{"id":34207,"date":"2021-05-20T09:57:43","date_gmt":"2021-05-20T07:57:43","guid":{"rendered":"https:\/\/www.intellias.com\/?p=34207"},"modified":"2024-07-29T12:36:33","modified_gmt":"2024-07-29T10:36:33","slug":"supply-chain-forecasting-and-demand-prediction-framework-for-2021","status":"publish","type":"blog","link":"https:\/\/intellias.com\/supply-chain-forecasting\/","title":{"rendered":"Supply Chain Forecasting and Demand Prediction Framework"},"content":{"rendered":"

What makes for an accurate prediction? Good data and the right methods for modeling it. The supply chain industry has both. What\u2019s often missing is a framework for developing and scaling supply chain forecasting solutions beyond the basic statistical models.<\/p>\n

However, with the commoditization of AI technologies \u2014 machine learning, deep learning, and predictive analytics \u2014 access to the right modeling methods is no longer a constraint. So what\u2019s feasible when it comes to supply chain forecasting? How far can you see and how fast can you go with adoption? Let\u2019s take a close look beyond the buzzwords.<\/p>\n

A technical framework for supply chain forecasting<\/h2>\n

Supply chain management (SCM) produces a wealth of big data you can use to support your decisions. The problem is that decision-critical data is stashed in the wrong places and out of view \u2014 in suppliers\u2019 systems, on-premises databases, and cloud repositories.<\/p>\n

For that reason, forecasting supply chain processes is hardly possible without first sorting out supply chain visibility.<\/p>\n

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Get a detailed view on supply chain visibility with clear reasons to invest into technology innovation<\/p>\n

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Last year, only 9%<\/a> of supply chain managers had visibility into upstream and downstream networks and emphasized data sharing with partners. That\u2019s problematic, as you cannot deploy demand forecasting in supply chain management if your partners don\u2019t share their data on inventory levels, sales volumes, and replenishment plans.<\/p>\n

A solid 60% of SCM leaders plan to improve their levels of data sharing with their ecosystem partners in 2021 and onward:<\/p>\n

Level of data sharing \u2013 current with plans to increase<\/b>\"Supply<\/p>\n

Source: Capgemini \u2014 Fast forward: Rethinking supply chain resilience for a post-COVID-19 world<\/a><\/em><\/p>\n

Data sharing and visibility are in place. What\u2019s next? How do you go from being able to review and interpret data from your partners to exercising predictive demand planning and forecasting in supply chain management?<\/p>\n

In short, you\u2019ll need to learn how to collect the right type of data and then dispatch it securely for predictive analysis.<\/p>\n

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Transportation and logistics development<\/p>\n

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Build a complete ecosystem of technologies around your supply chains for data-driven predictions<\/div>\n <\/div>\n <\/div>\n Learn more<\/span>\n\t\t <\/a><\/div>\n

At Intellias, we\u2019ve worked out the following technical framework for developing supply chain forecasting solutions:<\/p>\n

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  1. Determine and connect the required data sources for analysis<\/li>\n
  2. Create a secure data management and governance framework<\/li>\n
  3. Select the optimal set of demand forecasting methods in the supply chain<\/li>\n
  4. Launch supply chain analytics pilots<\/li>\n
  5. Scale towards adopting intelligent planning<\/li>\n<\/ol>\n

    Why the right data is crucial when implementing forecasting in supply chains<\/h2>\n

    Demand forecasting is a staple of supply chain management. Everyone\u2019s doing it in one way or another to create realistic production plans and inventory turnover projections. Most supply chain managers rely on a mix of historical transactional data and direct customer insights from sources such as surveys. Customer intelligence and other qualitative insights are particularly valuable when you\u2019re planning to launch a new product line or expand in a new market.<\/p>\n

    But the problem with such \u201cself-confessed\u201d data is its accuracy: people are faulty forecasters.<\/p>\n

    The best example of this is New Year\u2019s resolutions. Over 75%<\/a> of people will abandon their resolutions in 30 days, while just 8% will manage to accomplish them. When demand planning in supply chains is based on self-reported data and managers\u2019 judgments alone, the chances of operational mishaps are also high.<\/p>\n

    Take Shoes of Prey, for example \u2014 an Australian-born and globally scaled manufacturer of customizable footwear. Until 2018, the startup was rapidly acquiring customers who were enticed by the ability to design their own pair of shoes. The company had lean operations, didn\u2019t burn too much capital, and had an incredibly high net promoter score. Their audience loved their products and the design experience. Before entering the mass market, Shoes of Prey did a bunch of market surveys to project the demand for customizable shoes and prepare their manufacturing facility to meet shorter lead times.<\/p>\n

    But several months into the mass market expansion, the team learned<\/a> that what their consumers were consciously saying and what they subconsciously wanted were diametrically opposed, and the company ultimately failed.<\/p>\n

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    While there were strong early signs that the sizing and short-run manufacturing markets might work for us, we weren\u2019t able to clearly prove that these customers were willing to pay us enough at a large enough scale to cover our fixed costs.<\/p>\n\t\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\tMichael Fox, <\/span> co-founder of Shoes of Prey, on why the business failed<\/span><\/span>\n\t\t\t\t<\/small>\n\t\t\t<\/blockquote>\n\t\t<\/section>\n

    The takeaway: Qualitative data and market knowledge can contribute to demand prediction. But it\u2019s quantitative data that helps you get a more realistic picture of the actual state of affairs.<\/p>\n

    When customers say they hate the taste of a new beverage but your sales data suggests those awful-tasting drinks are selling like crazy (because of the publicity), you may want to hold off pulling them from the shelves.<\/p>\n

    What are good data sources for supply chain demand planning?<\/h3>\n

    You should collect a combination of:<\/p>\n