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AI Technologies Enhancing Supply Chain Management

AI Technologies Enhancing Supply Chain Management - SIPMM
AI Technologies Enhancing Supply Chain Management
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Artificial intelligence (AI), which supports the delivery of timely and accurate information, tops the list of promising technology areas for the supply chain. Technology innovation allows organization to streamline the supply chain, increase agility and visibility, control inventory more efficiently, and reduce inventory costs.

Organizations need to be concerned with how to get helpful information into the supply chain. Technology enhancement has grown beyond just supply chain management software. The reality of resource limitations, though, mean that, to win, electronics OEMs need to invest in the most helpful technologies, picking and choosing the ones that offer the best business advantage. That’s a challenging task in a marketplace that is increasingly rife with choices. In the end, all of these increase customer satisfaction and boost brand loyalty.

Supply chain area
Picture extracted from Rahul Chatterjee (2016)

Enhancing Productivity and Profits

The Understanding these two categories of AI capacities is important for future implementation of AI into business work tools. Imagine if a business could automate such tasks that are (more or less) ‘wasting time’.

“Businesses estimate they spend on average per week around 55 hours doing manual, paper-based processes and checks; 39 hours chasing invoice exceptions, discrepancies and errors and 23 hours responding to supplier inquiries” (mhlnews.com 2017). Previous studies, by the Tungsten Network, have suggested that valuable time and money is wasted on trivial supply chain related-tasks that are conducted operationally by humans.

Enhancing Productivity and Profits
Picture extracted from Kate Patrick (2018)

This loss has been equated to around 6500 hours, during the work year, that businesses are throwing away by processing papers, fixing purchase orders and replying to suppliers. Companies, even at that enterprise level, have already begun the implementation of AI tech into everyday supply chain tasks.

In particular, the application of AI into Supply Chain related-tasks holds high potential for boosting top-line and bottom-line value. Tech vendors such as IBM, Google, and Amazon have released products that utilize artificial intelligence.

How can AI enhance the SCM?

1. Chatbots for Operational Procurement
2. Machine Learning (ML) for Supply Chain Planning (SCP)
3. Machine Learning for Warehouse Management
4. Autonomous Vehicles for Logistics and Shipping
5. Natural Language Processing (NLP) for Data Cleansing and Building Data Robustness

Chatbots for Operational Procurement

Streamlining procurement related tasks through the automation and augmentation of Chabot capability requires access to robust and intelligent data sets, in which, the ‘procurebot’ would be able to access as a frame of reference, or it’s ‘brains’.

Chatbots for Operational Procurement
Picture extracted from Kodiak Community (2017)

As for daily tasks, chatbots could be utilized to:

• Speak to suppliers during trivial conversations.
• Set and send actions to suppliers regarding governance and compliance materials.
• Place purchasing requests.
• Research and answer internal questions regarding procurement functionalities or a supplier/supplier set.
• Receiving/filing/documentation of invoices and payments/order requests.

Machine Learning (ML) for Supply Chain Planning (SCP)

Supply chain planning is a crucial activity within SCM strategy. Having intelligent work tools for building concrete plans is a must in today’s business world.

ML, applied within SCP could help with forecasting within inventory, demand and supply. If applied correctly through SCM work tools, ML could revolutionize the agility and optimization of supply chain decision-making.

By utilizing ML technology, SCM professionals would be giving best possible scenarios based upon intelligent algorithms and machine-to-machine analysis of big data sets. This kind of capability could optimize the delivery of goods while balancing supply and demand, and wouldn’t require human analysis, but rather action setting for parameters of success.

Machine Learning for Supply Chain Planning
Picture extracted from Cristina Conti (2017)

Machine Learning for Warehouse Management

Taking a closer look at the domain of SCP, its success is heavily reliant on proper warehouse and inventory-based management. Regardless of demand forecasting, supply flaws (overstocking or under-stocking) can be a disaster for just about any consumer-based company/retailer.

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