AI is intelligence exhibited by machines, or when machines mimic or can replace intelligent human behavior, such as problem-solving or learning.
It can be applied in two ways:
- Automating processes and actions so they can operate without the need for human intervention
- Assisting the human decision-making process in day-to-day operations by reducing errors and identifying bias, especially in data analysis
AI applications in logistics:-
Warehouse logistics and transport operations generate huge volumes of data. To gain full benefit from this data we need to apply analytic tools to gain better insights. Machine learning techniques can be used to streamline and automate processes such as load forecasting and vehicle scheduling. New AI software includes functionality that teaches computers how to provide real-time information from the raw data, on which key decisions then are based.
Supply chain planning
ML can provide the best possible demand scenarios based on intelligent algorithms and machine-to-machine analysis of big data sets, using work tools that run in a continuous loop. This kind of capability could optimize the delivery of goods while balancing supply and demand, and wouldn’t require human analysis except for the setting of parameters.
Supplier management
Supplier risk is a huge concern for global organizations that have decentralised operations and AI can help. Data that is generated from supplier activity such as physical audits, supplier performance assessments and product failures provides an important basis for sourcing decisions. Supplier relationship management (SRM) is still mainly a human activity based on the use of available data, however stale or incomplete. Machine-to-machine automation can provide multiple ‘best supplier scenarios’ based on whatever parameters the user chooses.
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