The integration of Artificial Intelligence in the supply chain is quickly changing how executives think about their strategies and operate their businesses. AI-powered data analytics, like predictive and prescriptive analytics, can boost your forecast abilities and lead to better, data-driven decision-making.
The Role of AI and Data Analytics in the Supply Chain
In the last decade, computational tools for business performance have risen in popularity and relevance, primarily due to the emergence of Big Data. However, with the boom of artificial intelligence, these tools increased their efficiencies exponentially, and new ones emerged as game changers.
The supply chain is one of the business areas most affected by data, analytics, and the development of Artificial Intelligence. Executives all over the world are experimenting with Supply Chain AI and starting to reap the benefits it can bring to those brave enough to leap.
Leveraging data analytics and supply chain AI efficiently is one of the most extensive opportunities and challenges companies face today, and those able to do it will unlock a new world of possibilities.
Let’s dive a bit deeper into the world of Supply Chain AI and how it can supercharge data analytics. In this article, we will focus primarily on two powerful tools at the hands of managers for supply chain optimization: predictive analytics and prescriptive analytics.
What is predictive analytics?
Let’s start with the basics. Harvard Business Review defines all types of data analytics as
“The practice of examining data to answer questions, identify trends and extract insights, to provide executives with the information necessary to strategize and make impactful business decisions.”
Predictive analytics, in particular, uses data to predict future trends and events. Simply put, it strives to answer the question, “What might happen next?” It mainly uses historical data to make assumptions about the future, both near and distant, and it is beneficial for demand forecasting and scenario planning.
Predictive analytics can be conducted manually, but the advancements in extensive data machine learning have exponentially expanded their capabilities; AI-powered predictive analytics can mine massive datasets and provide insights otherwise unattainable by human beings alone.
What is prescriptive analytics?
Prescriptive analytics has been called by Forbes the future of data analytics because it goes beyond making predictions; it can recommend a course of action.
Prescriptive analytics uses data to determine the optimal course of action, making it a valuable tool for data-driven decision-making. Like predictive analytics, it is based on data but considers a broader range of factors and then uses “if” and “else” statements to make recommendations.
Machine learning algorithms benefit prescriptive analytics as they allow data analysts and executives to go through large datasets faster and more efficiently than ever before.
Predictive and Prescriptive Analytics as Supply Chain Solutions
A resilient and optimized supply chain can impact businesses’ success in today’s complex, fast-paced, competitive scenario. As pointed out by McKinsey, having a data-driven approach to supply chain management has proven to be particularly successful in enhancing visibility, improving operations planning, and increasing supply chain resilience overall. Companies in virtually every industry are quickly realizing this and focusing on the opportunities offered by new technologies.
According to the 2024 MHI Annual Industry Report published by MHI and Deloitte, 55% of supply chain leaders are increasing their supply chain AI investments, with 88% saying that they are planning to spend over $1 million and 42% planning to spend over $10 million.
According to the report, the technologies that receive the most interest are those aiming at increasing supply chain visibility and resiliency toward shifts in the market and disruptions. Supply Chain AI can power advanced data analytics that helps companies achieve precisely these goals.
With machine learning tools, Supply Chain AI can boost predictive and prescriptive analytics and give companies a real competitive advantage by harnessing the true power of data.
While Supply Chain AI offers massive benefits while at the same time cutting operational costs, implementing the technology can be complex and expensive, especially when it involves training machine learning models on proprietary data; this is way partnering up with data service providers like Tredence can help you overcome initial obstacles on your way to digitalization.
How Supply Chain AI can boost inventory optimization and demand forecasting
We have already seen that predictive analytics predicts future trends such as consumer demand, competitors’ moves, exchange rates, and many other essential variables. Predictive analytics solutions mainly rely on regression analysis and statistical modeling of historical data and can visualize results on a dashboard.
The main applications of predictive analytics in the supply chain are:
- Demand forecasting. Demand is never linear, therefore posing a key challenge for companies. Through analyzing past and current trends, predictive analytics can identify and predict trends and foresee surges and drops in consumer demand.
- Pricing strategies. By accurately forecasting demand for a product, it’s possible to adjust prices dynamically according to market changes and perceptions.
- Inventory management. Another direct consequence of demand forecasting is the ability to determine optimal inventory levels to satisfy demand while minimizing stock and costs.
Prescriptive analytics builds on predictive analytics, as it works on the results produced by the latter and takes them to the next level. It is precious in empowering supply chains, as it allows executives to answer questions like:
- What do customers want?
- What type of offer would perform best?
- What should be the ideal shipping strategy?
- How much should we scale up the business?
Prescriptive analytics is beneficial when it comes to designing the supply chain network. The size and complexity of today’s supply chains require stakeholders to make multi-variable choices, such as the cost of labor, logistic networks, locations, etc. Prescriptive analytics makes these choices more accessible and data-driven, eliminating the risk of human errors and optimizing time and costs.
In particular, prescriptive analytics plays a crucial role in supplier management. Due to its ability to analyze multiple factors, such as location, financial stability, and past performance, it can assess the risk associated with different suppliers, identify the most suitable ones, and suggest strategies to reduce risks, like diversifying the supplier base or renegotiating contracts.
Supercharge your supply chain with advanced data analytics
AI-powered predictive and prescriptive analytics are the future of supply chain risk assessment, demand forecasting, inventory optimization, and overall supply chain management. Supply chain systems powered by Machine Learning algorithms and tools can discover patterns and correlations within data sets that would otherwise remain invisible to humans or non-AI systems. This leads to better and more accurate forecasting, predictions, and decision-making, leading to more efficient inventory supply chain management.
Supply chain executives worldwide are already winning with predictive and prescriptive analytics. Companies must act quickly to avoid being left behind and losing the opportunity. AI-powered supply chain analytics solutions that drive visibility, minimize risks, and optimize costs will completely step up your supply chain strategy and lead you to the competitive forefront.
With a solid AI-powered digital supply chain solution to support and enhance your strategy, businesses can turn disruptions into opportunities and explore better supply chain analytics, resilience, and growth.
