3 Great Examples of Data Analytics for Logistics done right!
When we say the term, Data Analytics, we mean to say analysis of huge data. Data, as the term replicates huge chunks of data gathered from across all sectors and analytics is the adoption of significant tools that help gain relevant insights from the collected data. Major implementation of Data Analytics has been in the supply chain and logistics sector. The industry, until recent times, was based on age-old machines, equipment, and processes. This not only hampered the productivity but also became the reason behind falling face of the same.
A silver lining here was digital transformation. The last few years witnessed a massive shift in the growth of the supply chain and logistics industry. Much of this has been the impact of data analytics. Let’s walk through 3 examples depicting the correct application of Data Analytics in Logistics.
Supply Chain Visibility
Data analytics is one such technology that helps track products and machinery in real-time. Right from the production phase to the last mile distribution of goods, one can manage and monitor vehicles and keep track of the shipment. Continuous monitoring of devices leads to better delivery and improved status of shipment. This in a way promotes the efficiency of the supply chain and also enables an environment where leaders or the stakeholders can gather supply chain information better and faster. As an illustrative example, check how UK’s leading Food Service specialist gains real time visibility over its supply chain using a robust IoT & Azure Solution. The supply chain management solution integrated with a mobile app helped ensure effective communication between the fleet managers and drivers.
Predictive analysis is believed to be one of the major implications of data analytics in logistics. Today, companies can study and analyze behavioural patterns of machines which in turn account for detecting anomalies. Organizations have leverage over the behavioral changes that deter the functioning of the machines. What this means is companies can make use of predictive analysis and detect instances such as weather changes and deal with them better.
Also, predictive analytics plays a crucial role in maintaining a balance between demand and supply. Using past data and existing models, shippers can effectively generate reports on the consumption and predict what would be the demand. This in a way accelerates delivery and reduces wastage. For an example, read how a premier Logistics solutions provider utilizes vehicle's diagnostic data for tracking and preventive vehicle maintenance. Their Azure based IoT fleet management solution handles more than 5000+ vehicles and improves overall fleet operational efficiency.
Finding the best possible path from point ‘a’ to point ‘b’ is what we call as route optimization. This reduces the time taken to deliver a package and also, improves the efficiency of the system. Similar is the case with logistics route optimization. Gathering information from across all sources put up piles of data. Right from the GPS to weather, fleet information and the delivery schedules, all add to the system which is then used to predict the optimal route of delivery. As an illustrative example, read how a leading Food service specialist unlocks actionable insights through Microsoft Power BI based Business Intelligence solution. The solution allows the company’s supply chain & operations team; to track & manage their food delivery operations efficiently. The interactive Power BI supply chain dashboards unlock critical business insights on Temperature Threshold, Driver Performance & Customer behavior to support the team in taking intelligent actions
The Final Word
It is no surprise that the industry is growing and sooner a time would come when the entire segment would rely on data for executing every business operation. Then, not just the logistics industry, but most of the big service/product sector businesses will count on on their data analytics consulting team and choose to utilize the power of data analytics while trying to grow.