An organisation’s supply chain is a critical business process that is crucial for a successful customer experience. A high-performing supply chain enables business efficiency and responsiveness. Hence, customers get what they want, when and where they want it — in a way that is both profitable for the organisation and is sustainable.
In a sustainable supply chain management (SCM), integrating the economic, environmental, and social aspects is necessary to enable an easy flow of information and data. As meeting customer expectations effectively is becoming challenging, logistical performance and the interrelationship between supply chain members are becoming critical factors in supply chain management.
Small and medium-sized enterprises (SMEs) play an important role in supply chain management as they engage in value-creating activities. They supply raw materials, manufacture products, and deliver finished products to customers. Many SMEs providing support to large enterprises in global business networks suffer from inefficiencies by not taking SCM systems seriously. As a result, they are losing market share and, ultimately, money. Since only 25% of small and medium-sized enterprises have an operational supply chain management strategy, quality, delivery time, and cost savings give organizations a competitive advantage to survive in the market.
The interrelationship between logistical performance and supply chain elements is becoming an essential factor in supply chain management. Sectors such as warehousing and modern machinery can give an edge over competitors. The need for more sustainable and better-interconnected elements became visible in some parts of the country when the world was completely shut down. The COVID-19 scenario required coordination between authorities and the general public.
A critical factor in optimising SCM is having the proper IT infrastructure to support the operational (warehouse and transportation management) and the strategic side, like analytics or business intelligence. Real-time contextual and geographic data can help companies gain more control over their internal operations and thus gain a competitive advantage.
Below mentioned are a few ways in which Machine Learning and Location Intelligence can help optimise business practices:
Machine Learning models like Support Vector Machine (SVM) and Logistic Regression can extract hidden patterns based on the demand of goods in the consumer industry, thus can help businesses with demand prediction at the given point of time. This, in turn, also enhances inventory management and reduction in storage and inventory costs.
Algorithms based on image segmentation can help logistic hubs scan product defects, detect faults accurately, eliminate human errors, and reduce manual effort by creating differentiation between the product’s various components. Machine learning can also detect anomalies in machinery by monitoring the vibrational movements and help detect wear and tear, thus preventing critical failures.
Location intelligence comes from visualising and analysing volumes of location technology and empowers holistic planning, prediction, and problem-solving. It provides fleet managers with more significant insights into operations, real-time traffic, and weather conditions. This helps them cope with diverse topographies, complex road situations, track driver behaviour, and additionally helps in mitigating accident rates. With the help of predictive alerts using technologies like aerial and satellite imagery, cloud-based location algorithms, and HD maps, fleet managers can plan out efficient delivery routes. Using geo-coordinates and interactive HD maps, location intelligence allows companies to calculate accurate shipment ETAs, ensuring that drivers are on the optimal course, which can help cut costs.
Supply chains can be fully optimised by segmenting based on travel time, tolls, and fuel costs. Innovative tracking platforms provide information on the availability of the nearest packing centers, monitoring the location, thus eliminating key challenges such as unpredictable customs delays, lead times, loading time, and inventory.
“The goal is to hit the sweet spot of maximum value optimization, where risk is balanced against excessive caution.”