SupplyChain Magazine July 2020 | Page 63

“ Amid the current climate , where the COVID-19 pandemic has caused so much uncertainty within global supply chains , you could argue there has never been a greater need for visibility and synchronisation ”
— Fred Baumann , GVP for Industry Strategy , Blue Yonder
approaches and be open to learn from mistakes , building on each success . Supply chains that derive the best business value ensure that projects are commissioned with clear goals and the associated business questions and challenges they seek to address . Too often such initiatives are based on ‘ looking for opportunity in data ’ when the more value-driven approach is to invert that thinking to ‘ what insight do I need to address my opportunity – with data ’.”
Austin further highlights that while “ much has been made of creating ‘ data lakes ’ from which to draw data to run analytics , organisations should consider the application of data crawler technology that seeks out data in multiple places ( internal and external ) and then presents it for structured use through data layers . Applying machine learning techniques can suggest new associations of disparate sets of data to find even more value .”
“ Thoughtful application of Big Data and analytics will support the increasing focus on supply chain health ,” further explains Austin . “ The technology will equip and empower supply chain professionals ( whether in planning , manufacturing or logistics ) to understand and manage the health of the data that powers the supply chain shifting the focus to more value-added work , as well as increasing capabilities in best-of-breed supply chain systems , supported by self-healing supply chains ( auto data cleansing ) and combined with robotic process automation , and machine learning to make the cognitive supply chain a reality .”
However , “ the primary challenge of Big Data and analytics is that data across the end-to-end supply chain is not owned by any single entity , and data definitions ( the language or format of data ) vary between industries and
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