4 Ways AI is Helping to Improve Supply Chain Operations


4 Ways AI is Helping to Improve Supply Chain Operations


There is no doubt that in our connected, digital world, the competition for consumer dollars has become fierce in every vertical. As such, it has become increasingly critical to maximize efficiency while reducing uncertainty, especially in the supply chain.

Mounting expectations of supersonic speed and efficiencies between suppliers and vendors of all types has pressed the need for industry to leverage innovative digital technologies throughout the supply chain. Executives, supply personnel, data analysts, and shippers are all looking for the edge against the competition. 

Now, many are turning to AI and machine learning tools that offer significant opportunities for companies to harness their data in new ways to improve shipping and supply lines on both sides of their business.

Artificial intelligence has established itself as an essential tool for supply chain transformation. For example, 46% of supply chain executives anticipate that AI and cognitive computing will be their greatest areas of investment in digital operations over the next three years.

AI can help supply chain leaders make decisions faster and more accurately than traditional data analysis methods. This is true in a large number of applications related to transport and logistics, demand forecasting, and more. 

In this article, we are going to take a look at four ways that AI is improving supply chain logistics and helping businesses to improve performance. 

1.  AI’s role in risk management

 Where AI can fit into a risk management scheme for supply chain management is in revealing and keeping track of risks that might not be immediately recognizable. For anyone involved in supply chain management, understanding fluctuations in the chain is critical to ensuring a consistent supply of products. With AI, you’re able to feed your data into a workflow, and have the platform unveil areas where you can be proactive, instead of reactive when it’s too late.

This article in Forbes discusses the role of AI in risk management and discusses the challenge of maintaining supplier visibility. While data teams understand the need for advanced technology, many are still just beginning to scratch the surface of the possibilities of new technology. The author posits that the issue may be because “mapping supply chains is traditionally a painstaking process.”

This leaves businesses without a reliable source of truth, making decisions with a fragmented view of the supplier ecosystem. And when problems arise, those teams do not have the time to “comb through outdated databases or scour websites for certifications.”

The author goes on to say, “while organizations have long relied on digital tools to improve efficiency, AI-powered platforms can now do more than speed up contracts and reporting. By proactively screening supplier markets for up-to-date, comprehensive supply market data, teams can now generate learnings and improvements to unlock huge value – across product innovation, risk mitigation, and economic opportunities.”

The difficult aspect of adding AI into supply chain risk management is the challenge of development and deployment. Building AI models that fit the bill requires time and money, which is something that most companies attempting to solve supply chain issues at speed don’t have. 

For this reason, data teams are turning to AI platforms with ready-to-use workflows that allow them to uncover issues immediately.

2. Machine learning delivers advanced info on procurement

AI and machine learning models are being used to great effect by those working in procurement. With advanced information on costs and savings, these departments are able to save money and replace many of their manual processes.

One of the ways in which AI is used in procurement is in identifying opportunities that can affect impact to profitability. This article cites The Profound Benefits of AI Adoption in Procurement by Shamli Prakash and notes the importance of new technologies and data analytics to arm buyers with “unprecedented ammunition for identifying opportunities to deliver bottom-line impact.”

They call out Prakash, noting how he points to how procurement departments become more valuable as they find more ways to save money. Prakash goes on to say that “until recently it was a challenge for procurement to even have an accurate understanding of what an organization’s total spending was.”

The way around this is adding advanced analytical methodologies to build a solid data foundation that can help procurement save up to 15-25% of their budget. The article wraps up by quoting Prakash one last time:

“While the journey to becoming an AI enabled procurement organization needs time and effort, it is well worth the investment… Once the data foundation is in place, there are many different avenues through which procurement can leverage it to deliver consistently higher value.”

This article notes how important data is for procurement in today’s world. Focusing on the issue of spend analysis, the article states that “ML algorithms can be used to classify spend data into functional, structured, and standardized classes.”

By utilizing AI in this aspect of procurement, they note that “AI offers a clearer and more detailed insight into an organization’s spending.”

3. AI platforms advise order fulfillment

If a business only had to deal with one supplier, life would be wonderful and those involved in order fulfillment would go home early every day. However, we don’t live in that world and the processes behind ensuring smooth order fulfillment processes can keep those responsible up at night.

Fortunately, AI is helping to make those processes more streamlined and efficient.

The biggest issue for order fulfillment is ensuring that all the different inventory locations a business works with are in sync with order touchpoints like POS systems and ecommerce platforms. All of this also needs to be in sync with the systems used to monitor inventory, shipping, reporting, etc.

This article gets into the issues mentioned above, and goes on to discuss new issues that retailers face such as expedited shipping. Retailers know that they need to fulfill those orders in the time specified or deal with angry customers. “Order management systems that leverage artificial intelligence can simulate fulfillment and shipping scenarios, weighing the cost of shipping from a particular location against the ability to meet delivery windows.”

Using the right platform, retailers can plug into the system through available APIs and process this information in real-time, giving the retailer information on whether they should ship that order from a nearby location or from a standard shipping spot. Knowing the right course of action in a scenario like this can save an awful lot of time and money.

The above is just one example. Utilizing AI can help retailers to build more effective order fulfillment architectures that ensure customers are satisfied and shipping departments are not completely overwhelmed.

4. AI for better operational planning

One of the biggest aspects of operational planning is demand forecasting. Identifying and understanding patterns in demand forecasting and segmenting those away from noisy data is possible with machine learning workflows that help operational planners uncover the issues that affect their bottom line.

A great example of this in action comes from a watch manufacturer called Seculus. They have more than 50 different styles of watches. They assemble those watches themselves but get the parts for the assemblage from elsewhere. This means that they deal with more than 1,400 SKUs. They also must understand demand for specific items and the times when they increase and decrease. All of this information is blasting them with a huge amount of data that they need to sift through in order to advise production decisions.

They were able to leverage all that data by using an AI platform with ready-to-use workflows, developing prediction models that helped them review seasonality, product lifecycle, and swings in demand. They were able to predict demand forecasting with 86% accuracy within just a few days, allowing them to understand purchasing and production demands more clearly.

Armed with AI, any organization can make better operational planning decisions based on the data that they’re already drowning in, bringing them back up to the surface where they can breathe fresh, manageable air.

Accelerating AI adoption with enterprise software.

AI and ML are powerful technologies that help decision-makers see beyond the obvious. These tools quickly process vast amounts of data, faster than humanly possible, while surfacing trends and patterns to provide insights and recommendations.

Companies often assume that they need dedicated teams of AI and ML specialists in order to build and deploy learning models for their business. This is a costly and time consuming investment that can take months or years to see results.

However, at Stradigi AI, we have invested in building enterprise AI software to help companies democratize the use of AI throughout their organization. Kepler has been designed with years of data science expertise, and is available to help companies quickly deploy AI.

It alleviates the need for large budgets and data science teams. This allows for an affordable entry point, with much faster AI integration than building a new platform from the ground up or building on top of legacy solutions.

While the journey to becoming an AI-enabled organization needs time and effort, it is well worth the investment.

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