{"id":18149,"date":"2022-01-20T09:28:18","date_gmt":"2022-01-20T09:28:18","guid":{"rendered":"https:\/\/www.odysseylogistics.com\/?p=18149"},"modified":"2024-01-23T01:09:09","modified_gmt":"2024-01-23T01:09:09","slug":"putting-predictive-analytics-to-work","status":"publish","type":"blog","link":"https:\/\/www.odysseylogistics.com\/blog\/putting-predictive-analytics-to-work\/","title":{"rendered":"Putting predictive analytics to work in your supply chain\u00a0"},"content":{"rendered":"\t\t
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How predictive analytics and advanced technology platforms help companies save money, increase efficiencies and pivot quickly. <\/h6>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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If they didn\u2019t know it already, companies across most industries have learned that well-oiled supply chains are generally driven by accurate data, modern technology platforms and collaborative tools. With the global pandemic continuing to disrupt supply chains worldwide, organizations are increasingly turning to predictive analytics to help them make good decisions, pivot quickly and save money at a time when everyone is trying to do more with fewer resources.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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According to IBM<\/a>, supply chain analytics represents the ability to make data-driven decisions, based on a summary of relevant, trusted data, often using visualization in the form of graphs, charts and other means. This is key because while supply chains typically generate massive amounts of data, the related analytics helps to make sense of all this data, along the way uncovering valuable patterns and generating insights.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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How to Use Predictive Analytics<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Whether they want accurate inventory levels, shorter order lead times, better supply chain visibility, happier customers, or all of the above, organizations can use predictive analytics and advanced technology platforms to achieve these goals (and more). The use of statistics and modeling techniques to make predictions about future outcomes and performance<\/a>, predictive analytics factors in both current and historical data patterns to determine whether those patterns are likely to emerge again.\u00a0<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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With this information in hand, companies can adjust their resource allocation in a way that leverages possible future events while also allowing them to improve operational efficiencies and reduce risk. Looking specifically at the current port congestion and container shortage issues, Transmetrics\u2019 Asparuh Koev says predictive analytics and artificial intelligence (AI) can help minimize the impacts of container contingencies, improve operations and drive overall efficiency.\u00a0<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Tackling Port Congestion with Predictive Analytics<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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With MHI predicting that 62% of supply chain organizations will be using AI by 2026 (up from 17% in 2021), Koev sees data as a key facilitator of what he calls the \u201cnew era of transparency\u201d in the shipping industry.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\u201cBy examining patterns in large amounts of data, predictive analytics provides insights that can lead to better-informed decisions,\u201d Koev writes in AI and predictive analytics: Powerful ways to minimize container contingencies.<\/i><\/a>\u00a0\u201cEven if the demand is growing, analyzing the demand based on seasonality, last year, and historical data provides forecasts.\u201d<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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For example, an AI optimization model accounts for all of the costs that occur during the container lifecycle while data analytics offers visibility into the historical performance, health of current operations, and future costs based on predicted trends\u2014showing whether a container should be express shipped or has time to wait in the port.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\u201cThis way, companies can decide to move empty containers back to high-demand locations in time of need,\u201d Koev points out, \u201cmeaning they can save on storage costs while minimizing empty stock imbalances.\u201d<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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What\u2019s the Most Likely Outcome?<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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One of several types of supply chain analytics, predictive analytics helps companies understand\u00a0the most likely outcome<\/i>\u00a0or future scenario and its business implications. \u201cFor example, by using predictive analytics, you can project and mitigate disruptions and risks,\u201d IBM points out. Other key benefits include reduced operational costs, higher profit margins, a better understanding of potential risks and more accurate planning.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\u201cAs technologies such as AI become more commonplace in supply chain analytics, companies may see an explosion of further benefits,\u201d said IBM, adding that when information is analyzed in real-time, AI can quickly assess, understand and correlate data and offer real-time analysis based on that information. This gives companies broader supply chain intelligence, IBM adds, and allows them to \u201cbe more efficient and avoid disruptions\u2014while supporting new business models.\u201d<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

How predictive analytics and advanced technology platforms help companies save money, increase efficiencies and pivot quickly.\u00a0
\nIf they didn\u2019t know it already, companies across most industries have learned that well-oiled supply chains are generally driven by accurate data, modern technology platforms and collaborative tools. With the global pandemic continuing to disrupt supply chains worldwide, organizations are increasingly turning to predictive analytics to help them make good decisions, pivot quickly and save money at a time when everyone is trying to do more with fewer resources.
\nAccording to IBM, supply chain analytics represents the ability to make data-driven decisions, based on a summary of relevant, trusted data, often using visualization in the form of graphs, charts and other means. This is key because while supply chains typically generate massive amounts of data, the related analytics helps to make sense of all this data, along the way uncovering valuable patterns and generating insights.
\nHow to Use Predictive Analytics
\nWhether they want accurate inventory levels, shorter order lead times, better supply chain visibility, happier customers, or all of the above, organizations can use predictive analytics and advanced technology platforms to achieve these goals (and more). The use of statistics and modeling techniques to make predictions about future outcomes and performance, predictive analytics factors in both current and historical data patterns to determine whether those patterns are likely to emerge again.\u00a0
\nWith this information in hand, companies can adjust their resource allocation in a way that leverages possible future events while also allowing them to improve operational efficiencies and reduce risk. Looking specifically at the current port congestion and container shortage issues, Transmetrics\u2019 Asparuh Koev says predictive analytics and artificial intelligence (AI) can help minimize the impacts of container contingencies, improve operations and drive overall efficiency.\u00a0
\nTackling Port Congestion with Predictive Analytics
\nWith MHI predicting that 62% of supply chain organizations will be using AI by 2026 (up from 17% in 2021), Koev sees data as a key facilitator of what he calls the \u201cnew era of transparency\u201d in the shipping industry.
\n\u201cBy examining patterns in large amounts of data, predictive analytics provides insights that can lead to better-informed decisions,\u201d Koev writes in AI and predictive analytics: Powerful ways to minimize container contingencies. \u201cEven if the demand is growing, analyzing the demand based on seasonality, last year, and historical data provides forecasts.\u201d
\nFor example, an AI optimization model accounts for all of the costs that occur during the container lifecycle while data analytics offers visibility into the historical performance, health of current operations, and future costs based on predicted trends\u2014showing whether a container should be express shipped or has time to wait in the port.
\n\u201cThis way, companies can decide to move empty containers back to high-demand locations in time of need,\u201d Koev points out, \u201cmeaning they can save on storage costs while minimizing empty stock imbalances.\u201d
\nWhat\u2019s the Most Likely Outcome?
\nOne of several types of supply chain analytics, predictive analytics helps companies understand the most likely outcome or future scenario and its business implications. \u201cFor example, by using predictive analytics, you can project and mitigate disruptions and risks,\u201d IBM points out. Other key benefits include reduced operational costs, higher profit margins, a better understanding of potential risks and more accurate planning.
\n\u00a0\u201cAs technologies such as AI become more commonplace in supply chain analytics, companies may see an explosion of further benefits,\u201d said IBM, adding that when information is analyzed in real-time, AI can quickly assess, understand and correlate data and offer real-time analysis based on that information. This gives companies broader supply chain intelligence, IBM adds, and allows them to \u201cbe more efficient and avoid disruptions\u2014while supporting new business models.\u201d<\/p>\n","protected":false},"author":8,"featured_media":22279,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":"","_links_to":"","_links_to_target":""},"blog_category":[74,67],"class_list":["post-18149","blog","type-blog","status-publish","has-post-thumbnail","hentry","blog_category-industry-trends","blog_category-technology"],"acf":[],"yoast_head":"\nPutting Predictive Analytics to Work in Your Supply Chain - Odyssey Logistics<\/title>\n<meta name=\"description\" content=\"How predictive analytics and advanced technology platforms help companies save money, increase efficiencies and pivot quickly.\u00a0\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.odysseylogistics.com\/blog\/putting-predictive-analytics-to-work\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Putting Predictive Analytics to Work in Your Supply Chain - 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