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April 26, 2022

Benefits of Artificial Intelligence in Supply Chain Management: optimization and cost reduction

Author:




Edwin Lisowski

CSO & Co-Founder


Reading time:




8 minutes


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.

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-driven solutions employ technologies like natural language processing (NLP) and machine learning (ML) to understand and evaluate data at incredible scale and speed. They can correlate the data and dig out important insights, thus supplementing the brainpower of the operator.

AI in supply chain

Artificial intelligence has myriad applications and has made headlines by improving processes in different industries, including the supply chain.

Stats don’t lie.

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.

And that’s not all – Gartner surveys[1] say 64% of supply chain leaders have employed AI solutions in one way or another. 31% of them use AI to automate decision-making. In this article, we are going to examine how AI supports supply chain optimization. Read on!

Read on to find out how AI can 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.

supply 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 interesting 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

Inventory management

The use of big data is integral in the inventory management of different industries. For example, the majority of original equipment manufacturing (OEM) companies rely on the transfer of data internally and externally with other companies across the supply chain to create products according to a prearranged schedule. Let’s see how it works in the automotive industry.

supply chain statistics

Here, companies use the so-called just-in-time (JIT) manufacturing process, which requires the manufacturing facility to keep a minimal supply of cars on hand and rely on routine deliveries by suppliers. The OEMs notify their suppliers about the number of each vehicle design they will be creating and in what order. Therefore, the suppliers can deliver the required number of components in the suggested order to satisfy the production schedule.

This entire process is data-driven, which means artificial intelligence can provide considerable help to OEMs to manage inventories and streamline the manufacturing process.

Language barriers

Due to the upsurge in international sourcing, there’s a high likelihood of a language barrier involving companies from different locations around the globe. For example, there’s a need for a translator every time there’s a meeting between Chinese and American manufacturing facilities and many Mexican or African suppliers.

supply chain optimization

Artificial intelligence comes in to get rid of these language impediments. You see, it is fairly easy to train AI to translate between 2 languages, e.g., during a meeting. You simply need to feed vast amounts of material into the neutral network in any of the languages you wish to translate between[3].

So, once the data is uploaded, AI comes in to help with the translation during business meetings.

Customer service

A significant section of the supply chain optimization process involves working with consumers to guarantee timely deliveries, quick responses to raised queries, and great supplier-customer relationships. Thanks to AI, many companies are adopting the use of smart assistants like chatbots and voice bots to help with customer service.

customer service

This translates to huge cost savings through the reduction of the customer service budget. Also, chatbots can help build deep interactions with the customer, which translates to improved UX and customer service.

Besides chatbots, AI gives customer service representatives access to real-time status updates of all product orders. This information can be quickly shared with the customer or vendor if requested.

Therefore, the staff can then concentrate on other important matters.

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 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 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.

If you’d like to find out how Addepto can help you optimize supply chains using AI-based solutions, take a look at our AI consulting services and drop us a line for details.

References

[1] Doi.org. URL: https://doi.org/10.1016/0957-4174(90)90065-3. Accessed April 21, 2022
[2] Carly Fiorina (2004), “Information: the currency of the digital age,”. URL: http://www.hp.com/hpinfo/execteam/speeches/fiorina/04openworld.html. Accessed April 21, 2022
[3] Mckinsey.com. Featured Insights: AI Proves its Worth But Few Scale Impact. URL: https://www.mckinsey.com/featured-insights/artificial-intelligence/global-ai-survey-ai-proves-its-worth-but-few-scale-impact. Accessed April 21, 2022
[4] Green, Peter. “Artificial Intelligence for Real-Time Manufacturing Execution and Operations Management.”. Processing Magazine. URL: www.processingmagazine.com/aireal-time-manufacturing-management/. Accessed April 23, 2022
[5]Ibm. com. URL: https://www.ibm.com/watson. Accessed April 22, 2022
[6] Savidge, Alexandra. “AI in Supply Chain: 6 Reasons Executives Should Invest in AI in 2018.” Digital Authority. URL: www.digital authority.me/insights/ai-supply-chain strategy-24-04-2018., Accessed April 23, 2022



Category:


Artificial Intelligence