The next wave of the two most prominent technologies artificial intelligence and data analytics, is already making a hit. Where several industries are still pulling doors 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. Recent studies have declared that the adoption of Artificial Intelligence in the supply chain has 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 digitization 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.
So if you are not sure what AI and analytics can do to your supply chain business, this article is just what you need to clear all your doubts. Let’s look at how AI impacts the supply chain and how modern supply chain management brings the workforce, machines, and software into action.
Modern supply chain management driven by Artificial Intelligence and Analytics
Using AI-based solutions in supply chain and logistics 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 the supply chain 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. The experts call it supply chain analytics. 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 supply chain analytics examples:
1. Predictive analytics
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
This helps provide visibility and certainty to all kinds of internal and external data across the supply chain management.
3. Prescriptive analytics
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
This is best used in enhancing customer experience and relationships in supply chain management. 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.
Benefits of investing in AI and supply chain analytics
Given the significant business value at stake, multiple supply chain vendors and distributors are entering the competition. The new competition in the supply chain business includes demand planning (revolutionized by data analytics and machine learning), 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? Below are the primary reasons:
1. Enhanced 360-degree 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 stretched 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
The essential task 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, they help you save both time and money for future shipments.
4. Competitive edge
Observing 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 artificial intelligence 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 systems also interpret real-time machinery data that continuously monitors the inventory and stock in the warehouses.
6. Access to future insights
The AI ecosystem allows the supreme level of agility to the supply chain 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 even know it. This is like entering your supply chain enterprise into the future to achieve the maximum customer satisfaction level.
The above benefits prove nothing but the ever-expanding scope of AI and analytics in the supply chain and logistics. Your decision to invest in AI-based supply chain analytics solutions will be more simplified 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 follow the below-discussed practices on AI and analytics 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 the possibility of each business decision is constrained by a maximum and minimum range of product limits.
This data-rich modeling method is by far the best use case of data science for supply chain forecasts that empowers 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 previous purchased data and supplier deliveries.
2. Machine learning and AI 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 the reduction in 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, artificial intelligence for 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 use case of AI and analytics in the supply chain is the identification of critical suppliers and strategic partners. This helps you standardize lower-cost alternatives and predicate supply performance indicators for compliance.
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.
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 AI development services for your enterprise.
Appinventiv can 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.