Machine Learning and Artificial Intelligence in the Procurement Supply Chain
Optimized operations, better decision-making, speed in cutting cycle times, and continual improvement are already some of the best AI features. But in the coming years, AI in the supply chain will make waves like never before.
According to Gartner, supply chain firms anticipate a two-fold increase in machine automation in their supply chain activities over the next five years. Additionally, worldwide spending on IIoT platforms is expected to increase from $1.67 billion in 2018 to $12.44 billion in 2024, representing a 40% compound annual growth rate (CAGR).
So, optimizing productivity by lowering uncertainty is the top priority across sectors in today's connected virtual environment. The need or requirement for the sector to take advantage of artificial intelligence's (AI) prowess in distribution networks and logistics is further underlined by expectations of hypersonic speeds and efficiencies from suppliers and partners of all stripes.
In this article, we'll discuss the role of machine learning and artificial intelligence in procurement supply chains and why they are essential for efficient supply chain processes.
Introducing AI and Machine Learning
The supply chain and procurement sector have been using artificial intelligence and machine learning for a long time. Due to the supply chain management and procurement industries' heavy reliance on data, AI and machine learning algorithms are deemed life-saving tools. They give businesses a lot of computing capacity to handle massive databases.
While they may sound similar, AI and machine learning have different applications in supply chain management.
Machine learning and artificial intelligence use in inventory management is a good example. Moreover, inventory management can make or break a business in this sector. Without a sound system, businesses risk adding to costs, experiencing production delays, and even missing out on potential sales.
Organizations can identify issues like slow-moving inventory and impending stock-outs by processing the currently accessible data using machine learning and artificial intelligence in procurement supply chains.
Decision-making parties must base assumptions in the traditional inventory control model on essential variables, including lead times and market demands. Decision-makers can now evaluate big data sets and plug them into a supply chain model to improve forecasts, thanks to technology. Also, because machine learning makes it possible to recognize patterns or trends, the procedures improve over time.
Here are some further specialized uses of artificial intelligence and machine learning besides aiding in inventory management:
Machine learning and AI systems are frequently used in enterprise-level applications. It's often employed when making important decisions and conversing with clients or consumers.
Automating Purchase Orders
Many businesses have been employing AI to automate purchase orders for a while. Employees are liberated from physical labor and repetitive tasks, reducing human error.
Predicting the Better Supplier
One of the significant issues businesses encounter in the procurement and supply segment is likely locating the best suppliers. To address this problem, Junichiro Mori et al. demonstrated in 2010 how machine learning algorithms could be utilized to build prediction models for supplier selection.
AI Chatbot Support
The supply chain and procurement industries can benefit significantly from chatbot support. It can be used, for instance, to:
- Automate the process of picking up items at warehouses
- Respond to client inquiries
- Monitor shipments from suppliers
- Add GPS capabilities to delivery vehicles, so supervisors can monitor progress and submit new dispatch requests.
Predicting Demand for New Products
Various new products are introduced to the market during difficult times. Machine learning can be used by supply chain and procurement professionals to account for variables that may influence the demand for new products.
Operations Level Applications
AI and machine learning aren't merely pertinent and helpful for business decision-makers. The uses of procurement artificial intelligence at the operations level are illustrated below:
- Autonomous Trucks in a Mining Operation – Australian mining businesses save tons of money by automating equipment such as truck loading. Additionally, it contributes to worker safety, particularly in underground mines.
- AI-driven Agricultural Monitoring – Asia offers a bright future for using AI in farming services. The advancements primarily focus on using AI-driven crop monitoring, soil surveillance, and greenhouse management.
- Predictive Equipment Maintenance – For many manufacturers and suppliers, having a piece of machinery break down is bad news. It can result in the need to replace specialized hardware, supply delays, or, even worse, customer loss. For many organizations, predictive maintenance of equipment is therefore crucial.
- Prediction of Goods in Transit – With predictive analysis, businesses that send and receive goods can deal with fundamental problems before they arise. It can reduce costs and delays.
What Is Procurement AI?
AI is essentially a software program designed to solve a particular problem. It has the potential to drastically alter work processes in even massive firms because it is merely software.
Procurement is changing thanks to AI. Numerous time-consuming processes are being automated or improved by AI, and it is also providing procurement specialists with new insights based on incredibly intricate and substantial data sets.
Types of Procurement AI
Any software solution that includes self-learning and intelligent algorithms can be viewed as AI from the perspective of procurement.
The term "artificial intelligence" (AI) refers to any algorithms that display behaviors that are deemed "clever."
Algorithms are sets of rules that specify how to solve a particular problem and are used in all forms of AI. Anyone with a knack for math can calculate algorithms, and they also serve as the foundation for most computer software.
Software algorithms do not appear to do anything to the human eye, but they can be written and reprogrammed by specialists to address issues deemed crucial in software environments.
Moreover, RPA should not be regarded as AI even though it gives procurement much potential to increase process effectiveness. Consider RPA a software robot that imitates human behavior, whereas AI is a computer program simulating human intelligence.
The Changing Face of Supply-Chain Management
The supply chain connects many operations, such as production, procurement, marketing, and sales. Companies can optimize profits before interest, taxes, depreciation, and amortization (EBITDA) for the business by balancing trade-offs across functions via integrated planning.
The development of supply-chain management is highlighted by the experience of one major building materials firm. The company recently expanded the four dimensions of the objective of its supply-chain function: improve operational sustainability; deliver premium levels of service and integrate demand sensing for short-term changes; incorporate its manufacturing and distribution supply chains; and strengthen the organization. In support of this, the company increased the size of its major supply-chain team and created the chief supply chain officer position, who answers directly to the CEO.
These methods have strained supply chain operations, which now have to work as a "central cross-functional brain" within big businesses. In many supply chain companies, the focus of supply chain management has evolved from merely enhancing local function efficiency to dynamically optimizing the worldwide value.
Sales and operations planning has transformed into integrated business management in several industries (including chemicals, agribusiness, metals, and mining). Moreover, the COVID-19 pandemic's recent supply shortages and the spike in demand they caused have made it even more critical for supply chain companies to strengthen their central and demand planning capabilities.
So, supply chain, business strategy, or procurement teams must be more significant and relevant to improve performance. Supply chain companies must also deal with the following issues:
- Foreseeing demand across various product categories and regions.
- Dynamically determining trade-offs with various technical limitations and hundreds or thousands of interconnected variables.
- Using AI-based solutions to manage the broader value chain, such as processing or supply chain optimization, preventative analysis, or master data integrity.
- Ensuring that plans are carried out and flexible enough to respond quickly to variable impacts (such as demand shocks, production halts, and transportation disruption).
How Can You Use Machine Learning in Procurement?
The following is how you can use machine learning in procurement:
Use Artificial Intelligence in the Procurement Process
It is challenging to modify your procurement processes to employ AI. So, when putting the algorithms into practice and creating the functionality, you need to have a plan and go forward incrementally.
We'll review machine learning experts' rules for using AI solutions in business operations.
Choose a Modest Task
Start by examining your procurement procedure and determining which processes and tasks can be automated and improved. Finding simple tasks that take up a lot of staff time is an excellent beginning strategy.
Existing processes will work well with artificial intelligence. Therefore, improving the process and adding value to the operation will arise from adapting the method to the technology.
Model Massive Amounts of Data
The more data you need to examine, the more your approach will be reliable. However, having updated, high-quality data is just as critical as ensuring data quantity.
So, gather or collect as much data as possible before beginning the artificial intelligence installation. It will be simple to train the model and quickly arrive at the desired outcome.
AI algorithms can analyze previous data and provide fresh perspectives.
Create Specific Tasks for AI in the Procurement Sector
Start by assigning your simple model tasks. Your model will perform better and produce better results if you offer more transparent data and instructions.
Always Rely on Input From People
Even though it can improve the accuracy and efficiency of your procedure, consider human input into all operations and processes. That could come from the data's information extraction or even more obvious points.
How Does Artificial Intelligence Contribute to SCM Organizations?
Professionals in the supply chain industry are using artificial intelligence (AI) to solve significant problems and enhance international operations.
Supply chains are using AI-enhanced solutions to become more efficient, lessen the effects of a global workforce shortage, and find better, safer ways to move commodities from one location to another, increasing the performance of supply chain companies.
Applications for artificial intelligence can be found all along the supply chain, from the factory floor to the customer's doorstep. Shipping businesses use the IoT to track the mechanical condition and permanent location of pricey vehicles and other relevant transportation instruments and collect and analyze data about the products being shipped.
Retailers who serve customers are utilizing AI to understand better their key demographics and forecast future behavior. The list goes on — there's a strong possibility that AI is being utilized to improve, refine, and evaluate supply chain performance everywhere.
The advantages of AI in supply networks vary in their degree of tangibleness. For instance, analyzing the effects of predictive modeling based on supply chain data can eventually be beneficial. Still, some businesses claim that the introduction of AI into supply chains directly caused revenue shifts.
Sixty-one percent of executives who have incorporated AI into their supply chains claim lower costs, and more than 50 percent report higher revenues, according to recent research by McKinsey & Company. Furthermore, more than a third of research participants reported revenue growth of over 5 percent.
5 Examples of AI in Supply Chains
1. Demand Prediction Is Enhancing Inventory Supply and Demand Control
Machine learning is used to find patterns and significant factors in supply chain data through algorithms and "constraint-based modeling," a mathematical technique where the results of each choice are bound by a minimum and maximum range of constraints. Warehouse managers may now make considerably more informed judgments about inventory stocking thanks to this data-rich modeling.
This kind of big data, predictive research, is revolutionizing warehouse managers' management inventory by offering levels of knowledge unattainable with conventional, human-driven procedures and unending, self-improving demand forecasting cycles.
The Inventory Optimization platform from C3 AI, which provides warehouse managers with real-time data on stock levels and details about parts, components, and finished goods, is powered by AI. Based on information from production schedules, sales orders, and vendor shipments, the platform generates stocking suggestions as machine learning tools get more experienced.
2. AI Is Improving Delivery Logistics and Routing Efficiency
Companies that don't grasp delivery logistics risk slipping behind in a world where virtually everything can be ordered online and delivered within minutes. Customers today expect speedy, accurate shipment. When businesses cannot meet those expectations, they are only too pleased to look elsewhere.
Around 40 percent of consumers who tried delivery services for the first time during the COVID-19 outbreak say they plan to continue using these services indefinitely, according to McKinsey & Company. Customers have a wide range of options in large markets like New York and Chicago.
Delivery logistics is a complex, detail-oriented industry. This Economist article breaks down some of this complexity by highlighting the "devilishly complicated" task of transporting 25 parcels via van, where there are 15 septillion different routes that could be taken.
The most effective routes are created from all possible routes using AI-driven route optimization portals and GPS tools powered by AI, like ORION, a company used by logistics leader UPS. This task is impossible with conventional methods because they are insufficient for thoroughly analyzing the numerous route possibilities.
3. The Health and Lifespan of Vehicles Are Increasing Thanks to Machine Learning AI
Data from IoT devices and other sources collected from in-transit supply chain vehicles can offer priceless information about the condition and longevity of the costly machinery needed to keep commodities moving through distribution networks. Machine learning generates maintenance suggestions and failure forecasts based on historical and current data. This enables businesses to remove vehicles from the chain before performance concerns cause a snowball effect of delays.
Uptake, a Chicago-based company, analyzes data using AI and machine learning to forecast mechanical breakdowns for cargo containers and means of transport, including trucks, autos, railcars, tractors, and planes. The company's forecasts, which can significantly minimize downtime, are based on data from IoT devices, GPS data, and data derived from previous vehicle performance.
4. AI Insights Are Adding Efficiency and Profitability to Loading Processes
The 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. Art and science must identify the quickest, most effective ways to load and unload cargo onto trucks, ships, and airplanes.
Companies like Zebra Technologies provide real-time visibility into loading operations using a combination of hardware, software, and data or predictive analytics. These discoveries can be utilized to maximize interior space in trailers, cutting down on the amount of "air" transported. Zebra may also assist businesses in creating processing methods for managing more rapid, less dangerous, and effective parcels.
5. Supply Chain Managers Are Discovering Ways to Reduce Costs and Boost Revenue Using AI
The cost of shipping goods around the world is high and rising. According to the Drewry World Container Index, the cost of shipping goods grew by 12% in 2020, the most in five years.
Supply chain managers at high-performing procurement organizations like Echo Global Logistics utilize AI to manage carrier contracts, secure better freight 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 component of the supply chain.
Supply chain advances using AI are laying the groundwork for when we might finally anticipate deploying autonomous, AI-powered automobiles.
The cost and efficiency of a global supply chain that is becoming more and more complex will continue to be improved by the data that these platforms are mining and analyzing, enabling supply chain managers to make supply chain planning processes more efficient, reduce logistics costs, and improve enterprise resource planning.
Best Practices in AI Procurement
The following are some of the best practices in AI procurement:
Begin with the Monotonous Processes
Don't expect AI to completely transform the way you manage your procurement and supply chain operations at first. Do not consider AI to be some great new technology. Think of AI instead from the perspective of business processes.
Consider the laborious yet necessary company activities that require managing time and resources. The initial benefits of AI will not come from new applications but rather from integrating technology into current workflows, such as by enhancing your expenditure analysis or contract management procedures.
Gather All Available Procurement Information
Another common rule is to gather as much information as possible about procurement and supply chain processes before you use them. Don't hold off unless your data quality is flawless. Assume that advances in AI technology will enable you to better understand and interpret historical data over time.
The secret is to gather more information for AI to analyze. Better outcomes depend on how much data you allow AI to learn.
Clear the Procurement Obstacles for AI
AI and machine learning are currently excellent at specific use cases. Machine learning can be used to classify procurement expenses according to invoice line items, but it is unlikely that AI will handle complex supplier negotiations.
So, determine which commonplace chores take up much of your procurement team's time but affect performance.
Be Willing to Try New Things
Although AI has the potential to enhance procurement performance eventually, there are currently numerous unknowns.
So, be willing to try new things. Give emerging AI technology experts problems to solve and train data samples. Consider learning from failures, and keep your eye on the anticipated business advantages.
Moreover, recognize that technology is advancing quickly and that tomorrow's failed trials may be doable with today's cutting-edge AI techniques.
Embrace Human-Machine Interaction
Finally, keep in mind that every AI in procurement deployment will need active direction and support from procurement professionals. Plan for human-machine collaboration, with artificial intelligence enhancing, not replacing, the knowledge of your procurement department.
To best utilize both human and machine intelligence, lead the transformation.
What Are Our Predictions About Procurement?
We can draw some conclusions about what will be achievable shortly for AI systems and procurement, although no one can actually predict where we will go in the next 10 to 20 years. The analyst community has reached a fairly solid consensus that existing apps will continue to advance.
Processing payments and invoices, arranging and receiving orders, and managing demand for purchases are all activities that can be automated, according to McKinsey. Many of them already are.
However, automation is more challenging for vendor negotiation, recruitment, and management. Human intervention is often required due to constant errors. So, don't expect to have all of your tasks automated anytime soon, even though we will see great automation of simple jobs.
AI will make suggestions and take actions based on data from all players in the ecosystem, not just on the data of one particular participant. Although they are just hypothetical possibilities, they might represent the ultimate manifestation of the available AI applications.
The purchasing function's costs and the cost savings it produces are compared to determine the procurement ROI. These savings can also support investments, R&D, better customer experiences, sales training, sustainable offerings, and other initiatives — where AI and machine learning can boost the effectiveness of procurement.