[{"data":1,"prerenderedAt":649},["ShallowReactive",2],{"uc-delivery-route-optimization":3,"uc-regulations":429},{"useCase":4,"evidence":186,"blitsAiDeployments":310,"benchmarks":311,"indicative":312,"related":315,"indexability":427,"includeUnpublished":192},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":20,"patterns":22,"channels":24,"audience":28,"autonomy":29,"adoptionStage":30,"segment":31,"problem":32,"problemStats":33,"howItWorks":34,"valueDrivers":35,"kpis":40,"indicativeValue":43,"macroEstimates":78,"feasibility":79,"implementation":93,"risk":138,"blitsAi":165,"faq":167,"related":177,"datePublished":181,"dateModified":181,"lastVerified":181,"changelog":182,"slug":185},"AI route optimization for delivery fleets and trucks","Delivery route optimization","AI route optimization for delivery fleets","AI route optimization plans stops, loads and backhauls to cut miles. ArrowXL reports 13% lower fleet mileage with Descartes; Walmart cites 30 million miles avoided.","published","AI route optimization is software that plans which vehicle serves which stops, in what order and with what load, to cut miles, cost and late deliveries. It takes the day's orders, vehicle capacities, delivery windows and estimated travel times, produces the routes overnight, and in newer versions replans during the day as traffic and orders change.",[12,13,14,15,16],"delivery route planning","last mile route optimization","fleet routing and dispatch optimization","vehicle routing with AI","dynamic route optimization",[18,19],"logistics-and-transportation","retail-and-ecommerce",[21],"operations",[23],"prediction-and-scoring",[25,26,27],"internal-tools","mobile-app","api","employee-facing","copilot","mainstream","road-transport","A delivery fleet has more possible routes than a planner can compare. Every extra mile costs fuel,\ndriver time and vehicle wear, and a route that runs late means missed delivery windows, failed first\nattempts and extra stops the next day. Manual planning also depends on the person who knows the area,\nwhich makes results uneven between depots.\n\nTwo operators on this page describe their starting point. ArrowXL's chief information officer says\nthe company outgrew its previous planning technology because its outputs no longer gave logical,\nvaluable delivery insights to support growth. New Castle Building Products' chief operating officer\nsays route planning was manual and decentralized, which made it difficult to optimize deliveries\nconsistently across locations. At UPS scale the stakes are larger: a UPS executive told investors in\n2021 that every mile saved per driver in a year, across all its US drivers, saves the company more than\nUSD 60 million a year.",[],"1. **Collect the day's work.** Orders, stop locations, time windows, service times, vehicle types,\n   capacities and driver hours come from the order or transport management system.\n2. **Estimate travel and stop times.** The optimizer needs a drive time for each road and hour and a\n   service time for each stop. Many systems take these from mapping data and fixed settings. Amazon\n   says its AI based routing adjusts for conditions such as reduced visibility after sunset and the\n   extra time a downtown delivery needs to find parking at lunchtime. Other teams train a model on\n   the fleet's own telematics, a design choice no operator on this page documents.\n3. **Build the routes.** An optimizer assigns stops to vehicles and orders them to minimize miles and\n   lateness within the constraints. Walmart describes its tool as considering time, location and\n   store delivery windows, packing trailers efficiently and planning backhauls so trailers are not\n   empty on the return trip.\n4. **Review and release.** Planners check exceptions, such as stops the optimizer could not fit, and\n   release the plan to drivers' devices. ArrowXL says its system now plans 95% of routes overnight.\n5. **Replan during the day.** Newer systems, such as UPS's Dynamic ORION, reoptimize a route as\n   conditions change and give the driver turn by turn directions. Amazon says its driver navigation\n   now adjusts routes automatically in bad weather or when hazards are detected.\n6. **Review execution.** Actual drive and stop times, early route terminations and failed\n   deliveries are compared with the plan and used to correct the travel and service time settings.",[36,37,38,39],"cost-to-serve","customer-experience","employee-productivity","risk-reduction",[41,42],"cost-reduction","productivity-gain",{"referenceOrg":44,"inputs":45,"formula":73,"currency":74,"period":75,"resultLabel":76,"caveat":77},"A regional delivery fleet with 250 vehicles",[46,52,59,66],{"key":47,"label":48,"low":49,"high":49,"unit":47,"note":50,"sourceUrl":51},"vehicles","Delivery vehicles",250,"The reference fleet. ArrowXL by JP Home describes its fleet as approximately 250 vehicles.","https:\u002F\u002Fwww.descartes.com\u002Fresources\u002Fknowledge-center\u002Farrowxl-jp-home-optimises-last-mile-delivery-and-unlocks-capacity",{"key":53,"label":54,"low":55,"high":56,"unit":57,"note":58},"milesPerVehicle","Miles driven per vehicle per year",20000,30000,"miles per vehicle per year","Editorial assumption for urban and regional delivery vans and trucks. Replace with your own telematics data.",{"key":60,"label":61,"low":62,"high":63,"unit":64,"note":65},"mileageReduction","Reduction in miles driven",0.03,0.1,"fraction of miles","Editorial assumption, below the approximately 13% fleet mileage reduction that Descartes reports for ArrowXL, which it credits to better planning and less manual work together.",{"key":67,"label":68,"low":69,"high":70,"unit":71,"note":72},"costPerMile","Mileage related cost per mile",0.4,0.9,"USD per mile","Editorial assumption in US dollars for vans and light trucks, covering fuel, tires and maintenance only. Heavy trucks cost more per mile. Replace with your own cost per mile in your own currency.","vehicles * milesPerVehicle * mileageReduction * costPerMile","USD","per year","Mileage related cost avoided","Counts fuel, tire and maintenance cost avoided on the miles removed. It leaves out driver hours saved, more stops per route without more vehicles, fewer failed first attempts, lower emissions and the license, integration and change costs of the routing system.",[],{"complexity":80,"complexityNote":81,"dataPrerequisites":82,"integrations":87},"medium","Routing engines are bought as software or built from solvers. The work for the fleet is clean address and geocode data, accurate service times and vehicle constraints, an order feed from the order or transport management system, and driver devices that return the actual route. Planners also have to accept plans they did not build by hand.",[83,84,85,86],"Geocoded stop addresses with delivery windows and access constraints","Vehicle types, capacities, driver hours rules and depot locations","History of actual drive times and stop durations from telematics or driver devices","Order data with weight, volume and special handling needs",[88,89,90,91,92],"Order management or transport management system","Telematics and driver mobile apps","Mapping and traffic data provider","Customer notification tools for delivery windows","Warehouse or yard system for loading sequence",{"steps":94,"guardrails":113,"humanInTheLoop":118,"kpisToInstrument":119,"failureModes":125},[95,98,101,104,107,110],{"title":96,"detail":97},"Measure the baseline on real routes","Record miles, route duration, stops per route, early route terminations and first attempt delivery rate for a few weeks per depot before changing anything. Without it, the gain cannot be separated from seasonal volume.",{"title":99,"detail":100},"Fix the data the optimizer depends on","Clean addresses and geocodes, set realistic service times by stop type, and record vehicle and driver constraints. An optimizer working from wrong service times produces routes that look efficient and run late.",{"title":102,"detail":103},"Run in parallel before cutover","Plan the same days with the tool and with the current method, compare miles and lateness, and let planners explain the differences. Move one depot at a time.",{"title":105,"detail":106},"Put planners on exceptions","Release plans automatically when they pass checks on lateness, hours and capacity, and send only stops the optimizer could not place to a planner. ArrowXL says its system plans 95% of routes overnight, which leaves a small share for manual work.",{"title":108,"detail":109},"Add same day replanning","Once the overnight plan holds, allow reoptimization for traffic and new orders within limits drivers accept, and keep the driver able to override a sequence.",{"title":111,"detail":112},"Close the loop with execution data","Feed actual drive times, stop times and failed deliveries back into the estimates every week, and review routes drivers repeatedly deviate from.",[114,115,116,117],"Driver hours, rest and vehicle load rules are hard constraints, never soft costs the optimizer can trade away","Drivers can override a sequence for safety or access reasons, and overrides are logged and reviewed","Delivery windows promised to customers are checked before a plan is released","Location and performance data of drivers is used for routing and safety, with a documented purpose and retention period","Planners own exceptions: stops that do not fit, special handling and new depots. Drivers keep the final say on the road. Operations managers review lateness, mileage and driver feedback each week and approve changes to service time and constraint settings.",[120,121,122,123,124],"Miles per route and per stop, against the baseline for the same depot and season","Deliveries per route and per driver hour","Early route terminations and routes finishing late","First attempt delivery success and share of deliveries inside the customer's window","Share of routes released without manual edits",[126,129,132,135],{"title":127,"detail":128},"Wrong service times","Stops take longer than the model assumes, so routes overrun. Measure service time by stop type from driver data and update it often.",{"title":130,"detail":131},"Plans drivers do not follow","Drivers who see a sequence as illogical reorder it, and the gain disappears without anyone noticing. Compare planned and driven routes and talk to drivers about patterns.",{"title":133,"detail":134},"Savings credited to the wrong cause","A mileage drop can come from consolidating depots or lower volume. Compare against a baseline for the same depots and period, and note when manual processes were removed at the same time.",{"title":136,"detail":137},"Constraints left out","Low bridges, vehicle access limits or customer rules missing from the data produce plans that cannot be driven. Collect constraints from drivers and add them to the data.",{"euAiAct":139,"regulations":142,"guidance":148,"controls":159,"incidents":164},{"tier":140,"basis":141},"context-dependent","Planning routes for a fleet is not an Annex III use. It becomes high risk under Annex III point 4(b) when the system allocates tasks to drivers on the basis of individual behaviour or personal traits, or monitors and evaluates their performance and behaviour, for example by assigning harder routes or scoring drivers from telematics and using that for pay or discipline. A system that only optimizes stops, vehicles and loads, with no per driver profiling, is usually minimal risk.",[143,144,145,146,147],"eu-ai-act","gdpr","uk-gdpr","nist-ai-rmf","iso-42001",[149,155],{"title":150,"issuer":151,"region":152,"url":153,"note":154},"Annex III, high risk AI systems referred to in Article 6(2)","European Union","europe","https:\u002F\u002Fartificialintelligenceact.eu\u002Fannex\u002F3\u002F","Point 4(b) covers AI that allocates tasks based on individual behaviour or traits, or monitors and evaluates workers, which is the point to check for driver routing.",{"title":156,"issuer":151,"region":152,"url":157,"note":158},"Regulation (EC) No 561\u002F2006 on driving time and rest periods in road transport","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2006\u002F561\u002Foj","Sets driving time limits and rest rules for drivers of goods vehicles over 3.5 tonnes in the EU, so most last mile vans fall outside it, but a plan for heavier trucks must respect it as a hard constraint. The EUR-Lex page could not be text verified because it answers automated requests with a bot challenge.",[160,161,162,163],"Documented list of constraints the optimizer treats as hard, owned by operations and compliance","Records of manual overrides by planners and drivers with reasons","Data protection assessment for vehicle location and driver performance data","Review of any use of routing or telematics data in driver performance management",[],{"howToBuild":166},"The routing engine comes from a routing platform or the transport management system. Blits.ai adds\nthe conversations and approvals around it. An **AI agent** with a **SQL knowledge base** over planned\nroutes, stop status and exceptions lets dispatchers ask which routes are running late and why, and a\n**knowledge base** with depot rules, access notes and customer delivery policies answers planners\nand drivers consistently. **Custom functions** call the routing system's API to request a replan or\nto read a route.\n\nFor drivers and customers, an agent on **WhatsApp, SMS, web chat and voice** reports a delivery\nwindow, takes an access instruction or reschedules a stop, with **human handover** to dispatch for\nanything outside the agent's configured actions. An **agentic workflow** can propose a replan after a delay\nand wait for **human in the loop** approval before it changes a customer's window, **guardrails**\nand **PII masking** protect addresses and phone numbers, and **monitors** and **test suites** check\nthe agent after every change. The platform is model agnostic with EU and UAE data residency.",[168,171,174],{"question":169,"answer":170},"How many miles can AI route optimization remove?","Reported results vary and depend on the starting point. Descartes says ArrowXL by JP Home reduced fleet mileage by approximately 13%, and the company's chief information officer credits better planning and less manual work together. New Castle Building Products says it takes approximately 25,000 miles out of its routes each year, without a baseline. Walmart says its system avoided 30 million unnecessary miles, and a UPS executive said the original ORION deployment cut 8 miles per driver per day. Plan on the low end of these figures for a first depot, and measure your own baseline.",{"question":172,"answer":173},"Is route optimization really AI?","The public evidence is mixed. Walmart describes its route mapping as AI driven, and Amazon says it is investing in AI based routing technology for its delivery drivers, without describing the models or giving a result. Descartes calls its suite AI powered but its ArrowXL release does not say which planning steps use machine learning, and its New Castle Building Products release calls the routing data driven. UPS describes ORION as a technology and an optimization system, and the 2021 Investor Day transcript does not call it AI. Most of the mileage results on this page come from classic optimization software. Ask a vendor which steps use machine learning, such as travel and service time estimates, and what they add.",{"question":175,"answer":176},"Is AI route planning high risk under the EU AI Act?","Usually not. Optimizing stops, vehicles and loads is not an Annex III use. It can become high risk under Annex III point 4(b) if the system allocates tasks to drivers on the basis of individual behaviour or traits, or monitors and evaluates driver performance. Keep routing separate from driver scoring, or assess the combined system as high risk.",[178,179,180],"freight-dispatch-and-load-matching-agent","parcel-tracking-and-delivery-exception-agent","supply-chain-disruption-monitoring","2026-10-13",[183],{"date":181,"note":184},"First published","delivery-route-optimization",[187,220,245,268,292],{"title":188,"useCases":189,"organization":190,"vendors":196,"summary":199,"stage":200,"year":201,"channels":202,"languages":203,"metrics":205,"outcomeDisclosed":192,"sources":206,"verification":215,"grade":217,"id":218,"organizationSlug":219},"Amazon: AI routing technology for Delivery Service Partner drivers",[185],{"name":191,"anonymized":192,"country":193,"region":194,"industry":19,"wikidata":195},"Amazon",false,"US","global","Q3884",[197],{"name":191,"role":198},"in-house","Amazon's vice president of transportation says in September 2026 that Amazon is investing in AI based routing technology for the drivers of its Delivery Service Partner network. She says the routing adjusts for real world conditions, such as reduced visibility after sunset or the extra time a downtown delivery needs to find parking at lunchtime, and that driver navigation now adjusts routes automatically in bad weather or when hazards are detected. A September 2025 post from the same executive says Amazon uses AI for delivery quality and safety through routing technology that analyzes traffic patterns and road conditions to design safer routes. Neither post gives a mileage, time or cost result for the routing, says how many routes it covers, or describes the models.","production",2026,[25,26],[204],"en",[],[207,211],{"url":208,"title":209,"publisher":191,"date":210},"https:\u002F\u002Fwww.aboutamazon.com\u002Fnews\u002Ftransportation\u002Famazon-dsp-program-investment-2026","Amazon invests another $1.9 billion in the Delivery Service Partner program, launching new safety initiatives and AI-powered technology","2026-09-21",{"url":212,"title":213,"publisher":191,"date":214},"https:\u002F\u002Fwww.aboutamazon.com\u002Fnews\u002Ftransportation\u002Famazon-delivery-service-partner-investment-safety-ai-tools","Amazon invests $1.9B in the Delivery Service Partner program, launching safety initiatives and AI-powered tools","2025-09-29",{"level":216,"checkedAt":181},"source-verified","B","amazon-delivery-service-partner-ai-routing","amazon",{"title":221,"useCases":222,"organization":223,"vendors":227,"summary":230,"stage":231,"year":232,"channels":233,"languages":234,"metrics":235,"outcomeDisclosed":236,"sources":237,"verification":242,"grade":217,"id":243,"organizationSlug":244},"Walmart: AI route optimization for trucks and trailers in its supply chain",[185],{"name":224,"anonymized":192,"country":193,"region":225,"industry":19,"wikidata":226},"Walmart","north-america","Q483551",[228],{"name":229,"role":198},"Walmart Global Tech","Walmart built an AI driven route optimization system for its own trucks and trailers. It maps multi stop journeys against delivery windows, packs trailers, and plans backhauls so trailers return loaded. In March 2024 Walmart said it avoided 94 million pounds of CO2 by removing 30 million unnecessary miles and routing around 110,000 inefficient paths, and that it won the 2023 Franz Edelman Award for deploying the technology at scale. The same release announced that Walmart Commerce Technologies would offer the software to other businesses as a service. The release gives mileage and emissions figures but no cost, time or service level result.","scaled",2024,[25],[204],[],true,[238],{"url":239,"title":240,"publisher":224,"date":241},"https:\u002F\u002Fcorporate.walmart.com\u002Fnews\u002F2024\u002F03\u002F14\u002Fwalmart-commerce-technologies-launches-ai-powered-logistics-product","Walmart Commerce Technologies Launches AI-Powered Logistics Product","2024-03-14",{"level":216,"checkedAt":181},"walmart-route-optimization","walmart",{"title":246,"useCases":247,"organization":248,"vendors":251,"summary":254,"stage":231,"year":255,"channels":256,"languages":257,"metrics":258,"outcomeDisclosed":236,"sources":259,"verification":265,"grade":217,"id":266,"organizationSlug":267},"UPS: ORION and Dynamic ORION route optimization for delivery drivers",[185],{"name":249,"anonymized":192,"country":193,"region":225,"industry":18,"wikidata":250},"United Parcel Service (UPS)","Q155026",[252],{"name":253,"role":198},"UPS","UPS runs ORION (On Road Integrated Optimization and Navigation), its in house route optimization system for delivery drivers. At its June 2021 Investor Day, UPS's chief information and engineering officer described Dynamic ORION, a newer version that reoptimizes a driver's route as conditions change and gives turn by turn directions. He said initial results pointed to a further reduction of 2 to 4 miles per driver per day, in addition to the 8 miles per driver per day from the original ORION deployment. He then said the combined effect amounts to a reduction of more than 130 million miles driven and 10 million gallons of fuel saved each year. Another UPS executive said that every mile saved per driver across all US drivers saves UPS more than USD 60 million a year. UPS does not call ORION AI in this transcript, so this record shows route optimization only.",2021,[25,26],[204],[],[260],{"url":261,"title":262,"publisher":263,"date":264},"https:\u002F\u002Finvestors.ups.com\u002F_assets\u002F_9060b44505c87e8d5c29ab75ad389154\u002Fups\u002Fdb\u002F1082\u002F10498\u002Ffile\u002F2021+UPS+IAD+Final+Transcript(1).pdf","UPS 2021 Investor Day transcript","United Parcel Service","2021-06-09",{"level":216,"checkedAt":181},"ups-orion-delivery-route-optimization",null,{"title":269,"useCases":270,"organization":271,"vendors":274,"summary":278,"stage":200,"year":201,"channels":279,"languages":280,"metrics":281,"outcomeDisclosed":236,"sources":282,"verification":289,"grade":290,"id":291,"organizationSlug":267},"ArrowXL by JP Home: Descartes route planning for two person home delivery",[185],{"name":272,"anonymized":192,"country":273,"region":152,"industry":18},"ArrowXL by JP Home","GB",[275],{"name":276,"role":277},"Descartes Systems Group","platform","ArrowXL by JP Home is a UK home delivery and warehousing specialist that delivers large items such as washing machines and cookers with two person crews from a fleet of about 250 vehicles. Descartes says ArrowXL cut fleet mileage by approximately 13% using the route planning and execution tools in its fleet performance management suite. ArrowXL's chief information officer says the system now plans 95% of routes overnight, that early route terminations fell from 5% to 1%, and that deliveries outside customers' preferred time windows fell by 4%. A separate Descartes case study page lists the same 13% mileage cut alongside a 5.6% drop in average miles per route.",[25,26],[204],[],[283,287],{"url":284,"title":285,"publisher":276,"date":286},"https:\u002F\u002Fwww.descartes.com\u002Fresources\u002Fnews\u002Farrowxl-reduces-fleet-mileage-in-home-delivery-operations-by-13-percent-with-descartes","ArrowXL Reduces Fleet Mileage in Home Delivery Operations by 13% with Descartes","2026-08-11",{"url":51,"title":288,"publisher":276},"ArrowXL by JP Home Optimises Last Mile Delivery and Unlocks Capacity",{"level":216,"checkedAt":181},"C","arrowxl-descartes-route-planning",{"title":293,"useCases":294,"organization":295,"vendors":297,"summary":299,"stage":200,"year":201,"channels":300,"languages":301,"metrics":302,"outcomeDisclosed":236,"sources":303,"verification":308,"grade":290,"id":309,"organizationSlug":267},"New Castle Building Products: Descartes route planning across 24 locations",[185],{"name":296,"anonymized":192,"country":193,"region":225,"industry":18},"New Castle Building Products",[298],{"name":276,"role":277},"New Castle Building Products is a family owned distributor of roofing and exterior building materials with 24 locations in six northeastern US states. Its chief operating officer says route planning used to be manual and decentralized, which made it hard to optimize deliveries consistently across locations. With Descartes' route planning and execution platform the company centralized dispatch and says it has taken approximately 25,000 miles out of its routes each year while improving delivery reliability. Descartes describes the platform as optimizing routes against delivery windows, vehicle capacity and traffic.",[25],[204],[],[304],{"url":305,"title":306,"publisher":276,"date":307},"https:\u002F\u002Fwww.descartes.com\u002Fresources\u002Fnews\u002Fnew-castle-building-products-lowers-fuel-costs-cutting-25000-fleet-miles-annually","New Castle Building Products Lowers Fuel Costs by Cutting 25,000 Fleet Miles Annually with Descartes Solution","2026-06-10",{"level":216,"checkedAt":181},"new-castle-building-products-descartes-routing",0,[],{"low":313,"high":314},60000,675000,[316,344,372,402],{"slug":178,"title":317,"shortTitle":318,"definition":319,"status":9,"industries":320,"functions":321,"patterns":322,"audience":28,"autonomy":326,"adoptionStage":327,"segment":328,"evidenceCount":329,"publicEvidenceCount":329,"organizations":330,"bestGrade":217,"headline":334,"lastVerified":343,"indexable":236},"AI agent for freight dispatch and load matching","Freight dispatch and load matching","An AI freight dispatch and load matching agent coordinates moving a truckload for freight brokers and carriers. It reads an emailed quote request and replies with a price, ranks which loads to show which carriers, matches pickup and delivery details to an open dock appointment slot, and chases exceptions, so staff plan lanes and handle disputes instead of manual quoting and appointment calls.",[18],[21],[323,324,325,23],"agentic-workflow","recommendation-and-personalization","document-processing","supervised-agent","early-adopters","freight-brokerage",3,[331,332,333],"C.H. Robinson","J.B. Hunt Transport Services","Uber Freight",{"kpi":335,"label":336,"unit":337,"n":338,"nUpTo":310,"kind":339,"value":340,"qualifier":341,"claimant":342,"organization":333,"vendorReported":192},"conversion-rate-uplift","Conversion uplift","percent",1,"reported",12,"exact","organization","2026-09-28",{"slug":179,"title":345,"shortTitle":346,"definition":347,"status":9,"industries":348,"functions":349,"patterns":351,"audience":355,"autonomy":326,"adoptionStage":327,"evidenceCount":356,"publicEvidenceCount":357,"organizations":358,"bestGrade":217,"headline":366,"lastVerified":371,"indexable":236},"AI agent for parcel tracking and delivery exceptions","Parcel tracking and delivery exceptions","An AI agent for parcel tracking and delivery exceptions answers \"where is my parcel\" and resolves delivery exceptions for parcel carriers and postal operators. It handles issues such as missed deliveries, redelivery or a change of address or pickup point, delays, customs holds and lost or damaged parcel claims, on chat, messaging and phone, and hands disputes and claims above set limits to a human with the tracking history attached.",[18],[350,21],"customer-service",[352,353,323,354],"conversational-agent","voice-agent","classification-and-routing","customer-facing",8,7,[359,360,361,362,363,364,365],"Aramex","Chronopost","DPD Belgium","DPD Deutschland","DPD UK","Evri","PostNL",{"kpi":367,"label":368,"unit":337,"n":369,"nUpTo":310,"kind":339,"value":370,"qualifier":341,"claimant":342,"organization":360,"vendorReported":192},"containment-rate","Containment rate",2,85,"2026-09-27",{"slug":180,"title":373,"shortTitle":374,"definition":375,"status":9,"industries":376,"functions":379,"patterns":382,"audience":28,"autonomy":385,"adoptionStage":327,"segment":386,"evidenceCount":356,"publicEvidenceCount":356,"organizations":387,"bestGrade":290,"headline":396,"lastVerified":401,"indexable":236},"AI supply chain risk and disruption monitoring","Supply chain risk monitoring","Supply chain disruption monitoring is AI that watches transport lanes, weather, strikes, ports and a manufacturer's own suppliers' suppliers for signs of a coming disruption. It turns scattered news and sensor signals into one validated alert per event, giving planners enough lead time to reroute, expedite or substitute before the disruption reaches production or the customer.",[377,378],"automotive","manufacturing",[380,21,381],"procurement","risk-management",[383,23,384],"anomaly-detection","summarization","assist","supply-chain-risk",[388,389,390,391,392,393,394,395],"Andritz Group","Cisco","DuPont","Google","Medtronic","Schaeffler","Schneider Electric","ZF Friedrichshafen",{"kpi":42,"label":397,"unit":337,"n":338,"nUpTo":310,"kind":339,"value":398,"qualifier":399,"claimant":400,"organization":393,"vendorReported":236},"Productivity gain",50,"at-least","vendor","2026-09-29",{"slug":403,"title":404,"shortTitle":405,"definition":406,"status":9,"industries":407,"functions":408,"patterns":410,"audience":411,"autonomy":326,"adoptionStage":30,"evidenceCount":412,"publicEvidenceCount":412,"organizations":413,"bestGrade":217,"headline":422,"lastVerified":371,"indexable":236},"retail-demand-forecasting-and-replenishment","AI demand forecasting and automated replenishment for retail","Demand forecasting and replenishment","Demand forecasting and replenishment is machine learning that forecasts demand for every item in every store or fulfillment center, day by day, and turns that forecast into automatic orders. It draws on sales history, promotions, prices, weather and local events, and places store and warehouse orders within limits set by planners, who handle the exceptions.",[19],[21,409],"analytics-and-reporting",[23,383],"back-office",9,[414,415,416,417,418,419,420,421,224],"Aeon Retail","Albert Heijn","Best Buy Canada","Dollar General","Morrisons","One Stop","OXXO","Philippine Seven Corporation",{"kpi":423,"label":424,"unit":337,"n":310,"nUpTo":338,"kind":339,"value":425,"qualifier":426,"claimant":342,"organization":414,"vendorReported":192},"forecast-accuracy","Forecast accuracy",40,"up-to",{"indexable":236,"reasons":428},[],[430,435,440,446,452,458,464,471,479,486,493,499,505,512,519,526,532,538,545,550,555,561,567,574,579,584,590,595,601,606,614,620,626,632,638,643],{"id":143,"label":431,"issuer":151,"region":152,"url":432,"description":433,"useCases":434,"indexable":236},"EU AI Act","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1689\u002Foj","Regulation (EU) 2024\u002F1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",275,{"id":144,"label":436,"issuer":151,"region":152,"url":437,"description":438,"useCases":439,"indexable":236},"GDPR","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2016\u002F679\u002Foj","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",246,{"id":147,"label":441,"issuer":442,"region":194,"url":443,"description":444,"useCases":445,"indexable":236},"ISO\u002FIEC 42001","ISO and IEC","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F81230.html","The international management system standard for AI.",143,{"id":146,"label":447,"issuer":448,"region":225,"url":449,"description":450,"useCases":451,"indexable":236},"NIST AI Risk Management Framework","NIST","https:\u002F\u002Fwww.nist.gov\u002Fitl\u002Fai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",110,{"id":145,"label":453,"issuer":454,"region":152,"url":455,"description":456,"useCases":457,"indexable":236},"UK GDPR","Information Commissioner's Office","https:\u002F\u002Fico.org.uk\u002Ffor-organisations\u002Fuk-gdpr-guidance-and-resources\u002F","The UK's version of the GDPR, including rules on solely automated decisions.",81,{"id":459,"label":460,"issuer":151,"region":152,"url":461,"description":462,"useCases":463,"indexable":236},"dora","DORA","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2022\u002F2554\u002Foj","Digital Operational Resilience Act for financial entities: ICT risk, incident reporting and third party risk, including AI providers.",72,{"id":465,"label":466,"issuer":467,"region":152,"url":468,"description":469,"useCases":470,"indexable":236},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","https:\u002F\u002Fwww.fca.org.uk\u002Ffirms\u002Fconsumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",51,{"id":472,"label":473,"issuer":474,"region":475,"url":476,"description":477,"useCases":478,"indexable":236},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","https:\u002F\u002Fwww.mas.gov.sg\u002Fnews\u002Fmedia-releases\u002F2025\u002Fmas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",37,{"id":480,"label":481,"issuer":482,"region":475,"url":483,"description":484,"useCases":485,"indexable":236},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https:\u002F\u002Fwww.apra.gov.au\u002Foperational-risk-management","Australian operational risk standard covering critical operations and material service providers.",26,{"id":487,"label":488,"issuer":489,"region":225,"url":490,"description":491,"useCases":492,"indexable":236},"us-sr-11-7","SR 11-7 model risk management","Federal Reserve and OCC","https:\u002F\u002Fwww.federalreserve.gov\u002Fboarddocs\u002Fsrletters\u002F2011\u002Fsr1107.htm","US supervisory guidance on model risk management, applied by banks to AI and machine learning models.",23,{"id":494,"label":495,"issuer":151,"region":152,"url":496,"description":497,"useCases":498,"indexable":236},"nis2","NIS2 Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2022\u002F2555\u002Foj","Directive (EU) 2022\u002F2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",22,{"id":500,"label":501,"issuer":502,"region":194,"url":503,"description":504,"useCases":498,"indexable":236},"pci-dss","PCI DSS","PCI Security Standards Council","https:\u002F\u002Fwww.pcisecuritystandards.org\u002F","Security standard for any system that stores, processes or transmits cardholder data.",{"id":506,"label":507,"issuer":508,"region":152,"url":509,"description":510,"useCases":511,"indexable":236},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https:\u002F\u002Fwww.gov.uk\u002Fgovernment\u002Fcollections\u002Falgorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",18,{"id":513,"label":514,"issuer":515,"region":225,"url":516,"description":517,"useCases":518,"indexable":236},"hipaa","HIPAA","US Department of Health and Human Services","https:\u002F\u002Fwww.hhs.gov\u002Fhipaa\u002Findex.html","US rules for the privacy and security of protected health information.",17,{"id":520,"label":521,"issuer":522,"region":194,"url":523,"description":524,"useCases":525,"indexable":236},"fatf-recommendations","FATF Recommendations","Financial Action Task Force","https:\u002F\u002Fwww.fatf-gafi.org\u002Fen\u002Fpublications\u002FFatfrecommendations\u002FFatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",15,{"id":527,"label":528,"issuer":151,"region":152,"url":529,"description":530,"useCases":531,"indexable":236},"eu-amlr","EU Anti Money Laundering Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1624\u002Foj","Regulation (EU) 2024\u002F1624: the single EU rulebook for customer due diligence, beneficial ownership and suspicious transaction reporting.",14,{"id":533,"label":534,"issuer":535,"region":225,"url":536,"description":537,"useCases":531,"indexable":236},"us-tcpa","Telephone Consumer Protection Act","Federal Communications Commission","https:\u002F\u002Fwww.fcc.gov\u002Fconsumers\u002Fguides\u002Fstop-unwanted-robocalls-and-texts","US consent rules for automated and prerecorded calls and texts; the FCC has confirmed AI generated voices count as artificial voices.",{"id":539,"label":540,"issuer":541,"region":225,"url":542,"description":543,"useCases":544,"indexable":236},"us-bsa","Bank Secrecy Act","FinCEN","https:\u002F\u002Fwww.fincen.gov\u002Fresources\u002Fstatutes-and-regulations\u002Fbank-secrecy-act","US anti money laundering law: customer due diligence, suspicious activity reports and record keeping.",13,{"id":546,"label":547,"issuer":151,"region":152,"url":548,"description":549,"useCases":544,"indexable":236},"eu-accessibility-act","European Accessibility Act","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2019\u002F882\u002Foj","Directive (EU) 2019\u002F882: accessibility requirements for banking services, ecommerce and other digital services, applicable since June 2025.",{"id":551,"label":552,"issuer":151,"region":152,"url":553,"description":554,"useCases":544,"indexable":236},"solvency-ii","Solvency II","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2009\u002F138\u002Foj","Directive 2009\u002F138\u002FEC: risk based capital, governance and model requirements for insurers.",{"id":556,"label":557,"issuer":558,"region":225,"url":559,"description":560,"useCases":340,"indexable":236},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https:\u002F\u002Fwww.consumerfinance.gov\u002Frules-policy\u002Fregulations\u002F1002\u002F9\u002F","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",{"id":562,"label":563,"issuer":564,"region":194,"url":565,"description":566,"useCases":340,"indexable":236},"telecom-consumer-rules","Telecom consumer protection rules","National telecom regulators","https:\u002F\u002Fwww.berec.europa.eu\u002F","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":568,"label":569,"issuer":570,"region":152,"url":571,"description":572,"useCases":573,"indexable":236},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https:\u002F\u002Fwww.eba.europa.eu\u002Fregulation-and-policy\u002Fcredit-risk\u002Fguidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",11,{"id":575,"label":576,"issuer":151,"region":152,"url":577,"description":578,"useCases":573,"indexable":236},"eecc","European Electronic Communications Code","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2018\u002F1972\u002Foj","Directive (EU) 2018\u002F1972: consumer protection, contract, switching and security rules for telecom operators.",{"id":580,"label":581,"issuer":151,"region":152,"url":582,"description":583,"useCases":573,"indexable":236},"eu-psd2","PSD2","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2015\u002F2366\u002Foj","Payment Services Directive 2: strong customer authentication, transaction risk analysis exemptions and open banking access.",{"id":585,"label":586,"issuer":474,"region":475,"url":587,"description":588,"useCases":589,"indexable":236},"mas-notice-626","MAS Notice 626","https:\u002F\u002Fwww.mas.gov.sg\u002Fregulation\u002Fnotices\u002Fnotice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",10,{"id":591,"label":592,"issuer":151,"region":152,"url":593,"description":594,"useCases":589,"indexable":236},"mifid-ii","MiFID II","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2014\u002F65\u002Foj","Directive 2014\u002F65\u002FEU on markets in financial instruments: suitability and appropriateness of advice, record keeping and product governance.",{"id":596,"label":597,"issuer":598,"region":225,"url":599,"description":600,"useCases":356,"indexable":236},"us-fcra","Fair Credit Reporting Act","Federal Trade Commission","https:\u002F\u002Fwww.ftc.gov\u002Flegal-library\u002Fbrowse\u002Fstatutes\u002Ffair-credit-reporting-act","US rules on consumer reports, their accuracy and permissible use, relevant to credit scoring and screening.",{"id":602,"label":603,"issuer":151,"region":152,"url":604,"description":605,"useCases":357,"indexable":236},"eu-idd","Insurance Distribution Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2016\u002F97\u002Foj","Directive (EU) 2016\u002F97: conduct rules for selling insurance, including demands and needs testing and advice.",{"id":607,"label":608,"issuer":609,"region":610,"url":611,"description":612,"useCases":613,"indexable":236},"cbuae-ai-guidance","CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https:\u002F\u002Fwww.centralbank.ae\u002F","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",5,{"id":615,"label":616,"issuer":151,"region":152,"url":617,"description":618,"useCases":619,"indexable":236},"eu-mortgage-credit-directive","EU Mortgage Credit Directive","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Fdir\u002F2014\u002F17\u002Foj","Directive 2014\u002F17\u002FEU: creditworthiness assessment, disclosure and advice rules for residential mortgage lending.",4,{"id":621,"label":622,"issuer":623,"region":152,"url":624,"description":625,"useCases":619,"indexable":236},"pra-ss1-23","PRA SS1\u002F23 model risk management","Prudential Regulation Authority","https:\u002F\u002Fwww.bankofengland.co.uk\u002Fprudential-regulation\u002Fpublication\u002F2023\u002Fmay\u002Fmodel-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",{"id":627,"label":628,"issuer":629,"region":152,"url":630,"description":631,"useCases":619,"indexable":236},"uk-psr-app-reimbursement","UK APP scam reimbursement rules","Payment Systems Regulator","https:\u002F\u002Fwww.psr.org.uk\u002Four-work\u002Fapp-scams\u002F","Mandatory reimbursement of authorised push payment scam victims by UK payment firms, which shifts scam losses onto banks.",{"id":633,"label":634,"issuer":635,"region":475,"url":636,"description":637,"useCases":329,"indexable":236},"au-scams-prevention-framework","Australian Scams Prevention Framework","Australian Treasury","https:\u002F\u002Ftreasury.gov.au\u002Fconsultation\u002Fc2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",{"id":639,"label":640,"issuer":151,"region":152,"url":641,"description":642,"useCases":329,"indexable":236},"eu-mar","EU Market Abuse Regulation","https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2014\u002F596\u002Foj","Regulation (EU) 596\u002F2014: insider dealing and market manipulation, including the duty to detect and report suspicious orders and transactions.",{"id":644,"label":645,"issuer":646,"region":225,"url":647,"description":648,"useCases":329,"indexable":236},"nyc-local-law-144","NYC Local Law 144","New York City","https:\u002F\u002Fwww.nyc.gov\u002Fsite\u002Fdca\u002Fabout\u002Fautomated-employment-decision-tools.page","Bias audits and notices for automated employment decision tools used in hiring and promotion in New York City.",1791904361026]