In any supply chain, efficiency has always been of the utmost importance. The pandemic has only emphasized the importance of being fully optimized and ready to adapt to sudden increases in demand or market changes. Artificial intelligence (AI) and machine learning (ML) have been used by businesses to fill operational gaps and improve supply chain optimization. However, the adoption of such technology has stalled in the last year, with only 12% of supply chain professionals reporting that their company uses AI-based solutions in their operations.
Supply Chain Optimization Problems
The Struggle To Keep Unstructured Data Up To Date In The Absence Of Automation
One of the primary causes of this plateau is that supply chain optimization teams have to manually sift through massive amounts of unstructured data to generate insights on which to base business decisions.
On the other hand, businesses frequently fail to establish systems that allow them to collect this data through software-based object recognition, image tagging, and text recognition. Even with the introduction of the Internet of Things and advanced data collection software, 62 per cent of companies still have limited visibility of their supply chain, and only 6 per cent have complete visibility.
Market Forces Are Constantly Shifting As A Result Of Changing Customer Demand
Maintaining consistency in operations as customer demands change and evolve is a significant challenge in logistics management. Surface enables supply chain optimization executives to confidently predict vital operational metrics and make strategic decisions to improve profitability and efficiency.
Manually accomplishing this can be not easy. Human error and fluctuating customer demand are examples of dynamic variables that can change dramatically in a short period. AI can assist in closing this gap. However, AI systems designed to generate insight frequently perform best when built on well-defined models with consistent labels and rules. This can be a difficult commitment for companies with dedicated data management teams.
The Impact Of Intelligent Software On Supply Chain Operations
The fully integrated and automated systems provide a comprehensive view of the supply chain. It can be challenging to provide customers with virtually limitless options and maintain supply chain optimization. This is due to the complexities of each product line’s storage and delivery requirements.
Comprehensive computer vision datasets assist business leaders in developing holistic supply chain overviews and identifying patterns ripe for optimization. Amazon, in particular, has been able to reduce the environmental impact of its product packaging by utilizing machine learning. In this case, intelligent software-enabled supply chain managers to identify commonalities across a wide range of product categories and delivery methods.
Finding A Happy Medium Between Business And Customer Needs
The pandemic highlighted the importance of businesses identifying their customers’ needs and adapting quickly. As COVID-19 drove customers to purchase more essential products like toilet paper and hand sanitizer, supply chain optimization was needed to ensure adequate inventory and efficient delivery. The same managers would have to adjust inventory levels to match decreasing customer demand in a different scenario. When properly trained, AI-based solutions can fill these information gaps by combining data from multiple sources and databases to create an accurate situation report, which can then be translated into a supply chain strategy.
How To Use AI And Machine Learning To Generate And Analyze Insightful Data?
Gather And Organize The Appropriate Types Of Data
Assessing the appropriate metrics can help increase the reliability of their insights. This means that business leaders must collect the proper types of supply chain data and ensure that data collection is consistent across the chain.
While data collection from within the supply chain optimization is critical, sometimes, the data required to train models is unavailable or does not exist in-house. In those cases, it can be supplemented with open-source datasets obtained from a third-party vendor, or synthetic data can be generated.
Companies that use AI-based solutions to collect operational data can improve operational efficiency and streamline administrative processes. According to 79 percent of executives, AI systems simplify workflows and help them maintain high levels of efficiency across the business. Adaptability is a critical asset in a business environment that is constantly changing.
Use Precise Annotation For Model Training
Systems rely on consistent data labelling to identify and analyze trends when training and implementing AI models in supply chains. Business leaders must use precision annotation to eliminate exceptionally subjective data ambiguity in the supply chain.
Precision annotation allows Artificial intelligence (AI) and machine learning (ML) models to be trained using a single source of truth to reduce bias. Even global technology behemoths like Microsoft and Google have recognized the importance of eliminating this bias and have recently focused bug bounties on AI-specific challenges. The use of precise video and image annotation early in the model training process enables supply chain optimization executives to anticipate these challenges and counter them with unbiased and accurate annotation for all items in their inventory.
Market Trends Can Be Forecasted Using Advanced Data Analyses
Predictive analytics has increased in recent years and will continue to grow in the future. According to a recent report, 56 per cent of supply chain operations that are not currently using predictive analytics will do so by 2025. Unexpected market changes and evolving business environments can wreak unoptimized supply chains. Recent events, such as the spread of new coronavirus variants, can influence customer behavior. Intelligent software assists business leaders in identifying and responding to changing trends. This helps reduce the financial impact of wastage and operational inefficiencies on the supply chain.
For business leaders to reap the full benefits of predictive analytics, there must be a solid data-driven foundation to make these AI-powered decisions. When suitable data types are tagged, organized, and fed into intelligent software, business leaders can predict customer demand, anticipate supply chain challenges, and effectively respond to market trends.
AI and machine learning will eventually find their way into every aspect of digital supply chains. However, business leaders must lay the proper data foundations for these technologies to be as effective as possible.