{"id":14592,"date":"2021-08-13T10:00:25","date_gmt":"2021-08-13T08:00:25","guid":{"rendered":"https:\/\/www.tradecloud1.com\/?p=14592"},"modified":"2021-08-13T16:28:50","modified_gmt":"2021-08-13T14:28:50","slug":"ai-case-study-3-kostenbesparende-ai-in-de-productielogistiek","status":"publish","type":"post","link":"https:\/\/www.tradecloud1.com\/nl\/ai-case-study-3-kostenbesparende-ai-in-de-productielogistiek\/","title":{"rendered":"AI case study 3: Kostenbesparende AI in de productielogistiek"},"content":{"rendered":"<div class=\"fusion-fullwidth fullwidth-box fusion-builder-row-1 fusion-flex-container nonhundred-percent-fullwidth non-hundred-percent-height-scrolling\" style=\"--awb-border-radius-top-left:0px;--awb-border-radius-top-right:0px;--awb-border-radius-bottom-right:0px;--awb-border-radius-bottom-left:0px;--awb-padding-right:0px;--awb-padding-right-small:0px;--awb-flex-wrap:wrap;\" ><div class=\"fusion-builder-row fusion-row fusion-flex-align-items-flex-start fusion-flex-content-wrap\" style=\"max-width:1216.8px;margin-left: calc(-4% \/ 2 );margin-right: calc(-4% \/ 2 );\"><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-0 fusion_builder_column_1_1 1_1 fusion-flex-column\" style=\"--awb-bg-size:cover;--awb-width-large:100%;--awb-margin-top-large:0px;--awb-spacing-right-large:1.92%;--awb-margin-bottom-large:20px;--awb-spacing-left-large:1.92%;--awb-width-medium:100%;--awb-order-medium:0;--awb-spacing-right-medium:1.92%;--awb-spacing-left-medium:1.92%;--awb-width-small:100%;--awb-order-small:0;--awb-spacing-right-small:1.92%;--awb-spacing-left-small:1.92%;\"><div class=\"fusion-column-wrapper fusion-column-has-shadow fusion-flex-justify-content-flex-start fusion-content-layout-column\"><div class=\"fusion-title title fusion-title-1 fusion-sep-none fusion-title-text fusion-title-size-two\" style=\"--awb-text-color:#0073bd;--awb-margin-bottom:10px;--awb-margin-top-small:0px;--awb-margin-right-small:0px;--awb-margin-bottom-small:20px;--awb-margin-left-small:0px;--awb-font-size:24px;\"><h2 class=\"fusion-title-heading title-heading-left fusion-responsive-typography-calculated\" style=\"margin:0;font-size:1em;--fontSize:24;line-height:2.08;\"><b><br \/>\nThe Business Problem<br \/>\n<\/b><\/h2><\/div><div class=\"fusion-text fusion-text-1\"><p>In de wereld van logistiek kan het beheer van lege containers niet over het hoofd worden gezien. Boston Consulting Group (BCG) schat dat in de containervaart tot 8% van de bedrijfskosten van een scheepvaartmaatschappij betrekking heeft op het herpositioneren van lege containers &#8211; zonder de bijbehorende kosten van opslag en onderhoud van lege containers (BCG, 2015). Voor het wegvervoer is de situatie niet veel anders: onjuist beheer van de lege vloot kan leiden tot overschotten op het ene gebied en tekorten op het andere, wat leidt tot gederfde inkomsten en hogere kosten.<\/p>\n<p>Bij logistieke bedrijven springen er twee gemeenschappelijke factoren uit die het probleem van het beheer van lege containers nog verergeren:<\/p>\n<ul>\n<li>Het gebruik van Excel voor planning, dit is ineffici\u00ebnt mede doordat dit grotendeels handmatig ingevoerd moet worden (Supply Chain Dive, 2018)<\/li>\n<li>Er is een gebrek aan zicht op de kosten, door het ontbreken van een bedrijfsbreed kosten-overzichtssysteem<\/li>\n<\/ul>\n<\/div><div class=\"fusion-title title fusion-title-2 fusion-sep-none fusion-title-text fusion-title-size-two\" style=\"--awb-text-color:#0073bd;--awb-margin-top:30px;--awb-margin-bottom:10px;--awb-margin-top-small:0px;--awb-margin-right-small:0px;--awb-margin-bottom-small:20px;--awb-margin-left-small:0px;--awb-font-size:24px;\"><h2 class=\"fusion-title-heading title-heading-left fusion-responsive-typography-calculated\" style=\"margin:0;font-size:1em;--fontSize:24;line-height:2.08;\"><b>The Solution<\/b><\/h2><\/div><div class=\"fusion-text fusion-text-2\"><p>NileDutch en TIP Trailer Services zijn twee Nederlandse logistieke spelers die zich tot AI-oplossingen wenden om het beheer van hun lege vloot te verbeteren. Hoewel NileDutch op zee opereert en TIP Trailer Services op land, komen de bedrijven overeen in zowel de totale omvang van de vloot als in de problemen die ongebruikte activa voor hen kunnen cre\u00ebren. Beide bedrijven kunnen het probleem dan ook op dezelfde manier oplossen: door het verbeteren van de forecasts van de ondernemingen, terwijl ze hun lege vloot effici\u00ebnter kunnen herpositioneren. Door gebruik te maken van historische gegevens &#8211; meestal eigen gegevens &#8211; als basis voor algoritmen voor machine learning, konden de transportbedrijven op maat gemaakte oplossingen ontwikkelen om de kosten, in verband met het beheer van de lege vloot, te verlagen.<\/p>\n<\/div><div class=\"fusion-title title fusion-title-3 fusion-sep-none fusion-title-text fusion-title-size-two\" style=\"--awb-text-color:#0073bd;--awb-margin-bottom:0px;--awb-margin-top-small:0px;--awb-margin-right-small:0px;--awb-margin-bottom-small:20px;--awb-margin-left-small:0px;--awb-font-size:18px;\"><h2 class=\"fusion-title-heading title-heading-left fusion-responsive-typography-calculated\" style=\"margin:0;font-size:1em;--fontSize:18;--minFontSize:18;line-height:2.78;\"><b>Voordelen<\/b><\/h2><\/div><div class=\"fusion-text fusion-text-3\"><p>Een op maat gemaakt Artificial Intelligence-systeem kan logistieke bedrijven helpen beter te plannen, wat leidt tot minder opstoppingen en een effici\u00ebnter gebruik van de bedrijfsmiddelen. Dit heeft geleid tot:<\/p>\n<ul>\n<li><strong>Effici\u00ebntere planning:<\/strong> door af te stappen van de tijdrovende handmatige planning met behulp van Excel, zijn de bedrijven in staat om automatische plannen te maken. Deze zijn ook nog nauwkeuriger door de implementatie van AI. Dit vermindert de ophoping, zowel fysiek, bij de herpositionering van activa, maar ook organisatorisch, doordat tijd van werknemers vrijkomt.<\/li>\n<li><strong>Lagere opslagkosten:<\/strong> de door AI aangedreven planningstool kan beter voorspellen wanneer \u00e9n waar bepaalde bedrijfsmiddelen nodig zijn. De containers en trailers van de bedrijven hebben daardoor uiteindelijk minder tijd in opslagfaciliteiten doorgebracht.<\/li>\n<li><strong>Meer transparantie tussen afdelingen:<\/strong> omdat bedrijven nu werken met geconsolideerde informatie die beschikbaar is voor alle kantoren in de wereldwijde toeleveringsketens, kunnen beter ge\u00efnformeerde beslissingen worden genomen door alle medewerkers.<\/li>\n<\/ul>\n<\/div><div class=\"fusion-title title fusion-title-4 fusion-sep-none fusion-title-text fusion-title-size-two\" style=\"--awb-text-color:#0073bd;--awb-margin-top:30px;--awb-margin-bottom:10px;--awb-margin-top-small:0px;--awb-margin-right-small:0px;--awb-margin-bottom-small:20px;--awb-margin-left-small:0px;--awb-font-size:24px;\"><h2 class=\"fusion-title-heading title-heading-left fusion-responsive-typography-calculated\" style=\"margin:0;font-size:1em;--fontSize:24;line-height:2.08;\"><b>Impact<\/b><\/h2><\/div><div class=\"fusion-text fusion-text-4\"><p>In het geval van <strong>NileDutch <\/strong>is de AI-oplossing in staat om <strong>10 tot 12 weken van tevoren<\/strong> een geoptimaliseerd plan voor lege containers te berekenen, waarbij kan worden vastgesteld hoe een overschot aan containers kan worden voorkomen en wat de meest effici\u00ebnte routes zijn om de vloot te herpositioneren. Het bedrijf was ook in staat zijn vloot in te krimpen: de AI-gestuurde planning stelde het bedrijf in staat aan dezelfde vraag te voldoen met minder middelen.<\/p>\n<p>Bij <strong>TIP <\/strong>kan een soortgelijk systeem <strong>zes weken <\/strong><strong>van tevoren een vraagnauwkeurigheid van 95% bieden.<\/strong> Op een tweewekelijkse basis is de nauwkeurigheid tot 98%. Dankzij de geoptimaliseerde en flexibele planning kan het bedrijf nu ook eenrichtingsverhuur aan zijn klanten aanbieden. Met de implementatie van het AI-systeem verwacht het bedrijf <strong>een stijging van de omzet met 11%. <\/strong><\/p>\n<\/div><div class=\"fusion-title title fusion-title-5 fusion-sep-none fusion-title-text fusion-title-size-two\" style=\"--awb-text-color:#0073bd;--awb-margin-top:30px;--awb-margin-bottom:10px;--awb-margin-top-small:0px;--awb-margin-right-small:0px;--awb-margin-bottom-small:20px;--awb-margin-left-small:0px;--awb-font-size:24px;\"><h2 class=\"fusion-title-heading title-heading-left fusion-responsive-typography-calculated\" style=\"margin:0;font-size:1em;--fontSize:24;line-height:2.08;\"><b>Toegankelijkheid en vereisten<\/b><\/h2><\/div><div class=\"fusion-text fusion-text-5\"><p>Om het AI-optimalisatiesysteem te kunnen bouwen en trainen, moeten bedrijven eerst de benodigde gegevens verkrijgen. Hoewel er historische gegevens beschikbaar waren, was er een aanpak in meerdere stappen nodig om de gegevens die vanuit verschillende EDI\u2019s binnenkwamen om te zetten in \u00e9\u00e9n bruikbare set.<\/p>\n<p>Nadat de historische gegevens waren opgeschoond, werden ze ingevoerd in algoritmen voor machine learning om een vraagvoorspellingsmodel te maken, waarmee de bedrijven konden bepalen hoeveel activa op een bepaald moment op een bepaalde plaats nodig zouden zijn.<\/p>\n<p>Beide bedrijven beschikten niet over alle gegevens die nodig waren om dit model te bouwen, echter was dit geen probleem: het model werd verder gecre\u00eberd met gegevens van derde partijen uit de wereldwijde logistiek, zoals havenautoriteiten, opslagpartners of vracht-uitwisselingspartners, om de optimale routes te cre\u00ebren voor de herpositionering en opslag van hun activa. Nile Dutch verzamelde bijvoorbeeld de volgende gegevenspunten:<\/p>\n<ul>\n<li>Opslagkosten<\/li>\n<li>Gate kosten<\/li>\n<li>Reparatiekosten<\/li>\n<li>Herpositioneringskosten<\/li>\n<li>Leveranciers<\/li>\n<li>Landen<\/li>\n<li>Havens<\/li>\n<li>Depots<\/li>\n<li>Veiligheidsvoorraden<\/li>\n<li>En andere: stuwadoorskosten, sorteringskosten, outports, reizen, enz.<\/li>\n<\/ul>\n<p>Al deze informatie samenbrengen en een bruikbaar plan opstellen voor een grote rederij zou een onmogelijke taak zijn met handmatige invoer in Excel. Voor een AI-systeem is het gewoon een kwestie van de gegevens invoeren en de output gebruiken &#8211; om nog maar te zwijgen van het altijd lerende en verbeterende aspect van het systeem dat ontstaat door het verzamelen van meer gegevens naarmate het wordt ingezet en in de loop der jaren.<\/p>\n<\/div><\/div><\/div><\/div><\/div><div class=\"fusion-fullwidth fullwidth-box fusion-builder-row-2 fusion-flex-container nonhundred-percent-fullwidth non-hundred-percent-height-scrolling\" style=\"--awb-border-radius-top-left:0px;--awb-border-radius-top-right:0px;--awb-border-radius-bottom-right:0px;--awb-border-radius-bottom-left:0px;--awb-padding-right-small:42px;--awb-margin-top:40px;--awb-flex-wrap:wrap;\" ><div class=\"fusion-builder-row fusion-row fusion-flex-align-items-flex-start fusion-flex-content-wrap\" style=\"max-width:1216.8px;margin-left: calc(-4% \/ 2 );margin-right: calc(-4% \/ 2 );\"><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-1 fusion_builder_column_1_1 1_1 fusion-flex-column\" style=\"--awb-padding-top:31px;--awb-padding-right:40px;--awb-padding-left:76px;--awb-bg-color:#f7f7f7;--awb-bg-color-hover:#f7f7f7;--awb-bg-size:cover;--awb-border-color:#0071bd;--awb-border-top:0;--awb-border-right:0;--awb-border-bottom:0;--awb-border-left:5px;--awb-border-style:solid;--awb-width-large:100%;--awb-margin-top-large:0px;--awb-spacing-right-large:1.92%;--awb-margin-bottom-large:20px;--awb-spacing-left-large:1.92%;--awb-width-medium:100%;--awb-order-medium:0;--awb-spacing-right-medium:1.92%;--awb-spacing-left-medium:1.92%;--awb-width-small:100%;--awb-order-small:0;--awb-spacing-right-small:1.92%;--awb-spacing-left-small:1.92%;\"><div class=\"fusion-column-wrapper fusion-column-has-shadow fusion-flex-justify-content-flex-start fusion-content-layout-column\"><div class=\"fusion-title title fusion-title-6 fusion-sep-none fusion-title-text fusion-title-size-three\" style=\"--awb-margin-top-small:0px;--awb-margin-right-small:0px;--awb-margin-bottom-small:20px;--awb-margin-left-small:0px;--awb-font-size:22px;\"><h3 class=\"fusion-title-heading title-heading-left fusion-responsive-typography-calculated\" style=\"margin:0;font-size:1em;--fontSize:22;--minFontSize:22;line-height:2.5;\">Want to know more what AI can do for your supply chain?<br \/>\n<a href=\"https:\/\/www.tradecloud1.com\/en\/contact\/\"><span style=\"color: #0073bd;\"><b>Contact us.<\/b><\/span><\/a><\/h3><\/div><\/div><\/div><\/div><\/div><div class=\"fusion-fullwidth fullwidth-box fusion-builder-row-3 fusion-flex-container nonhundred-percent-fullwidth non-hundred-percent-height-scrolling\" style=\"--awb-border-radius-top-left:0px;--awb-border-radius-top-right:0px;--awb-border-radius-bottom-right:0px;--awb-border-radius-bottom-left:0px;--awb-flex-wrap:wrap;\" ><div class=\"fusion-builder-row fusion-row fusion-flex-align-items-flex-start fusion-flex-content-wrap\" style=\"max-width:1216.8px;margin-left: calc(-4% \/ 2 );margin-right: calc(-4% \/ 2 );\"><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-2 fusion_builder_column_1_1 1_1 fusion-flex-column\" style=\"--awb-bg-size:cover;--awb-width-large:100%;--awb-margin-top-large:0px;--awb-spacing-right-large:1.92%;--awb-margin-bottom-large:20px;--awb-spacing-left-large:1.92%;--awb-width-medium:100%;--awb-order-medium:0;--awb-spacing-right-medium:1.92%;--awb-spacing-left-medium:1.92%;--awb-width-small:100%;--awb-order-small:0;--awb-spacing-right-small:1.92%;--awb-spacing-left-small:1.92%;\"><div class=\"fusion-column-wrapper fusion-column-has-shadow fusion-flex-justify-content-flex-start fusion-content-layout-column\"><div class=\"fusion-title title fusion-title-7 fusion-sep-none fusion-title-text fusion-title-size-two\" style=\"--awb-text-color:#0073bd;--awb-margin-top:30px;--awb-margin-top-small:0px;--awb-margin-right-small:0px;--awb-margin-bottom-small:20px;--awb-margin-left-small:0px;--awb-font-size:24px;\"><h2 class=\"fusion-title-heading title-heading-left fusion-responsive-typography-calculated\" style=\"margin:0;font-size:1em;--fontSize:24;line-height:2.08;\"><b>More AI Case Studies:<\/b><\/h2><\/div><div class=\"fusion-recent-posts fusion-recent-posts-1 avada-container layout-default layout-columns-2\"><section class=\"fusion-columns columns fusion-columns-2 columns-2\"><article class=\"post fusion-column column col col-lg-6 col-md-6 col-sm-6\"><div class=\"recent-posts-content\"><span class=\"vcard\" style=\"display: none;\"><span class=\"fn\"><a href=\"https:\/\/www.tradecloud1.com\/nl\/author\/olyyaa-shulgagmail-com\/\" title=\"Berichten van Tradecloud Marketing Team\" rel=\"author\">Tradecloud Marketing Team<\/a><\/span><\/span><span class=\"updated\" style=\"display:none;\">2021-08-13T16:28:50+02:00<\/span><h4 class=\"entry-title\"><a href=\"https:\/\/www.tradecloud1.com\/nl\/ai-case-study-3-kostenbesparende-ai-in-de-productielogistiek\/\">AI case study 3: Kostenbesparende AI in de productielogistiek<\/a><\/h4><p class=\"meta\"><span class=\"vcard\" style=\"display: none;\"><span class=\"fn\"><a href=\"https:\/\/www.tradecloud1.com\/nl\/author\/olyyaa-shulgagmail-com\/\" title=\"Berichten van Tradecloud Marketing Team\" rel=\"author\">Tradecloud Marketing Team<\/a><\/span><\/span><span class=\"updated\" style=\"display:none;\">2021-08-13T16:28:50+02:00<\/span><\/p><p> The Business Problem  In de wereld van logistiek kan het beheer van lege containers niet over het hoofd worden gezien. Boston Consulting Group (BCG) schat dat in de containervaart tot 8% van de [...]<\/p><\/div><\/article><article class=\"post fusion-column column col col-lg-6 col-md-6 col-sm-6\"><div class=\"recent-posts-content\"><span class=\"vcard\" style=\"display: none;\"><span class=\"fn\"><a href=\"https:\/\/www.tradecloud1.com\/nl\/author\/olyyaa-shulgagmail-com\/\" title=\"Berichten van Tradecloud Marketing Team\" rel=\"author\">Tradecloud Marketing Team<\/a><\/span><\/span><span class=\"updated\" style=\"display:none;\">2021-08-13T01:04:28+02:00<\/span><h4 class=\"entry-title\"><a href=\"https:\/\/www.tradecloud1.com\/nl\/ai-case-study-2-efficient-voorraadbeheer-met-behulp-van-artificiele-intelligence\/\">AI case study 2: Effici\u00ebnt voorraadbeheer met behulp van Artifici\u00eble Intelligence<\/a><\/h4><p class=\"meta\"><span class=\"vcard\" style=\"display: none;\"><span class=\"fn\"><a href=\"https:\/\/www.tradecloud1.com\/nl\/author\/olyyaa-shulgagmail-com\/\" title=\"Berichten van Tradecloud Marketing Team\" rel=\"author\">Tradecloud Marketing Team<\/a><\/span><\/span><span class=\"updated\" style=\"display:none;\">2021-08-13T01:04:28+02:00<\/span><\/p><p> The Business Problem Een grote uitdaging voor productiebedrijven is te weten wat, wanneer, waar en hoeveel voorraad moet worden besteld en opgeslagen. MKB-bedrijven berekenen dit traditioneel handmatig met behulp van Excel, Google Sheets of [...]<\/p><\/div><\/article><article class=\"post fusion-column column col col-lg-6 col-md-6 col-sm-6\"><div class=\"recent-posts-content\"><span class=\"vcard\" style=\"display: none;\"><span class=\"fn\"><a href=\"https:\/\/www.tradecloud1.com\/nl\/author\/olyyaa-shulgagmail-com\/\" title=\"Berichten van Tradecloud Marketing Team\" rel=\"author\">Tradecloud Marketing Team<\/a><\/span><\/span><span class=\"updated\" style=\"display:none;\">2021-07-15T19:40:16+02:00<\/span><h4 class=\"entry-title\"><a href=\"https:\/\/www.tradecloud1.com\/nl\/ai-case-study-1-demand-forecasting-using-artificial-intelligence\/\">AI case study 1: Demand Forecasting met behulp van Artificial Intelligence<\/a><\/h4><p class=\"meta\"><span class=\"vcard\" style=\"display: none;\"><span class=\"fn\"><a href=\"https:\/\/www.tradecloud1.com\/nl\/author\/olyyaa-shulgagmail-com\/\" title=\"Berichten van Tradecloud Marketing Team\" rel=\"author\">Tradecloud Marketing Team<\/a><\/span><\/span><span class=\"updated\" style=\"display:none;\">2021-07-15T19:40:16+02:00<\/span><\/p><p> The Business Problem  Een van de grootste uitdagingen voor bedrijfsleiders vandaag de dag is de volatiliteit van de vraag in relatie tot het voorspellen van de vraag. Terwijl de beschikbaarheid van data blijft [...]<\/p><\/div><\/article><\/section><\/div><\/div><\/div><\/div><\/div><div class=\"fusion-fullwidth fullwidth-box fusion-builder-row-4 fusion-flex-container nonhundred-percent-fullwidth non-hundred-percent-height-scrolling\" style=\"--awb-border-radius-top-left:0px;--awb-border-radius-top-right:0px;--awb-border-radius-bottom-right:0px;--awb-border-radius-bottom-left:0px;--awb-padding-top:40px;--awb-flex-wrap:wrap;\" ><div class=\"fusion-builder-row fusion-row fusion-flex-align-items-flex-start fusion-flex-content-wrap\" style=\"max-width:1216.8px;margin-left: calc(-4% \/ 2 );margin-right: calc(-4% \/ 2 );\"><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-3 fusion_builder_column_1_1 1_1 fusion-flex-column\" style=\"--awb-padding-top:42px;--awb-bg-size:cover;--awb-width-large:100%;--awb-margin-top-large:0px;--awb-spacing-right-large:1.92%;--awb-margin-bottom-large:20px;--awb-spacing-left-large:1.92%;--awb-width-medium:100%;--awb-order-medium:0;--awb-spacing-right-medium:1.92%;--awb-spacing-left-medium:1.92%;--awb-width-small:100%;--awb-order-small:0;--awb-spacing-right-small:1.92%;--awb-spacing-left-small:1.92%;\"><div class=\"fusion-column-wrapper fusion-column-has-shadow fusion-flex-justify-content-flex-start fusion-content-layout-column\"><div class=\"fusion-title title fusion-title-8 fusion-sep-none fusion-title-text fusion-title-size-three\" style=\"--awb-text-color:#191919;--awb-margin-top:30px;--awb-margin-bottom:10px;--awb-margin-top-small:0px;--awb-margin-right-small:0px;--awb-margin-bottom-small:20px;--awb-margin-left-small:0px;--awb-font-size:16px;\"><h3 class=\"fusion-title-heading title-heading-left fusion-responsive-typography-calculated\" style=\"margin:0;font-size:1em;--fontSize:16;--minFontSize:16;line-height:2.5;\">Bibliography<\/h3><\/div><div class=\"fusion-text fusion-text-6\"><p style=\"font-size: 12px; line-height: 22px;\" data-fusion-font=\"true\"><a href=\"https:\/\/www.altexsoft.com\/blog\/demand-forecasting-methods-using-machine-learning\/\">Alexsoft. (2019, November 11). Demand Forecasting Methods: Using Machine Learning and Predictive Analytics to See the Future of Sales. Retrieved April 6, 2021, from Alexsoft<\/a><\/p>\n<p style=\"font-size: 12px; line-height: 22px;\" data-fusion-font=\"true\"><a href=\"https:\/\/www.ibm.com\/blogs\/internet-of-things\/iot-cheat-sheet-digital-twin\/\">Armstrong, M. M. (2020, December 4). Cheat sheet: What is Digital Twin? Retrieved April 21, 2021, from ibm.com<\/a><\/p>\n<p style=\"font-size: 12px; line-height: 22px;\" data-fusion-font=\"true\"><a href=\"https:\/\/www.bcg.com\/publications\/2015\/transportation-travel-logistics-think-outside-your-boxes-solving-global-container-repositioning-puzzle\">BCG. (2015, November 17). Think Outside Your Boxes: Solving the Global Container-Repositioning Puzzle . 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Retrieved April 06, 2021, from Capgemini<\/a><\/p>\n<p style=\"font-size: 12px; line-height: 22px;\" data-fusion-font=\"true\"><a href=\"https:\/\/blogs.nvidia.com\/blog\/2009\/12\/16\/whats-the-difference-between-a-cpu-and-a-gpu\/\">Caulfield, B. (2019, December 16). What\u2019s the Difference Between a CPU and a GPU? Retrieved April 22, 2021, from nvidia.com<\/a><\/p>\n<p style=\"font-size: 12px; line-height: 22px;\" data-fusion-font=\"true\"><a href=\"https:\/\/research.aimultiple.com\/demand-forecasting\/#:~:text=unexpected%20demand%20fluctuations.-,AI%20in%20Demand%20Forecasting,decrease%20around%2010%20to%2040%25\">Dilmegani, C. (2021, January 7). Demand forecasting in the age of AI &amp; machine learning [2021]. Retrieved April 6, 2021, from AImultiple<\/a><\/p>\n<p style=\"font-size: 12px; line-height: 22px;\" data-fusion-font=\"true\"><a href=\"https:\/\/news.europawire.eu\/shipment-planning-and-inventory-management-improved-with-ai-and-big-data-on-kuehne-nagels-new-version-of-seaexplorer\/eu-press-release\/2020\/04\/15\/10\/09\/18\/79328\/\">Europawire. (2020, April 15). Shipment planning and inventory management improved with AI and big data on Kuehne + Nagel\u2019s new version of SeaExplorer. Retrieved from Europawire<\/a><\/p>\n<p style=\"font-size: 12px; line-height: 22px;\" data-fusion-font=\"true\"><a href=\"https:\/\/towardsdatascience.com\/detecting-sounds-with-deep-learning-ed9a41909da0\">Hyeongchan, K. (2020, December 16). Detecting Sounds with Deep Learning. Retrieved April 21, 2021, from towardsdatascience.com<\/a><\/p>\n<p style=\"font-size: 12px; line-height: 22px;\" data-fusion-font=\"true\"><a href=\"https:\/\/inoxoft.com\/how-to-improve-inventory-management-using-ai\/\">Kvartalnyi, N. (2021, May 11). 6 TIPS OF HOW TO IMPROVE INVENTORY MANAGEMENT USING ARTIFICIAL INTELLIGENCE. Retrieved from Inoxoft<\/a><\/p>\n<p style=\"font-size: 12px; line-height: 22px;\" data-fusion-font=\"true\"><a href=\"https:\/\/synlabs.io\/704-2\/\">Majumdar, D. (n.d.). Case Study-How SynergyLabs AI solutions Brought Efficiency in warehouse Inventory management. Retrieved April 19, 2021, from Synlabs<\/a><\/p>\n<p style=\"font-size: 12px; line-height: 22px;\" data-fusion-font=\"true\"><a href=\"https:\/\/sg.micron.com\/insight\/micron-uses-data-and-artificial-intelligence-to-see-hear-and-feel\">Micron Technology. (2021). Case Study: Micron Uses Data and Artificial Intelligence to See, Hear and Feel. Retrieved April 21, 2021, from sg.micron.com<\/a><\/p>\n<p style=\"font-size: 12px; line-height: 22px;\" data-fusion-font=\"true\"><a href=\"https:\/\/towardsdatascience.com\/what-is-deep-learning-and-how-does-it-work-2ce44bb692ac\">Opperman, A. (2019, November 19). What is Deep Learning and How does it work? Retrieved April 21, 2021, from towardsdatascience.com<\/a><\/p>\n<p style=\"font-size: 12px; line-height: 22px;\" data-fusion-font=\"true\"><a href=\"https:\/\/www.n-ix.com\/automation-warehouse-inventory-management\/\">Serheichuk, N. (2020, December 15). Inventory management automation: How you can benefit from it. Retrieved from N-ix<\/a><\/p>\n<p style=\"font-size: 12px; line-height: 22px;\" data-fusion-font=\"true\"><a href=\"https:\/\/blogs.gartner.com\/jitendra-subramanyam\/prediction-models-traditional-versus-machine-learning\/#:~:text=In%20traditional%20approaches%2C%20the%20parameter,for%20transforming%20inputs%20into%20outputs\">Subramanyam, J. (2019, July 8). Prediction Models: Traditional versus Machine Learning. Retrieved May 20, 2021, from Gartner.com<\/a><\/p>\n<p style=\"font-size: 12px; line-height: 22px;\" data-fusion-font=\"true\"><a href=\"https:\/\/www.supplychain247.com\/article\/coca_cola_leverages_ai_for_inventory_management\">Supply Chain 247. (2017, March 28). Coca-Cola Leverages AI for Inventory Management. Retrieved from SupplyChain247<\/a><\/p>\n<p style=\"font-size: 12px; line-height: 22px;\" data-fusion-font=\"true\"><a href=\"https:\/\/www.supplychaindive.com\/news\/supply-chain-innovation-survey-BluJay-AdelanteSCM\/530263\/\">Supply Chain Dive. (2018, August 17). Two-thirds of companies consider Excel a supply chain system. Retrieved from Supply Chain Dive<\/a><\/p>\n<p style=\"font-size: 12px; line-height: 22px;\" data-fusion-font=\"true\"><a href=\"https:\/\/www.symphonyretailai.com\/supply-chain\/demand-forecasting-ai\/\">Symphony Retail. (n.d.). demand forecasting ai. Retrieved April 06, 2021, from symphonyretail<\/a><\/p>\n<p style=\"font-size: 12px; line-height: 22px;\" data-fusion-font=\"true\"><a href=\"https:\/\/www.tradegecko.com\/inventory-management\">TradeGecko. (2019, December 4). What is inventory management? Retrieved from tradegecko<\/a><\/p>\n<p style=\"font-size: 12px; line-height: 22px;\" data-fusion-font=\"true\"><a href=\"https:\/\/www.intel.com\/content\/dam\/www\/public\/us\/en\/documents\/best-practices\/faster-more-accurate-defect-classification-using-machine-vision-paper.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">Tuv, E., Murat, G., Enis, P., &amp; Lee, D. H. (2018, November). Faster, More Accurate Defect Classification Using Machine Vision. Retrieved April 21, 2021, from Intel.com<\/a><\/p>\n<\/div><\/div><\/div><\/div><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":10,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[8697],"tags":[8708,8702],"class_list":["post-14592","post","type-post","status-publish","format-standard","hentry","category-ai-nl","tag-ai-case-study-nl","tag-artificial-intelligence-nl"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v22.0 (Yoast SEO v28.2) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Kostenbesparende AI in de productielogistiek &#8211; Tradecloud<\/title>\n<meta name=\"description\" content=\"Case study. 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