- Modern supply chain management driven by Artificial Intelligence: Understanding data analytics in supply chain
- What are the benefits of AI in the supply chain?
- Top five AI and analytics-enabled use cases to control supply chain disruption
- Understanding the future of AI in supply chain: Best supply chain predictions for 2023 and beyond
- Make your supply chain business future-ready
The next wave of the two most prominent technologies – artificial intelligence and data analytics, is already making a hit. Where several industries are still struggling to overcome the post-pandemic effects, there are a few industries that took the opportunity to adopt these modern technologies at a large scale.
One of them is the supply chain industry. Data from Statista shows that AI solutions in the supply chain market have resulted in better inventory management, smart manufacturing, dynamic logistic systems, and real-time delivery controls.
The main objective of using AI in supply chain and logistics is to increase efficiency and productivity. This introduction of AI in supply chain management has led to more sustainability, making every enterprise wonder if digital transformation at this scale can benefit their respective supply chain business.
A recent study conducted by McKinsey has determined that the implementation of AI-enabled supply-chain management has led to significant improvements. Specifically, adopters of this technology have seen a 15% reduction in logistics costs, a 35% reduction in inventory levels, and a 65% increase in service levels. This demonstrates the potential of AI-enabled supply-chain management to revolutionize the industry, and its importance in the modern business landscape.This blog will help you understand what AI and data analytics in the supply chain can do for your business.
So, let’s move ahead and look at how AI in the supply chain impacts the businesses. In addition to this, we will also take a look at how integrating AI development services for your enterprise will bring the workforce, machines, and software into action.
Modern supply chain management driven by Artificial Intelligence: Understanding data analytics in supply chain
Using AI and/or analytics in supply chain refers to the process where smart machines can perform problem-solving tasks. This automated process of smart industry manufacturing run by IIoT (Industrial Internet of Things), can drive the entire supply chain without any manual participation. Enterprises that use AI in supply chain operations aim to make the supply chain business more:
- Instrumented –
machine-generated data flowing out of IoT devices
- Intelligent –
more accurate and competent assumptions with the help of data analytics and modeling
- Interconnected –
extensive connectivity for better decision-making
The supply chain data analysis helps optimize the workflow where large amounts of data can provide forecasting, identify inefficiencies and drive innovation. There are majorly four kinds of supply chain analytics that you can follow to make insightful data-driven decisions for your supply chain business.
Here are the few examples of analytics in supply chain:
1. Predictive analytics
Predictive analytics is a technique that leverages the power of statistical modeling and regression analysis to identify and understand trends from historical data in order to make predictions about future trends.
This helps supply chain companies predict the most likely future outcome and its business implications. For instance, using predictive analytics to mitigate risks and disruptions.
2. Descriptive analytics
Descriptive analytics is a form of data mining that involves the analysis of large datasets to identify patterns and generate summaries that allow users to gain insight into a given situation. This type of analytics utilizes historical data to uncover trends and draw conclusions that can be used to inform decision-making.
Descriptive analysis is another example that can help you understand the concept behind analytics in supply chain. This helps provide visibility and certainty to all kinds of internal and external data across the supply chain management.
3. Prescriptive analytics
Prescriptive analytics is a powerful tool for supply chain operations, allowing for the exploration of how specific changes will affect outcomes. Through this, potential improvements can be identified and recommended, providing a valuable resource for optimizing supply chain operations.
This includes collaborating with logistic partners to reduce time and efforts for maximum business value. One popular reference is the use of SRM (Supplier relationship management) as a prescriptive analytic approach.
4. Cognitive analytics
If you wish to understand advanced analytics in supply chain management, cognitive analytics is the way to go. This is best used in enhancing customer experience and relationships. The feedback data received through AI-driven systems is analyzed and executed in reports and dashboards to answer complex questions.
This advanced use of supply chain analytics can automatically allow your business to pursue breakthrough ideas and provide better customer needs and demands. If you still haven’t decided on embracing AI and analytics for your business, our next point of discussion is for you.
What are the benefits of AI in the supply chain?
Given the significant business value at stake, multiple supply chain vendors and distributors are now looking forward to the benefits of AI in supply chain planning. The new offshoots of the supply chain business includes demand planning (revolutionized by AI ML in supply chain), real-time inventory management (controlled by IoT and connected systems), and end-to-end dynamic margin optimization within the supply chain industry (driven by AI-based solutions).
At such times, choosing the right solution is critical. Therefore, to manage the complexity of the modern supply chain, your business needs to embrace these smartly designed solutions aligned with your everyday needs.
Why else do you need to invest in AI and analytics-based solutions? Find the primary reasons below:
1. Enhanced 360-degree responsiveness
The most sought-after use of AI in the supply chain is enhanced visibility and responsiveness. Artificial intelligence in the supply chain allows your business to gather relevant past and current data from multiple connected devices. This includes implementing the SRM software, CRM and ERP systems, and business intelligence solutions to existing data. This way, you can evaluate the performance on a broader scale. Similarly, supply chain data analysis also predicts and minimizes risks and negative impacts on the distribution channels.
2. Improved customer experience
This cannot be stressed enough that big data and artificial intelligence have taken the customer experience to a whole new level. These solutions allow the supply chain to create personalized products based on the current user demands. One widely used instance can be modern transport and logistics using voice-activated means of tracking shipments and orders. This goes both ways where even the customers can perform the voice-activated query search using Alexa or Google assistant.
3. Increased fleet efficiency
Increased fleet efficiency is another use of AI in the supply chain that has made several organizations implement the use of this technology. The essential role of AI in supply chain management is on-time product delivery. The advanced AI-based GPS tools enable better navigation and optimization of the route for fleeting and transportation. These tools access the most effective route for product delivery by processing the driver, vehicle, and customer data through machine learning. Simultaneously, AI and machine learning in the supply chain will help you save both time and money for future shipments.
4. Competitive edge
Observing and acting upon the market trends and patterns is the key to staying ahead in the supply chain business. AI in supply chain analytics can harness real-time data from external resources such as industrial production, weather, and employment history. With all the accumulated data, you can better gauge the market conditions and assess upcoming demands for stable growth.
You may also leverage the AI’s sensory competencies to reshape your product portfolio and capital expenditure. This is currently the preferable application of AI in supply chain management.
5. Simplified inventory management
Let’s not forget that well-organized inventory management is the foundation of the supply chain business. The analytics-based machine vision software can minimize the standard manual input and create accurate forecasts. The AI in supply chain management system also interprets real-time machinery data that continuously monitors the inventory and stock in the warehouses.
6. Access to future insights
AI in the supply chain ecosystem makes way for a supreme level of agility for the business. The same goes for the use case of data science for supply chain forecast, where you can determine your client’s needs before they arise. This will help you take your supply chain enterprise to a whole new-level and achieve the maximum customer satisfaction level.
The above benefits can help you understand the ever-expanding scope of AI in logistics and supply chain. Your decision to invest in AI in supply chain analytics solutions will be easy once you check out the use cases of these technologies in your business.
Top five AI and analytics-enabled use cases to control supply chain disruption
It’s time for modern supply chain enterprises to empower their business with reliable and automated data visual analytics platforms. You can refer these AI use cases in supply chain to minimize the supply chain disruption and make the most out of your business.
1. Demand forecasting in warehouse supply and demand management
Machine learning is leveraged to identify influential factors in supply chain and transportation data with algorithms and constraint-based modeling. Constraint-based modeling is a mathematical approach where a maximum and minimum range of product limits constrain the possibility of each business decision.
This data-rich modeling method is by far the best use case of data science and AI for supply chain forecasts that empower warehouse employees to make more informed decisions on inventory stocking. Another approach is big data predictive analysis that offers a deep level of insights to self-improve forecasting loops.
Today’s supply chain management uses AI solutions to power its inventory optimization, where the warehouse and stock managers are informed on real-time control of parts, components, and finished goods. As machine learning ages, the AI system produces stocking recommendations based on previously purchased data and supplier deliveries.
2. AI and machine learning in the supply chain for the longevity of transportation and logistics
IoT device data is generated from in-transit supply chain vehicles to deliver real-time insights on the longevity of the transport vehicles. The machine learning systems integrated into the vehicles make maintenance recommendations and failure predictions based on past data. This will allow you to take fleeting vehicles out of the chain before the performance issue causes any kind of delay in the deliveries. Not to forget, AI in logistics and supply chain helps reduce the downtime that can save significant mechanical failures.
3. AI in adding portability to supply chain loading process
Supply chain management comes with a great deal of detail-oriented analysis, including how shipments and goods are loaded and unloaded from the shipping containers. Both data modeling and AI precision are needed to determine the most efficient ways to get the goods on and off the containers.
Modern supply chain companies use the combination of software (SRM), hardware, and supply chain data analytics to get hands-on real-time visibility into the loading process. The gathered data can also be used to design less risky and quick process protocols to manage parcels.
4. AI for cost-saving and revenue boost in supply chain
When it comes to the ultimate cost-saving, AI and analytics solutions are the most effective options to negotiate better shipping and procurement rates, pinpoint changes in the supply chain profit process and manage courier contracts. You can assess a centralized database that takes virtually every aspect of the supply chain to deliver financial decision-making.
In general, advanced analytics in supply chain management is paving the way for new innovations where platforms are used for mining and analyzing cost-effective revenue-building standards. A Bloomberg report suggests that in the past two years, the overall cost in the supply chain has reduced to 12% leading to profits.
5. Data analytics-based strategic sourcing in supply chain
The most underrated application of AI in supply chain industry is the identification of critical suppliers and strategic partners. This helps you standardize lower-cost alternatives and predicate supply performance indicators for compliance.
We hope these multiple AI use cases in supply chain have made you understand the importance of the technology in this ever growing sector. Utilizing descriptive and predictive analytics also contributes to further augmentation in manufacturing industries like hi-tech, CPG, and consumer electronics. The key to advanced self-service AI and analytics enables a high degree of transparency and independency in the supply chain business.
Understanding the future of AI in supply chain: Best supply chain predictions for 2023 and beyond
According to a study from McKinsey, implementing AI and data analytics in logistics and supply chain management will enable industries to improve their logistics costs by 15%. In addition, they can also expect an improvement in inventory and service levels by 35% and 65%, respectively. Here are the few best supply chain predictions that will help you comprehend AI in supply chain trends effectively.
1. It is predicted that more than 75% of large enterprises will use intralogistics smart robots to manage their warehouse operations by the year 2026. These robots will further supplement the need of the human workforce while making sure that the implementation of processes is faster and less expensive.
2. According to a recent report, by the year 2026, more than 75% of vendors dealing with commercial supply chain management applications will be able to deliver results that are backed by advanced analytics as well as artificial intelligence (AI) and data science. Using analytics in operations supply chain management will further ensure an improved decision-making process for supply chain users. Many large supply chain vendors have already signed up for implementing AA and AI in their services, while small vendors are catching up shortly.
3. By 2025, more than 25% of supply chain vendors will witness the power of a microservices architecture while making way for true composability. Even though upgrading the SCE application requires them to invest a huge amount of money at the initial stages, the investment is worth it, considering they will be able to tackle the challenges associated with adoption at scale.
4. It is also predicted that not implementing AI in the supply chain will lead to significant value loss for more than 80% of the supply chain organization as they will not be able to merge their initiatives for digital supply chain twin and control towers. The latter is further responsible for handling delays in the shipments, thereby decreasing losses and increasing the average ROI.
Now that you have looked at the various future predictions for the use of AI in the supply chain, let us move ahead and discuss how Appinventiv is the right fit for your supply chain organization that is looking forward to diving into the latest AI solutions.
Make your supply chain business future-ready
The benefits of artificial intelligence in supply chain management are indispensable. AI is a commonplace technology for the supply chain now that enterprises of every scale and size have adopted its extensive applications. Given the current scenario, every supply chain business model needs to be critically integrated with AI and analytics solutions for optimization.
Therefore, If your supply chain business still lacks the above-discussed modern solutions, it’s high time to choose Appinventiv’s data analytics services to help you derive valuable insights from your business data for maximum scalability and performance. We can empower your business with the best possible services, from supply chain analytics and data visualization to data warehousing and business intelligence solutions.
Get in touch with our team of developers to explore and deep dive into the benefits of AI for your supply chain business.
Q. What is AI in supply chain?
A. AI in supply chain simply means using AI solutions in your supply chain management tasks. They can deliver powerful optimization capabilities that are further required to accurately plan the capacity, improve overall productivity, increase quality, lower implementation costs, and offer more excellent outputs while encouraging safer working conditions.
Q. How AI and machine learning are applied in the supply chain?
A. AI and ML are applied in the supply chain ecosystem with the help of advanced algorithms. The role of AI in supply chain solutions will be to enhance the quality of data and offer you a wholly redefined overview of the warehouse and supply chain. It can further help you predict the demands and help in restoring the optimal stock levels promptly. A dedicated AI development services organization like Appinventiv can help you integrate AI ML in your supply chain management software effectively.
Q. How can AI make supply chains more sustainable?
A. AI can help companies optimize the supply chain by identifying bottlenecks and trends in production, labor, and pricing that can be exploited to reduce costs and increase revenue. The analysis will predict future demand, allowing companies to steer materials away from areas of high risk and toward new sources of demand.