How Data Driven Technologies are Transforming Logistics and Supply Chain Industry
The logistics and supply chain industry has been so scattered and unorganized for many years in the Indian logistics market and still continues to do so. Technological logistics startups and other companies have started to make use of data driven technologies such as Big data, Analytics, Internet of things (IOT), sensor technologies and other machine learning algorithms to handle real time analytics of huge data which are unstructured. Although there are many factors which are affecting the logistics and supply chain management, it is important to understand the applications of data driven technologies to simplify the complex aspects in data associated in supply chain management.
The other aspect which is important on dealing with large messy and unstructured data in warehousing is forecasting. The forecasting not only tells you a specific action to be done at a certain criteria, but also predicts the outcome in the near future and alerts you to take proper steps accordingly. For example in warehouses, the digital cameras are usually used to monitor the levels of stock and provides alert when next set of stocking is required. However, if the data is fed through machine learning algorithms to train an intelligent stock management system to predict when a next set of supply is required. Therefore, the bottom-line is supply chain management can not only be made organized, linearly structured but also be automated efficiently with the help of data driven technologies. Logistics personnel such as Supply chain managers will rely on alert-based decision making for operations to be done effectively and reduce costing. There is a pertinent and immediate need for logistics companies to implement digital data driven technologies in the supply chain. The implementation of data driven machine learning, Big data, Analytics technologies has impacted significantly in various organizations’ supply chains for helping to deliver on expectations for realizing business value.
Using these technologies some logistics startups in India are prepared to disrupt the market by creating great value in the sector. It is obvious that these value creation to the logistics market is certainly getting traction from Venture Capitalists and Angel investors to fund the logistics startups and be part of the growing revolutionary logistics market. In terms of speaking of business aspects with the usage of digital data driven technologies, Machine learning algorithms and Big data analytics it has paved with opportunities to significantly improve operational efficiency, reducing overall costs, and decision making across the supply chain. Ultimately it is not only helping the logistics companies and other companies get the business intelligence but also provide efficient services to the customers. Risk is inevitable in the fast growing business change. It is important that the supply chain must adapt constantly to unforeseen change. Marketplace plays a minor role in the shift and also due to shifts in world events. Another unavoidable source of risk is rapid change in demand levels — witness the plummeting sales of light trucks, vans and SUVs that automakers are experiencing as fuel prices explode off the charts. As organizations have moved to demand-focused supply chains, they’ve become much more serious about their demand data, and forecast analytics has become an area of intense development activity. The core issues are how to acquire relevant data about orders and other future demand indicators, then how to leverage that into the supply chain planning and execution processes.
Once organizations integrate big data analytics strategies into their operations, it is important that they also update their talent strategy, contracting, talent to leverage the power of analytics. With the help of analytics, automation is done for regular supply chain decision making-related tasks. By automating the massive amounts of data from various sources across the supply chain will lead to increased operational efficiency. Automating data ingestion will help enable increased use of business intelligence for improved decision making. As far as the analytics is concerned, there are key aspects on supply chain to be impacted : They are Data, Insights, Visualization, Machine and Security.
Big data and other technologies are currently efficiently capturing, integrating, storing and leveraging supply chain data to obtain meaningful insights for getting business intelligence. Insights Analytics is pushed into supply chain operations at important decision making and it will create strategic pointers for driving organizational and cultural transformation. Visualization tools are enabling real-time data visualization and creates great interactive user experience for insights of real-time data, identifying issues and instant cause analysis. For machine learning, computing, Internet of Things (IoT) and robotics, they are impacting all supply chain functions, automating decisions and driving a host of operational improvement from asset utilization to customer updates to service and enterprise agility. In Security, Supply chain analytics plays important role in overall supply chain security as a structured, open and connected ecosystem of players that creates a dynamic, quickly shifting environment with new risks to manage.
Data-driven decision-making is certainly an increasing trend in the supply chain and it is is essential to the future success of supply chain activities and processes. Among the biggest challenges that come with increased visibility and more data is determining how to best use that information to drive improvements that benefit the customer. With the logistics companies in India such as Rivigo, BlackBuck, Sendit and other technology focused firms making use of data for better optimization of processes with the above technologies will definitely help the Indian logistics organized and structured benefiting the businesses across sectors depending on logistics and contributing more to economic growth of India.