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In the post-COVID era, they can be empowered with a high standard of data and analytics sophistication to cope and thrive. By allowing that, they could have a steady demand forecast based on sensing algorithms and react faster to such events. To introduce him better, he has been a supply chain analytics professional for over 14 years.
To date the company has moved 5,000 applications to Microsoft Azure as it applies predictive analytics , AI, robotics, and process automation in many of its business operations. The company is also refining its data analytics operations, and it is deploying advanced manufacturing using IoT devices, as well as AI-enhanced robotics.
Additionally, institutions are finding it difficult to forecast trends, as historical data isn’t relevant anymore. This means that while a few industries like pharma and hygiene-related firms find increased demand, specific industries based on supply chain, travel and tourism, manufacturing, etc. see a direct adverse impact.
The pre-COVID-19 forecasts are no longer kind of valid as the pandemic has entirely disrupted the market. Cheaper cost of raw materials, laborers, and lower duties has allowed most of the city manufacturers to either shift their factories to China or are depending on contracting manufacturers there. Thank you, Suvodip.
Now, those were some of the impacts from a manufacturing perspective, but from a supply-side to be able to supply to retailers, I think retailers are also demanding that products should be fulfilled a lot more faster and frequently. Also, Melita, planning, and forecasting, as I spoke about this earlier, is becoming a huge challenge.
If you’re, say, selling disinfectants or essential goods, you’re wondering how to manufacture and ship these goods to the consumer. In the short run, this means they have to get their demand forecast right. So, let’s say you are a CXO in a retail or consumer goods barring essentials.
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