{"id":35457,"date":"2021-07-13T16:17:48","date_gmt":"2021-07-13T14:17:48","guid":{"rendered":"https:\/\/www.intellias.com\/?p=35457"},"modified":"2024-07-29T02:51:42","modified_gmt":"2024-07-29T00:51:42","slug":"supply-chain-analytics-adoption-guidance-for-scm-leaders","status":"publish","type":"blog","link":"https:\/\/intellias.com\/supply-chain-analytics\/","title":{"rendered":"Supply Chain Analytics: Adoption Guidance for SCM Leaders"},"content":{"rendered":"
When it comes to reading your supply chain analytics report, do you feel it would be nice to learn just a bit more about the meaning behind all those numbers? Perhaps you have a solid understanding of your historical progress and current operational state, but you cannot precisely gauge what opprortunities or risks are waiting around the corner.<\/p>\n
The truth is that every link and participant in a supply chain generates a wealth of data about the past, present, and future of supply chain operations. However, not all organizations can see, capture, and transform that data into intelligence for decision-making.<\/p>\n
In other words, they lack end-to-end supply chain analytics solutions.<\/p>\n
Supply chain analytics refers to a cohort of methods and technological means companies employ to draw data from connected applications like inventory management, fleet management, shipping, and fulfilment software to obtain summarized intelligence on current and projected supply chain performance.<\/p>\n
Any supply chain is a composite entity with many moving parts, producing and requiring data to ensure effective operations at every leg. A mishap within one link in a supply chain can shake the entire chain, resulting in operational disruptions and unmet customer expectations.<\/p>\n
Traditional and new supply chain analytics solutions stave off chaos by providing a consolidated view of all operations as well as granular insights into individual segment performance.<\/p>\n
Featuring supply chain business intelligence (BI) tools, self-service analytics reports, visual dashboards, and data science models, supply chain analytics software comes in different shapes and sizes.<\/p>\n
But all supply chain analytics solutions can be divided into one of three groups according to the type of analytics provided:<\/p>\n
Each of these types of supply chain analysis tools has its merits and place in modern supply chain management. Yet predictive analytics in supply chain management is undeniably gaining momentum as global leaders continue with supply chain digitization.<\/p>\n
Need a technical framework for supply chain digitization?<\/p>\n
Traditional BI tools are good for processing data that is pre-cleansed, well-structured, and stored in the required format. Typically, such tools are bound to one specific function (e.g. SCM analytics for finance) and limited by a set of standardized reports. For example, you may be able to easily generate a financial compliance report for a local regulator but may not have an option to generate all insights needed for, say, GDPR reporting.<\/p>\n
The scope of traditional business intelligence supply chain solutions ends with descriptive analytics. Most BI software can provide answers to the following questions:<\/p>\n
A newer generation of predictive supply chain solutions has a more extensive reach.<\/p>\n
Connected to data warehouses or data lakes, such solutions leverage big data analytics<\/a> and machine learning (ML)<\/a> to model advanced scenarios spanning the what, why, how, and what\u2019s next. This allows business leaders not only to learn about past and current happenings but also to understand the underlying reasons for them and get a peek into the near future.<\/p>\n In 2018, 78% of organizations<\/a> surveyed by Ventana Research relied on spreadsheets for supply chain planning.<\/p>\n Three years later, supply chain managers rank advanced supply chain analytics as a crucial technology investment, due for short-term adoption:<\/p>\n Source: Gartner \u2014 How to build a strong supply chain analytics strategy<\/a><\/em><\/p>\n Why such an increase in interest?<\/p>\n Because modern supply chains have grown increasingly complex and prone to multiple headwinds and tailwinds coming from every direction.<\/p>\n Consumers expect a stellar omnichannel shopping experience, whereas the business climate encourages businesses to develop multilateral relationships with partners to grow and scale jointly. Not to mention the overall shift to globalized sourcing strategies and mounting regulatory pressure around reducing environmental footprints and improving supply chain sustainability<\/a>.<\/p>\nWhy the need for better supply chain management analytics is dire<\/h2>\n
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