Using artificial intelligence to supply chains offers real advantages for the businesses that put it in place. McKinsey's latest research shows that 61% of executives report reduced costs, and 53% report increased sales due to the implementation of artificial intelligence into their supply chains. According to various data, more than one-third record a sales bounce of more than five percent. Revenue-generating supply chain management areas include sales and demand, forecasting, spending analytics, and optimization of the logistics network.
So what is AI is Supply Chain?
AI is intelligence displayed by computers, or when computers imitate or may substitute, intelligent human behavior, such as problem-solving or learning. It can be applied in two ways :
1.Automation of processes and behavior so that they can function without the need for human interaction.
2.Assisting the human decision-making process in day-to-day operations by reducing errors and identifying bias, especially in data analysis.
AI Applications in Logistics
Warehouse logistics and transport activities produce massive quantities of data. To fully benefit from these results, we need to apply analytical tools to obtain better insights. Machine learning techniques can be used to streamline and automate processes such as load forecasting and vehicle scheduling.
Benefits of AI
According to BI Intelligence, Business Insider's premium research service, AI, and machine learning ( ML) will bring enormous benefits to supply chain and logistics operations. These include cost savings by reduced redundancies and risk reduction, better forecasting, quicker distribution by more optimized routes, and enhanced customer support. They agree, however, that AI has yet to achieve widespread acceptance.
Reducing Wasted Admin Time
Businesses spend hundreds of hours a week making manuals, paper-based procedures, testing anomalies and mistakes, and chasing suppliers. Many of these operations can be automated. Technology vendors such as IBM, Google, and Amazon have launched products that use artificial intelligence, such as "virtual assistant" AI bots such as Alexa and Siri.
Supply Chain Planning
ML will provide the best possible demand scenarios based on smart algorithms and machine-to-machine analysis of massive data sets, using work tools running in a continuous loop. This capability could optimize the distribution of goods while balancing supply and demand, and would not require human analysis, except for the setting of parameters.
Supplier risk is a significant concern for multinational companies that have decentralized operations, and AI can help. Data produced from the supplier's operation, such as physical audits, supplier performance reviews, and product failures, provide a significant basis for purchasing decisions. Supplier Relationship Management ( SRM) is still mostly a human operation focused on using available data, although stale or incomplete.
Results to Date have been Somewhat Disappointing
There is no consensus as to why AI is not easily rooted in the supply chain. It may be because:
- Present systems are in dispute with other internal systems;
- The data used is out-of-date, rising to weak decision-making;
- Each phase of the supply chain is too complex;
-Technology is too expensive to buy or operate
There have been some achievements, some popular in consumer applications. Coca-Cola is looking to follow the example of technology firms. AI will live in vending machines, enabling you to personalize your favorite soft drink.
Artificial Intelligence Success Factors
"Without real-time details, AI is only making bad decisions faster," says Greg Brady, CEO of One Network Enterprises, a global provider of AI solutions. He defined 8 items that AI requires to deliver value in the supply chain.
- Links to data in real-time: Stale data leads to bad decision-making.
-Access to external data: AI needs access to external and downstream data otherwise, the results would be no different than the conventional system.
-End goal support: Amid limitations – high standards of customer quality at the lowest possible cost.
-Decision-making must weigh change vs. cost of change: An AI tool must consider cost vs. benefit trade-offs when making decisions.
-The decision-making process must be continuous: self-learning and self-monitoring. The AI system must keep a constant eye on the problem and reset and fine-tune as needed.
-AI engines must be autonomous decision-making engines: Significant value can only be accomplished when the AI makes and performs wise decisions through trading partners.
-AI engines must be highly scalable: The machine must process large amounts of data very quickly. AI solutions must be able to make smart choices rapidly and on a large scale.
-Must have a way for users to get involved with the system: Users need visibility on the decision criteria to understand the issues that the AI system can not solve. Users must be able to control and override AI decisions when appropriate.
AI Challenges in Supply Chain
The adoption of AI faces many obstacles. Significant capital investment, upgrading of I.T systems and making organizational changes are needed. As a consequence, only the most notable players can afford this. Organizations with old legacy systems face several other significant hurdles to deploying and reaping the benefits of AI.
Despite its potential for added value, there is already concern that AI can replace repetitive and manual tasks resulting in job losses. Companies will need to build plans to answer how workers' studies shift as AI systems automate some manual functions. Besides, there are also protection and safety issues related to IT infrastructure and human life.
What is appealing about AI-based solutions is that they learn and drive continuous improvement over time. Everything we need to do is control the human interface.