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Many businesses struggle with demand forecasting. Whether you run a small business or a large enterprise, the challenge of predicting customer behavior and stock levels never gets easier. Even major organizations like Target and Walmart that are able to afford teams of data scientists<\/a> have recently reported struggles with\u00a0 excess inventory<\/a>\u00a0due to poor demand forecasting.<\/p>\n
During this time of global uncertainty, many businesses have adopted a just-in-case mindset. They\u2019ve relied on archaic methods of forecasting, scouring old data and drawing poor conclusions based on past problems.<\/p>\n
But understanding demand accurately shouldn\u2019t be so much of a struggle in 2023. Even as we battle post-pandemic turmoil, we now have clear alternatives to legacy forecasting tools \u2014 thanks to artificial intelligence (AI)<\/a>. And we don\u2019t need endless reams of historical data to access the real-time patterns necessary to accurately forecast demand. In fact, AI-driven<\/a> demand sensing has been shown to reduce inventory errors in supply chain management by up to\u00a0 50%<\/a>, according to McKinsey & Co.<\/p>\n
Why does effective demand forecasting hinge on AI?<\/h2>\n
Today\u2019s forecasting tends to be based on old and inefficient methods, leading to mass misconceptions and inaccuracies. These inaccuracies limit sales forecasts, leading to overcorrections in capacity planning and supply chains that are incorrect from the start.<\/p>\n
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Every company produces data, of course, but it\u2019s almost all trapped in siloes and walled-point solutions that have evolved for specific tasks over many decades. Siloes emerge for noble reasons \u2014 they represent a business\u2019s attempts to organize and become structured. <\/p>\n
Truthfully, siloes are useful in many scenarios, but if the boundaries between them are too sturdy and there\u2019s a lack of effective communication, siloes will negatively impact business, putting more pressure on processes. Inaccuracies are most common in silo-heavy organizations because teams and departments just don\u2019t have enough of a shared language. Rigid siloes also make data, even good data, less credible.\u00a0<\/p>\n
When working with ThroughPut\u2019s clients, I\u2019ve seen AI make all the difference in demand forecasting. That\u2019s because it can pull from disparate datasets, using real-time patterns to sense the <\/a>demand<\/a> around the corner rather than just assuming future demand from past events.<\/p>\n
Using an AI-driven system will pick out time-stamped data \u2014 regardless of barriers \u2014 and rapidly stitch together a global vision of your virtual supply chain network. Supply chain<\/a> AI processes the best signals from the noise that is constantly being generated by your disparate data systems and turns the din into a song you can understand.<\/p>\n
Furthermore, AI is superior at analyzing and making sense of data in vast quantities; yet it also doesn\u2019t need much information to learn. AI trained for real-world applications already intuits which data signals to extract from an ocean of noise, so it can solve needs before they cause problems.<\/p>\n
The quality of data is most important, not the quantity, and delaying the use of AI to sense demand is only going to cause current supply challenges to stagnate and potentially get worse. From there, share prices and shareholders suffer. We are seeing this today across industries: innovation laggards and slow adopters paying the price for relying on old forecasting methods.<\/p>\n
What demand forecasting myths need to be overcome?<\/h2>\n
On a quest for the best accuracy possible, what other myths can we bust in the world of demand forecasting?<\/p>\n
One misconception that proliferates around tired businesses is that demand forecasting can never truly be accurate, making it more trouble than it\u2019s worth. But if you can account for margin of error, use high-quality data and analyze patterns effectively, demand forecasting can be accurate and make tangible differences to the way your supply chain operates.<\/p>\n
Another one of the biggest misconceptions is that a company needs to undergo a lengthy and expensive digital transformation, systems integration, or cloud or data lake<\/a> project, with armies of consultants and data scientists, in order to adopt AI-driven tools and get the kind of results it needs. Although digital transformation might be useful in the long term, businesses have immediate needs for better demand forecasting that they have to address sooner rather than later. Your company already has all the data it needs to solve these problems.<\/p>\n
The bottom line is that improved accuracy in demand planning will result in higher sales and profits. When demand planning is based on old data and poor assumptions, inaccurate results inevitably ensue, leading to ineffective decisions, vague customer service and, ultimately, lost business. AI can turn forecasting into demand sensing: forecasting best-guesses the likely outcomes; AI-driven demand sensing sees the past and the present while zeroing in on what\u2019s most likely to come in the future.<\/p>\n
By applying supply chain AI and predictive replenishment to your existing data, you can realize true demand sensing downstream, access far greater accuracy of the highest-demand SKUs, and ultimately attain higher sales, profits and output \u2014 all in a more sustainable fashion.<\/p>\n
Seth Page is the chief operations officer and head of corporate development at\u00a0<\/em>ThroughPut Inc<\/em><\/a>.<\/em><\/p>\n
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\n Seth Page, ThroughPut Inc.<\/p>\n","protected":false},"excerpt":{"rendered":"
February 16, 2023 1:07 PM Image Credit: chombosan\/Shutterstock Check out all the on-demand sessions from the Intelligent Security Summit here. Many businesses struggle with demand forecasting. Whether you run a small business or a large enterprise, the challenge of predicting customer behavior and stock levels never gets easier. Even major organizations like Target and Walmart<\/p>\n","protected":false},"author":1,"featured_media":608971,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[30323,25135,46],"tags":[],"class_list":{"0":"post-608970","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-accurate","8":"category-demand","9":"category-technology"},"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/posts\/608970","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/comments?post=608970"}],"version-history":[{"count":0,"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/posts\/608970\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/media\/608971"}],"wp:attachment":[{"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/media?parent=608970"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/categories?post=608970"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/newsycanuse.com\/index.php\/wp-json\/wp\/v2\/tags?post=608970"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}

Event<\/h3>\n\nIntelligent Security Summit On-Demand<\/span><\/p>\nLearn the critical role of AI & ML in cybersecurity and industry specific case studies. Watch on-demand sessions today.<\/span><\/p>\n<\/div>\n