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Leveraging AI to Optimize End-to-End Supply Chain Management

By Umme Sutarwala - June 05, 2022 3 Mins Read

Leveraging AI to Optimize End-to-End Supply Chain Management

The ever-changing customer expectations and competitive nature of the supply chain sector have driven firms to innovate. It goes without saying that technology, particularly AI is at the heart of this transformation.

The margin for mistakes is increasingly reducing as global supply networks become more complicated. With rising competitiveness in a connected digital world, improving productivity by decreasing all types of risks is more important than ever. The demand for the industry to leverage the power of Artificial Intelligence (AI) in supply chains and logistics is being driven by rising expectations of agility, speed and efficiency among vendors and all business stakeholders.

Here are three ways in which AI applications can generate new ideas and increase operational efficiency in supply chain management processes:

Optimizing delivery logistics and route efficiency

Companies without a good control over delivery and logistics risk losing market, especially in an environment which is highly digitized. Customers today judge their vendors on the speed, agility and quality of services delivered.

Precise inventory management

Efficient inventory management needs to assure that logistics and supply chain do not face any challenges. There are various jobs in this process that have traditionally been manual and time-consuming, and prone to human error. The way forward for these is certainly artificial intelligence and utilization of RPA.

Proper inventory management can all avoid all challenges pertaining to stocks, deliveries and data, specially helping with financial management of inventories throughout the supply chain.

AI-driven inventory management technologies can be highly successful due to their capacity to handle enormous amounts of data. These intelligent algorithms can swiftly evaluate and understand large datasets, offering timely supply and demand forecasting advice.

These AI systems with clever algorithms can also estimate seasonal demand and predict new consumer behaviors. This AI tool helps companies predict future client demand trends while reducing the expense of overstocking undesired goods.

Increased client satisfaction

Big data and artificial intelligence have added untold value to consumer experience. These technologies enable the supply chain to be almost seamless, using order, and delivery data and finding the best logistics match for the process. Modern transportation and logistics, for example, may employ voice-activated tracking systems to track shipments and orders.   

AI insights build on efficiency and profitability to loading operations

Supply chain management necessitates a lot of in-depth research, to identify the most efficient logistics, planning, and transportation data and records.

To provide real-time insight into logistics procedures, companies employ a combination of hardware, software, and data analytics. These findings can be utilized to maximize the cost optimization for deliveries process, and can also assist businesses in developing faster, less dangerous, and more efficient package handling methods.

Also Read: AI Takes Over Supply Chain Management

Competitive advantage

The key to keeping abreast in the supply chain company is to observe market trends and patterns. Artificial intelligence in supply chain analytics may pull real-time data from external sources, including industrial output, environment, and employment history. Firms can better measure market circumstances and estimate prospective needs for sustained growth using all of the acquired data.

Enterprises may also use AI’s sensory abilities to restructure their product portfolio and capital spending. AI in supply chain management is currently preferred for this application.

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Umme Sutarwala

Umme Sutarwala is a Global News Correspondent with OnDot Media. She is a media graduate with 2+ years of experience in content creation and management. Previously, she has worked with MNCs in the E-commerce and Finance domain

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