, and transport mode. Reduction analytics: identifying the highest-impact emissions reduction opportunities — modal shift from road to barge (35% emissions reduction per tonne-km), electric vehicle deployment prioritisation (which routes have the right distance profile and charging infrastructure), and warehouse energy efficiency investments (which facilities have the highest energy cost per unit throughput). (5) Executive analytics: decision-grade intelligence for leadership. Operations dashboard: real-time operational performance — shipment volumes, on-time delivery, warehouse utilisation, transport efficiency, and exception management. Drill-down from company-wide to country, to region, to facility, to individual shipment. Commercial dashboard: customer portfolio analytics — revenue, profitability, growth trajectory, and service quality by customer segment. Pipeline analytics showing new business opportunities with profitability forecasts. Financial dashboard: management reporting integrated with operational data — revenue and margin by business unit, customer, and service line with variance analysis. Cash flow analytics incorporating payment terms and collection patterns. Board reporting: quarterly board pack automatically generated with KPIs, trends, and exception commentary — replacing the 2-week manual process of compiling board materials from 11 different systems.

Results over 12 months. Shipment visibility: customer complaint rate reduced 58% — "where is my shipment?" complaints from 23% of all complaints to 8%. Customer self-service portal handling 89% of tracking queries without human intervention. Average tracking query resolution from 45 minutes to instant (self-service) or 3 minutes (customer service with unified view). Cold chain compliance: temperature data completeness from 66% to 98% — eliminating regulatory risk for food safety. Zero cold chain compliance incidents in 12 months (versus 4 in prior year). Warehouse optimisation: company-wide utilisation from 72% to 81% — a 9-percentage-point improvement representing EUR 23M in additional revenue from existing capacity (avoiding EUR 35M in planned new warehouse investment). Seasonal overflow costs reduced 62% through predictive capacity management — from EUR 8.4M to EUR 3.2M annually. Cold chain network optimisation reducing cold storage energy cost 18% through better load balancing. Transport efficiency: empty running from 28% to 21% — a 7-percentage-point improvement saving EUR 35M annually. Intermodal shift: barge usage from 8% to 22% of eligible shipments — saving EUR 18M annually and reducing transport emissions 12%. Route optimisation reducing average fuel consumption 6% across the own fleet — EUR 11M in fuel savings. Customer profitability: the 3 unprofitable top-20 customers addressed — one contract renegotiated (to EUR 1.2M profit from EUR 4.2M loss), one relationship managed to profitability through service simplification, one exited (by mutual agreement after transparent cost sharing). Total profit improvement from customer portfolio optimisation: EUR 8.4M annually. New business pricing accuracy improving win rate on profitable business by 15% — EUR 42M in new profitable revenue. Sustainability: CSRD-compliant emissions reporting achieved 8 months ahead of regulatory deadline — with full audit trail. Scope 1 emissions reduced 8% through fleet optimisation. Scope 3 data provided to 45 major customers for their own sustainability reporting — becoming a competitive differentiator ("we can tell you the exact carbon footprint of your supply chain through our network"). Modal shift contributing 12% transport emissions reduction. Financial impact: warehouse optimisation EUR 23M; transport efficiency EUR 64M; customer profitability EUR 8.4M; sustainability value EUR 4.2M (estimated from customer retention and new business). Total annual impact: EUR 99.6M. Development: EUR 680K. Annual platform: EUR 195K. ROI: 146x first year.