Are you a supply chain management professional? Then you must have heard about supply chain digitalization. People say the digitalization of the supply chain is the future. It is the key to unlocking or extracting every bit of value from the supply chain for the benefit of an organization.
The digital supply chain landscape is a complex one, however. Therefore, supply chain professionals must keep themselves updated about digitalization frameworks, strategies and technologies — say, by joining virtual events on the topic.
For instance, there’s a virtual event on FastForums, “The Digital Transformation of Supply Chains”, that will provide supply chain professionals with a wealth of insights on the digitalization of supply chains, demonstrate real-world supply-chain digitalization benefits, and provide a venue to interact with supply-chain digitalization thought leaders and practitioners.
At this virtual event, speakers will cover:
- key digitalization trends;
- reason to believe in the digitalization of the supply chain;
- strategic insights on how a company can seamlessly transition to a digital supply chain;
- rebuilding people’s capabilities to align with the digital transformation;
- changing the business model to meet shifting customer demands;
- ensuring the sustainability of the supply chain;
- existing and emerging technologies in the supply chain ecosystem;
- the role of big data in supply chain management;
- business excellence and supply-chain digitalization outcomes.
Table of Contents
Artificial Intelligence in the Digitalization of Supply Chains
The digitalization of supply chains requires the seamless integration of multiple technologies and systems. The internet of things, digital twins, drones, robotic goods to person systems, robotic process automation, enterprise resource planning systems, machine learning, and data analytics are the standard tools and technologies that make up the digitalization technologies ecosystem.
Among these, there’s artificial intelligence, one of the technologies most often floated around in supply-chain digitalization discourses. This is understandable. AI continues to make a mark in all sorts of industries, mainly because of its ability to process massive volumes of data to identify patterns and predict outcomes that would have remained obscure with non-AI-driven analysis.
The data supports this. According to McKinsey, AI integration in supply-chain management has led to an improvement of 15% in logistics cost, 35% in inventory levels and 65% in service levels in early adopter companies compared to their competitors.
AI works. However, despite the proven benefits of using AI in supply chain management, AI adoption rates are less than ideal. It appears that less than one-third of supply chain professionals are using artificial intelligence as a tool for everyday supply-chain management.
The reason for the seemingly slow adoption of potentially revolutionizing AI technology is the disjointed digitalization approach of enterprises. Specifically, companies would often implement digital solutions to automate specific processes, but these solutions are not interconnected. Therefore, human intervention is still needed to make decisions and liaise between interdependent applications.
Companies are also often slow to implement all the technologies they need to maximize the potential of AI applications in supply chains.
To take advantage of AI, supply chain professionals need to actively monitor their inventory, obtain real-time inventory and location updates, and actively track vehicles and pallets, among others. Without extensive instrumentation, active tracking and monitoring, a continuous supply of data, and a system that integrates all data and automates data-driven decisions, a company cannot maximize the benefits of AI.
How AI Can Make a Difference in Supply Chain Management
AI has wide-ranging applications in supply chain management. Here are a few of them.
1. Demand Forecasting and Inventory Management
AI software can handle and process massive amounts of data. AI systems can start spotting consumer demand patterns using multi-channel sales data juxtaposed against external data (say, the weather, season, stock market conditions, inflation, and other seemingly random information).
Testing its demand predictions against historical demand data and widening the net to include more datasets in its analysis can further refine AI demand algorithms and improve its predictive accuracy. Over time, AI systems can become more and more accurate at forecasting customer demands, which will help make the entire supply chain more efficient with its use of resources.
Specifically, a company will become better at managing its inventory, particularly at matching inventory with demand. A company can even predict where its product needs to be at any given time to meet customer demand and improve inventory turnover.
Ultimately, AI in demand forecasting helps a company avoid tying up its assets in excess inventory. At the same time, it ensures no sales are left unrealized because of stocking and unavailability issues.
2. Shipping, Routing and Logistics
AI can help optimize operations. Using data — particularly schedules and rates from logistics and shipping partners, customer orders, fulfilment data, traffic conditions, among others — AI software can decide on the best delivery platforms, delivery routes and fulfilment schedules. AI can even book logistics services, as needed, to meet expected delivery deadlines.
With AI at the helm of logistics and routing operations, companies can improve the cost-efficiency of their operations. Specifically, they can minimize logistics costs but still deliver their products on time.
3. Scenario Modelling and Process Improvements
Supply chain professionals can also use AI software to create a digital twin or a virtual replica of the entire supply chain. Supply chain professionals can use a digital twin to run advanced simulations and system diagnostics.
By creating a digital representation of your entire supply chain (including assets, warehouses, inventory, etc.), it can be easier to spot value leakages and inefficient and wasteful processes. This will help you find solutions to close such leaks and optimize business processes.
Likewise, you can run advanced simulations using AI to assess how much a significant disruption (e.g., the COVID-19 pandemic) will cost your organization. Thus, you can also work on finding ways to mitigate adverse effects, minimize costs and, overall, improve your organization’s resilience against adverse market conditions.
With AI, you can also run simulations of what will happen to your earnings when you increase the number of your warehouses, increase plant capacity, or increase the number of your field employees.
For instance, AI advanced scenario modelling can help an airline assess the costs and benefits of operator-to-operator leasing and compare them to the costs and benefits of the traditional long-term operating or finance lease.
By helping companies visualize the impact of planned changes before actually implementing them, AI helps organizations make better (i.e., data-driven) business decisions.
Artificial Intelligence and the Supply Chain
Artificial intelligence and the supply chain are natural bedfellows. Supply chains are highly complex, and outcomes and business value depend on a wide range of highly variable factors.
AI ensures these factors are given due consideration, and the organization can extract as much value as it can from its supply chain.
Therefore, if you are a supply chain professional, you cannot ignore the AI revolution in supply chain management.
Start joining supply chain digitalization virtual events so you can learn more about the subject, understand AI-enabled supply chain frameworks and hear from industry experts.