The rapidly evolving business landscape and consumer expectations make it imperative for supply chain functions to adapt and improve their performance.
With globalization and shifts in labor markets, supply chain transformations in leading suppliers is underway. Data tells this tale even better. Here are 10 statistics that predict the future of supply chains:
81% of supply chain managers reported that Data Analytics will be crucial when it comes to reducing costs
Data analytics are used to run supply chains smoothly and efficiently. By analyzing data from past processes, supply chains can focus on patterns and predict future requirements. This can help better manage their inventory
75% of large enterprises are expected to employ some form of intralogistics smart robots for their warehouse operations by 2026
Smart Robotics are a specialized form of cyber-physical robotic automation used in warehouse and distribution processes. Leveraging warehouse operations to automation can reward businesses with:
Improved inventory management
Reduced stress on human staff
Reduced risk in dangerous tasks
Low error rate due to automation
Increased operational efficiency
Gartner predicts that by 2026, 25% of companies that make supply chain execution software will change their main program to use microservices, but only 5% of supply chain businesses will adopt composable application architectures
Independently-deployable applications to help complete a business process is known as Microservice in Supply Chain software. Their use ranges from order management to customer intelligence, microservices have a clear advantage over the traditional way supply chain management software functions.
In the year 2026, 80% of companies are expected to suffer a significant loss due to a failure to merge their digital supply chain twin and control tower initiatives
Digital twin is a digital replica of a physical supply chain which helps organizations recreate their real supply chain in a virtual world. A supply chain control tower is a cloud-based solution that implies advanced technologies. Through artificial intelligence (AI), machine learning, and the Internet of Things (IoT), they help manage supply chains.
Through digital twins supply chains can test situations, model different modes and flows and have a better understanding on how their future decisions can impact their network operations.
Merging the two can result in
Visibility across the entire supply network
Connected Environment across all functions
Resource Optimization for humans and machines
Promoting optimal network efficiency
Businesses with optimized supply chains have 15% lower costs, 3X faster cash to cash cycles and less than 50% inventory holdings. Whereas, 79% of companies achieve revenue growth more than the average of their industry when equipped with high performing supply chains
An optimal operation of a manufacturing and distribution of supply chain includes the optimal placement of inventory and minimizing operating costs. By optimizing the supply chain you can have more insight into all activities boosting customer satisfaction and increasing re-ordering and customer retention.
In the next two years, Data Analytics is believed to be one of the key technologies for supply chain management by 40.7% of modern companies
The use of big data analytics to enhance supply chain processes is known as Data Analytics. If used correctly, it enables companies to transform data into actionable reports and visualizations to attain better results by making better decisions in their supply chain operations.
25% of supply chain decisions will be made across intelligent edge ecosystems by the end of 2025
Edge ecosystems are a physical location where things, people and data connect together. An example can be a distribution center. Data communications networks will help artificial intelligence based supply chain decisions. These ecosystems enable decision making as close to the original information.
A Statista report in 2021 reveals that 55.73% of supply chain executives want to invest in production planning and demand forecasting
Production forecasting is the process of estimating future demand and resources required for retail products. Usually, these resources include manual labor, money, machinery, and raw materials. Inventory forecasting helps manufacturing businesses balance their goals by saving on operational costs while making sure customer demands are met.
By 2026, more than 75% of companies that make supply chain management software will include advanced analytics, AI, and data science in their products
A combination of machine learning and intelligence technology is helping industries procure big amounts of data. AI algorithms can analyze data and predict the product demand. An accurate forecasting of demand can take off strain from an overall supply chain, resulting in lower expenditure and increased productivity. It also improves speed in insight discovery, reporting and taking decisions timely.
A Statista report in 2021 revealed that 40.02% supply chain executives want to invest in real-time supply chain visibility
Real-time visibility is complete and accurate information about every aspect of the supply chain at any given time. This kind of visibility is important in making better decisions. It provides supply chain management with the ability to stay visible and gives them power to make business-enhancing decisions without delay.