In recent years, the global supply chains have become more complex and pretty challenging to manage. The Covid 19 pandemic has particularly increased market volatility, elevating the need for flexibility and agility. But thanks to artificial intelligence, every sector is redefining its supply chain management. In this article, we will show you how to cut supply chain costs using AI consulting company.

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By definition, AI is the ability of machines to mimic human capabilities like seeing, thinking, sensing, and predicting. [1] The objective is to turn the collected data into information. Then derive insights from the information. Finally, the insights can be implemented to gain a competitive advantage [2].

AI in supply chain

Artificial intelligence has myriad applications and has made headlines by improving processes in different industries, including the supply chain. According to the McKinsey report,[3] a huge chunk of the surveyed companies experienced increased revenues thanks to AI adoption. 44 % of the companies cited cost reductions as a major win from AI implementation. The cost savings are even greater for supply chains. Read on to find out how you can use AI to cut supply chain costs.

Process streamlining

Artificial intelligence is effective in streamlining supply chain management processes. In a manufacturing setting, for example, different machines produce massive amounts of data, which may be difficult for a human to collect, interpret, and draw insights from. Artificial intelligence can gather this data and draw insights from it. This includes when machines are faulty or which processes are time-consuming and need upgrading.

suppy chain management

Production scheduling is a great example of how artificial intelligence is used in a manufacturing setting to streamline processes. Scheduling which components need to be manufactured is a difficult task and requires plenty of information to arrive at such decisions. As a scheduler, you will need to consider a host of data inputs, such as:
• Inventory levels
• Demand for a variety of different components
• The most opportune time to make deliveries
• How to pick from line downtime

But with artificial intelligence in charge of the scheduling process, the system implements the manufacturing sequence according to pre-set instructions. Thus, managers don’t have to answer questions such as, “What do I work on next?”[4]

It might be interestinfg for you: Scenario-based supply chain optimization with AI

Forecast demand

Companies always seek to balance supply and demand. To do so, they need better forecasting in their supply chain and management. Artificial intelligence plays a major role in projection and forecasting. It can automatically process, assess, and predict data, and ultimately provide an accurate and consistent forecast of demand.

Forecast demand with AI

This, in turn, allows organizations to optimize their sourcing with respect to purchases and order processing. Thus, they can save on expenses associated with warehousing, transportation, and supply chain administration.

Artificial intelligence also identifies trends and patterns that can be used to design effective retail and manufacturing techniques. For instance, businesses can use artificial intelligence to stock only a specific number of products they intend to sell, reducing wastage. Access to accurate sales trends also allows companies to place more orders for soon-to-be-popular products.

Read more about 10 Use Cases of AI and Machine Learning in Logistics and Supply Chain

Mitigate supply risk

Many companies struggle with how to manage supply risks. What they don’t seem to understand is that the secret lies in predicting and accommodating known and unknown variables such as pandemics and fluctuating trade relationships.

Supply risk

Take the automotive industry example, where a typical vehicle has between 15,000 and 25,000 component parts. So, if these components are sourced from different parts of the world, then there’s a high likelihood of challenges within the supply chain management. These challenges could include:
• Customs
• Labor shortage
• International weather
• Political differences
• Long lead times

But with AI, it’s easy to evade such supply risks. By use of systems such as Watson[5], companies can monitor data sources like social media and weather trends to track the risk factors in a supply chain. This enables them to identify which fields of acting upon first and eventually streamline and cut costs on the entire supply chain.

Quality Control

In a manufacturing process, artificial intelligence technologies can learn the attributes of a “defective” part. They can then detect these defective parts through camera monitoring. And finally, eliminate them from the production line. [6] This quality control process can save the supplier huge chunks of money over time, as the defective parts are eliminated and rectified if possible.

quality control

Final thoughts on artificial intelligence in supply chain management

Artificial intelligence is reinventing the supply chain management. It’s helping organizations cut supply chain costs through a series of processes. These include process streamlining, production scheduling, mitigating supply risk, and improving demand forecasting and quality control.
Despite the benefits, companies can expect to encounter a few hurdles before realizing the full potential of artificial intelligence. The first challenge is to inspire the confidence and trust of the supply chain stakeholders and decision-makers. Other obstacles to AI implementation in supply chain operations include:
• The protracted implementation processes
•High cost
• A lack of computing power
• A scarcity of data scientists
• Security challenges

Nevertheless, the benefits already witnessed point toward an efficient and cost-effective supply chain. The sooner you adopt this powerful technology in your company’s supply chain, the sooner you start enjoying the rewards.


[1] URL: Accessed April 21, 2022
[2] Carly Fiorina (2004), “Information: the currency of the digital age,”. URL: Accessed April 21, 2022
[3] Featured Insights: AI Proves its Worth But Few Scale Impact. URL: Accessed April 21, 2022
[4] Green, Peter. “Artificial Intelligence for Real-Time Manufacturing Execution and Operations Management.”. Processing Magazine. URL: Accessed April 23, 2022
[5]Ibm. com. URL: Accessed April 22, 2022
[6] Savidge, Alexandra. “AI in Supply Chain: 6 Reasons Executives Should Invest in AI in 2018.” Digital Authority. URL: strategy-24-04-2018., Accessed April 23, 2022

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