Artificial Intelligence: Making Supply Chain Management Scalable

Artificial intelligence has transformed the way the world used to be and will transform every action in the future when it comes to interaction with machines. However, literally, no industry is left untouched with AI, but its role seems notable in the supply chain industry.

Today, AI allows organizations to streamline the supply chain, increase agility and visibility, control inventory efficiently, and reduce inventory costs. But this technological advancement has grown beyond just to empower supply chain management software. The reality of resource

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.

Enhancing Productivity and Profits

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” ( 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

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 ‘procure it’ would be able to access as a frame of reference, or it’s ‘brains’.

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 (ML) for Supply Chain Planning (SCP)

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.

“A forecasting engine with machine learning, just keeps looking to see which combinations of algorithms and data streams have the most predictive power for the different forecasting hierarchies” ( 2017).ML provides an endless loop of forecasting, which bears a constantly self-improving output. This kind of capabilities could reshape warehouse management as we know today.

Machine Learning for Warehouse Management

Autonomous Vehicles for Logistics and Shipping

Intelligence in logistics and shipping has become a center-stage kind of focus within supply chain management in the recent years. Faster and more accurate shipping reduces lead times and transportation expenses, adds elements of environmental friendly operations, reduces labour costs, and — most important of all — widens the gap between competitors.

Autonomous Vehicles for Logistics and Shipping

If autonomous vehicles were developed to the potential, the impact on logistics optimization would be astronomical. “Where drivers are restricted by law from driving more than 11 hours per day without taking an 8-hour break, a driverless truck can drive nearly 24 hours per day. That means the technology would effectively double the output of the U.S. transportation network at 25 percent of the cost” ( 2016).

Natural Language Processing (NLP) for Data Cleansing and Building Data Robustness

Natural Language Processing (NLP) for Data Cleansing and Building Data Robustness

NLP, applied through the correct work took, could build data sets regarding suppliers, and decipher untapped information, due to language barrier. From a CSR or Sustainability & Governance perspective, NLP technology could streamline auditing and compliance actions previously unable because of existing language barriers between buyer-supplier bodies.

A Road to the Summary

As you can see, laying the proper groundwork for AI pays huge dividends. There’s no doubt that AI offers even greater promise in the future, but, as these results show, there are significant benefits and dramatic results waiting for companies that focus on the fundamentals and put AI to use today.

The beauty of AI-based solutions is that they learn and drive continuous improvement over time. They get more precise and sophisticated as they gather more data and more experience. With the right AI solution in place, you can outpace your competitors today, and be well-positioned for reaping even bigger rewards of AI’s promise tomorrow.

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