Many supply chain gaps have become apparent as a result of the current market volatility and developments in the corporate sector. Supply chains are now more prominent than ever because of remote work, unexpectedly high demand, and logistical complexity. Commodity volatility, shifting demand projections, and supplier-specific difficulties have an impact on a lot of organisations. Companies may optimise their supply chains in ways that simply weren't conceivable in the past by using data analytics. Retailers, suppliers, and manufacturers can increase the supply chain's resilience and efficiency by using big data and predictive analytics in supply chain management.
A supply chain is a fantastic source of data that is generated by customers, the business, and its operations. Businesses get an enormous competitive edge and access to a world of unending possibilities by evaluating and utilising this data.
Production, sourcing, warehousing, inventory management, and logistics all benefit from Industry 4.0's smarter approach to supply chain management. It prompts supply chain managers, manufacturers, and suppliers to reconsider how they plan their networks of suppliers and use cutting-edge technologies.
The enormous amount of data generated by the supply chain can be converted into insightful data that can be used to help identify problems and opportunities, changing the business strategy of the organisation from reactive to proactive. Data and quantitative techniques are used in supply chain analytics to improve decision-making. It is made possible by the emergence of datasets for analytics from the standard, frequently unstructured data held on supply chain management systems and enterprise resource planning systems.
In the era of greater interconnectedness, these insights become extremely important for supply chain risk management. The supply chain is more exposed than ever as a result of the new risks, such as cyberthreats, that appear alongside the conventional ones. Big data can be quite useful in identifying and avoiding these dangers. Additionally, the processing of supply chain data can contribute to improving customer service – it can help better preserve products during transportation and prevent shipment delays due to unforeseen circumstances.
Predictive Analytics can take the guessing out of supply chain management by digging into the future and giving you insight based on certain conditions. It covers:
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Temperature
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Weather
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Social Hazards
The most common social hazards are:
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Economic and social injustice
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Sports and event-related riots
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Politically-motivated civil unrest
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Reaction to police actions
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Natural Disaster Zones
Join us on May 30 and 31, 2023, to become a part of the community and take advantage of one-of-a-kind networking opportunities with senior-level decision-makers who will share and discuss the most effective course of action, strategies, and knowledge regarding the shifting supply chain landscape.You can participate actively in highly insightful discussions with limited seating to exchange ideas and gain knowledge from industry leaders.
To register or learn more about the Forum please check here: https://bit.ly/3DsfWE4
For more information and group participation, contact us: [email protected]
