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AI in Supply Chain: Challenges, Benefits & Implementation
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27th Dec, 2023
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Supply chains have significantly gotten harder to manage in recent years. In the present corporate world, supply chain professionals' ability to effectively manage these complex processes and adapt to unexpected challenges has become a major responsibility. By regionalizing and optimizing flows, businesses aim to create more sustainable and environmentally responsible supply chain strategies.
With AI, businesses can more effectively and flexibly navigate the complicated structures of present-day supply chains. Businesses can use artificial intelligence (AI) to improve decision-making, streamline operations, and boost the overall performance of their supply chains. AI has the potential to change the game because of its capacity to analyze massive amounts of data, comprehend relationships, offer visibility into processes, and promote smarter decision-making. In this article, we will discuss the role of AI in supply chain management, examples of artificial intelligence in supply chain management, how is AI used in the supply chain, and much more.
AI in Supply Chains & Logistics: A Future Full of Promises
Supply chain firms anticipate a two-fold increase in the amount of machine automation in their supply chain activities over the next five years, according to Gartner. The Industrial Internet of Things (IIoT) is also likely to see a large increase in worldwide investment, with projections indicating a jump from $1.67 billion in 2018 to $12.44 billion by 2024. Given that the compound annual growth rate (CAGR) during that period was 40%, this indicates significant growth.
Supply chains and logistics have a lot of potential in the future thanks to AI. It gives businesses an opportunity to improve decision-making along the whole supply chain, achieve substantial productivity, and simplify processes. AI has the potential to completely transform the way things are transported from producer to consumer, from demand forecasting and inventory management to route optimization and last-mile delivery. Supply chains are likely to become more adaptable, robust, and customer-focused as AI develops. Businesses now have tremendous chances to maintain their competitiveness and satisfy consumers' ever-changing needs.
Future iterations of generative AI promise substantially greater performance and an ever-expanding, ever-improving menu of alternatives for exploiting firm data to create a competitive advantage. AI learning has to be a vital component of organizational training programs and Artificial Intelligence course details are essential for those seeking to enhance their skills in artificial intelligence.
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Challenges of AI in Supply Chain
While using AI in supply chain and logistics management has many potential advantages, there are also difficulties and things to take into account. Integrating data and ensuring security are two significant challenges when implementing AI in supply chains. Let us also look at a few more challenges:
- The Capacity to Scale: The degree of initial start-up users or systems that may be required to be more impactful and effective could be higher because the majority of AI and cloud-based systems are quite scalable. This is something that supply chain partners will need to thoroughly discuss with their AI service providers because every AI system is distinct and diverse.
- System Limitations: Usually cloud-based, AI systems demand a lot of bandwidth. The expense of this AI-specific technology can prove to be a significant initial expenditure for many supply chain partners. Operators occasionally need specialized hardware to access these AI capabilities.
- The Cost of Training: The implementation of AI and its efficient application will require staff training, which is another component that will require a major time and financial investment. The supply chain partners will need to collaborate closely with the AI providers to develop a training solution that is effective and reasonably priced during the integration phase. This could have an influence on business productivity.
- The Associated Operational Expenses: An amazing network of individual processors power an AI-operated machine, and each of these components requires upkeep and repair from time to time. The difficulty in this situation is that the operational investment could be rather large given the potential cost and energy required. The electricity costs could skyrocket and have a direct impact on the overhead costs when it comes time to replace some of these components.
Common Supply Chain Tasks That Can Be Automated
Tasks in the supply chain can be automated using AI to save time and money compared to performing them manually. It is high time for teams to upskill themselves with the right AI skills courses, and KnowledgeHut’s AI training program is an excellent choice. You can know more about KnowledgeHut Artificial Intelligence course details and see if this course suits your learning requirements.
Business supply chain tasks that can be automated include:
- Warehouse robotics: To move materials and carry out other duties, a business can utilize automated equipment and specialized software. Using warehousing software, many receiving and shipping processes can be automated. These include managing suppliers, handling documentation, and interacting with customers. The physical tools and machines you employ to run your warehouse can also be automated, in addition to software.
- Inventory Automation: You need to have a solid understanding of where each SKU is kept, and how much of it is available to manage an efficient warehouse. If your company has to manage dozens, hundreds, or even thousands of different products, frequently spread across numerous warehouses or locations, it can be challenging to keep up without automated tools and machinery. Utilizing inventory management software will help you simplify the complete stock control procedure, maximizing cash flow, production, and inventory tracking to eventually increase sales and meet customer expectations.
- Predictive analytics: By examining patterns and trends in this data, predictive analytics helps organizations make informed predictions about future events or outcomes. In the context of supply chains, predictive analytics can play a crucial role in automation. It enables businesses to anticipate various aspects of their supply chain operations, such as demand forecasting, inventory management, and logistics optimization. Teams must develop in-depth knowledge and abilities if they want to flourish in data science and use predictive analytics for supply chain automation. For both individuals and teams, enrolling in a top Data Science certification program is a terrific approach to acquiring the necessary skills.
- Back Office Automation: Through automated data entry and real-time visibility of business insights, back-office automation boosts productivity. In supply chain processes, data entry can be a significant bottleneck. By reducing the potential for human error and shortening the processing time, back-office automation aids in the solution to this problem.
How to Implement AI in Supply Chains
You must first evaluate your level of digital readiness before making significant investments in new technologies. Below are the four steps to follow before implementing AI for your supply chain:
Step 1: Identification, Plan, and Road Map for Value Development
Identifying and prioritizing any area of value creation across all functions, from procurement and manufacturing to logistics and, ultimately, commercial, is the first step that businesses must take. Performing an independent diagnostic at the beginning is something that less than one-third of businesses do, yet doing so can guarantee that businesses have a complete list of all value-creation prospects.
Step 2: Design of the Proposed Solution and Selecting the Service Provider
Finding a single vendor that can satisfy all of these requirements is becoming increasingly unlikely due to the complexity of supply chains, which includes demand forecasting, planning optimization, and measuring digital execution. Executives should understand that the proposed solution by the providers, whose objective is frequently to push for a single end-to-end solution, won't always be the best one for their organization.
Step 3: Systems Integration and Implementation
Many businesses lack the necessary expertise to adopt technology across their entire organization. Upon selecting a solution, there is a risk of implementation falling behind schedule and exceeding the allocated budget, all while losing sight of the primary objective – to effectively address the value-creation opportunities right from the start. Companies should approach implementation and system integration holistically.
Step 4: Developing Capabilities, Managing Change, and Effectively Maximizing Value
Companies must take care of essential auxiliary components like organization, change management, and capability building even as they concentrate on technological solutions. According to our research, this task is frequently difficult. Employees will need to adopt new working practices, and it will take a coordinated effort to inform the workforce of the reasons why changes are required. Incentives will also be needed to reinforce the desired behaviors.
Real-world Examples of Artificial Intelligence in Supply Chain Management
The numerous practical uses of AI in supply chain management are among its most intriguing features. AI is being utilized to enhance every part of the logistics network, from forecasting demand to rerouting traffic and managing inventories. Below is a list of AI use cases in supply chain:
1. Demand Prediction Improves Inventory Supply and Demand Control
Algorithms and constraint-based modeling, a mathematical technique where the results of each action are bound by a minimum and maximum range of constraints, are being used to uncover patterns and influential elements in supply chain data. Warehouse managers may now make considerably more informed judgments about inventory stocking thanks to this data-rich modeling.
2. AI Is Improving Delivery Logistics and Routing Efficiency
Companies who don't have a good grasp on delivery logistics run the danger of slipping behind in a world where virtually everything can be ordered online and delivered within a matter of minutes. Customers today expect speedy, accurate shipment, and when a business cannot meet those expectations, they are only too pleased to look elsewhere. The most effective routes are generated using AI-driven route optimization platforms and GPS tools powered by AI, such as ORION, a company employed by logistics leader UPS.
3. Machine Learning AI is Extending the Lifespan of Transportation Vehicles:
In-transit supply chain vehicle data is gathered via IoT devices and other sources. The condition and longevity of the costly equipment required to maintain the smooth flow of goods across supply chains are revealed by this data, which is extremely useful. Based on historical and current data, machine learning generates maintenance suggestions and failure forecasts. This gives companies the opportunity to cut out vehicles from the chain before performance issues result in a series of delays.
4. AI Insights Are Making Loading Processes More Profitable and Efficient
Detail-oriented analysis is a big part of supply chain management, and that involves looking at things like how goods are loaded and unloaded from shipping containers. To identify the quickest, most effective ways to load and unload cargo onto trucks, ships, and airplanes, art and science are required. Companies like Zebra Technologies provide real-time visibility into loading operations using a combination of hardware, software, and data analytics. These discoveries can be utilized to maximize interior space in trailers, cutting down on the amount of "air" transported.
5. AI Is Helping Supply Chain Managers Discover Cost-Cutting and Revenue-Boosting Techniques:
The cost of shipping goods around the world is high and rising. Companies like Echo Global Logistics utilize AI to manage carrier contracts, negotiate better shipping and procurement rates, and identify areas where supply chain modifications could increase revenues. Users can get financial decision-making guidance from a single database that practically accounts for every facet of supply chains. Supply chain advances using AI are laying the groundwork for a time when we might finally anticipate the deployment of autonomous, AI-powered automobiles.
Which Companies Are Using AI In Supply Chain Management?
By adopting AI as both a technological tool and the catalyst for organizational change, businesses may fully realize its potential in supply-chain management. Numerous companies offer supply chain and logistics software and solutions with artificial intelligence. Let us look at each of the companies using AI in supply chain in detail:
1. Coupa
Coupa offers a variety of AI and digital solutions that enable logistics network organizations to make data-driven decisions. Businesses may acquire logistical data and forecast results by modeling various scenarios, thanks in part to the Supply Chain Modeler. The AI-powered features also take into account extraneous elements like tariffs and weather patterns, giving businesses the ability to assess potential hazards and make the required modifications to their logistics network operations.
2. Epicor
Epicor uses Microsoft Azure, a cloud platform powered by artificial intelligence, to improve its business solutions for distributors and manufacturers. Management of the supply chain and logistics are some of these options. To improve user interaction with its products, the company is also considering integrating Microsoft's speech-to-text and advanced search features.
3. Echo Global Logistics
A transportation management startup called Echo uses artificial intelligence to provide its customers with logistics network solutions that simplify shipping and receiving. Customers may ship their items quickly, securely, and affordably with the use of these options. The Echo offers a variety of services, such as rate negotiations, transportation procurement, shipment execution and tracking, carrier management and selection, compliance, executive dashboard creation, and full shipment reports.
4. LivePerson
With the use of an AI-powered conversational platform from LivePerson, effective customer service is made possible by assessing client intent and emotion to drive the conversation. The platform is also capable of managing several conversations at once, whether they are being carried out by a human agent, a bot, third-party software, or a combination of all of them.
5. Infor
Applications from Infor's intelligent logistics network combine cutting-edge algorithms, optimization tools, and machine learning to link the virtual and real worlds. This enables businesses to gain useful information and take wiser business decisions. Logistics network planning, procurement automation, supply chain financing, supply management, supply chain visibility, transportation management, and warehouse management are among the services offered by Infor.
Benefits Of AI In Supply Chain and Logistics Management
Because AI has so many advantages, more and more companies are implementing it in one form or another. It is anticipated that by 2025, around 96% of supply chain and manufacturing enterprises would have implemented AI!
A number of advantages arise from implementing AI in supply chain management, many of which support strategic advantage and operational excellence. Below are some major advantages:
- Operational efficiency: Optimal efficiency can be attained by using supply chain components as important data sources for machine learning algorithms. Price planning is one area where this value is obvious. Through the utilization of this priceless data, price modifications that are influenced by current trends, product life cycles, and competitive positioning may be adjusted, thus improving the supply chain planning procedure as a whole.
- Reduced costs: AI can assist businesses in lowering labor and transportation costs by automating tasks and detecting inefficiencies.
- Increased income: Artificial intelligence in the supply chain can assist businesses in increasing revenue and strengthening their bottom line by enhancing efficiency and lowering costs.
- Complete Visibility: Manufacturers must easily have complete visibility of the whole supplier value chain given the intricate web of supply chains that exists today. A single virtualized data layer provided by a cognitive AI-driven automated platform can be used to identify chances for improvement, remove bottleneck procedures, and uncover cause-and-effect relationships. Instead of using redundant historical data, all of this is done using real-time data.
- Conscious Decision Making: By making recommendations for the best course of action based on cognitive predictions, AI-driven supply chain optimization software magnifies crucial decisions. This could improve the efficiency of the entire supply chain. It also reveals potential effects on time, expense, and revenue across a range of scenarios. Additionally, it continuously enhances these recommendations as relative situations change since it learns new things over time.
- Enterprise Resource Planning Simplification: The ERP architecture can be streamlined with AI in the supply chain and logistics to make it more efficient and intelligently integrate people, processes, and data. The data becomes more event-driven and receptive over time, processing larger volumes of data to intelligently learn, quantify, rank, and recommend cures more regularly and proactively when AI is properly integrated on ERP and associated data systems.
- Better customer service: By giving customers access to real-time tracking information, artificial intelligence can help businesses become more responsive to their customers' requirements.
Disadvantages of AI In Supply Chain and Logistics Management
While using AI to supply chain and logistics management has many potential advantages, there are also difficulties and things to take into account. Let has have a look at the disadvantages:
- Complexity: Integrating artificial intelligence into logistics management and networks can be challenging and expensive in terms of both technology and resources.
- Data quality: To operate efficiently, AI systems need high-quality data. It can be difficult to guarantee that data is correct and full.
- Human resources: Employers may need to undergo retraining, and qualified new candidates may need to be found.
- Security and compliance: Because AI systems are susceptible to cyberattacks, it's crucial to make sure that data is protected, and the business complies with all applicable laws.
- Ethics: As artificial intelligence is more fully incorporated into supply chain and logistics management, it is crucial to think about the ethical implications of its use and to make sure that it is done so in a just and responsible way.
Going Green in Logistics and AI’s Role
The logistics sector is placing more and more emphasis on going green or implementing sustainable practices. This entails cutting back on waste production, resource conservation, and carbon emissions. Employing sustainable practices benefits businesses by lowering costs, increasing efficiency and responsiveness, and strengthening their reputation. It also contributes to environmental protection.
The use of artificial intelligence in supply chain management has a big potential to help with environmentally friendly logistics methods. For instance, by determining the most fuel-efficient routes, AI-powered transportation management systems may optimize routes and lower fuel usage. Carbon emissions may be significantly reduced as a result of this. Additionally, AI-enabled sensors and IoT technologies may be used to track and analyze data in real-time, enabling businesses to spot problems earlier and boost the efficiency of their supply chain as a whole.
To maximize inventory control and conserve resources, the right goods must be accessible at the right time and location. Automation driven by AI can drastically cut down on the requirement for manual labor, reducing waste and conserving resources.
What’s The Future of AI In Supply Chain Management?
According to Gartner, "The growth of IIoT will enable supply chains to more effectively deliver more differentiating services to customers."
Traditional business models are going to become obsolete as supply chain organizations begin to prioritize outcomes over items. Organizations will be better equipped to make decisions as a result of this shift, which will boost performance and promote continual improvement. Adopting and making use of the newest cloud-based and SaaS (Software as a Service) technologies becomes a strategic move. With large IT and OT (Operational Technology) budgets, this strategy helps businesses not only compete with but also outperform global organizations.
We are on the verge of a profound change in the way systems function. These systems will become predictive, adaptive, and ever-learning rather than just responding to events. According to PwC, AI applications have the potential to revolutionize business practices and boost the global economy by up to $15.7 trillion by 2030. Today, AI can help supply chain optimization by introducing much-needed agility and precision. When repetitious manual processes can be automated, it can also result in a revolutionary improvement in operational and supply chain efficiencies and a reduction in costs.
Bottom Line: AI in Supply Chains
The use of AI in supply chain management is expected to grow in importance over the coming years, enabling businesses to compete in a globally competitive market and optimize operations. Artificial intelligence will be a crucial element in creating new technologies and procedures that will shape the future of logistics network management as the sector adapts and develops. Companies will be able to monitor their supply chain activities in real-time thanks to AI-enabled sensors and IoT technologies, delivering insightful data that can help with decision-making. Collaborative AI will also link various components of the logistics network and interact with partners, suppliers, and clients to improve operations. The organizations will be able to exchange information and insights in real-time, leading to improved supply chain operations.
Frequently Asked Questions (FAQs)
1. What is the future of AI in supply chain management?
Several developments will likely emerge with the use of AI in supply chain management. As organizations become more aware of AI's potential to boost productivity and cut costs, we should expect to see it used more and more in supply chain management. It is anticipated that AI technologies would interact more easily with current supply chain management systems, enabling more precise data analysis and decision-making.
2. How does AI affect supply chain performance?
AI can improve overall customer responsiveness, reduce operating costs, and spot inefficiencies. The incorporation of AI into supply chain and logistics operations holds the potential of enhancing productivity, reducing waste, and better adapting to the shifting demands of the modern market as it develops.
3. What is an example of AI in logistics?
Delivery drones are increasingly using AI to automate and optimize the distribution process. Delivery drones may get to their destination more precisely and effectively utilizing AI, avoiding obstructions, and choosing the most practical route. In order to prevent collisions, they can also talk to one another. This may result in less expensive shipping as well as quicker and more dependable shipping timeframes. AI can also be used to monitor and track the cargo being sent, ensuring that it arrives on schedule and in good condition.
4. Which companies are using AI in logistics?
Coupa, Epicor, Echo Global Logistics, LivePerson, Infor, Covariant, Zebra Technologies, HAVI, C3 AI, and Symbotic are just a few examples of companies using AI in logistics.
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