Novartis taps Amazon know-how for ‘smart’ manufacturing, supply chain –

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        Novartis taps Amazon know-how for ‘smart’ manufacturing, supply chain
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        Novartis has joined forces with Amazon, drawing on the online retail giant’s technological wizardry to reduce bottlenecks and improve the efficiency of its manufacturing and distribution.
        The deal with Amazon Web Services (AWS) is aimed at using the tech company’s cloud-based systems to collect inventory, quality, and production data across its network into central hubs, dubbed Insight Centres, that Novartis says will help it to track production in real time.
        The increase in efficacy is essential in light of a shift in the industry towards the production of personalised therapies – such as Novartis’ CAR-T Kymriah (tisagenlecleucel) for blood cancers – which are more complex to make than conventional drugs.
        For Novartis, partnering with AWS buys into the many years Amazon has spent developing supply chain and automation systems that enable millions of products to be delivered to hundreds of millions of individuals.
        Financial terms of the deal – which comes four years after the two companies first started working together – aren’t being disclosed.
        The deal also follows Novartis’ wide-ranging partnership with Microsoft to develop artificial intelligence tools for use in its R&D operations.
        Novartis’ global head of strategy and operations Amit Nastik said pharma manufacturing as a whole is not as advanced as manufacturing in other industries, and because it is highly regulated “there is a natural hesitancy to try technology that is not fully proven.”
        “The Insight Centres will allow us to look at our whole value chain from the start to the end, from how we source our material, how we do our demand forecasting, how we operate at the shop floor, to how we supply our customers and patients,” he added.
        For instance, Novartis plans to use AWS Internet of Things (IoT) services to develop better visual inspections of manufacturing sites by generating images that can be analysed using computer vision algorithms


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