Real Time Supply Chain Analytics

Real Time Supply Chain Analytics

By Sairam Bollapragada & Rajesh Mohandas

Supply chain managers have seen increasing challenges to create, and keep, efficient and effective supply chain methods, Customer Service, Operating Cost Control, Planning and Risk Management, Inventory Control, GTM Speed, Supplier / Partner relationship management, Green Supply Chain and Talent (Resources) are a few burring issues that are on the top of the charts that burn the midnight oil. A survey by McKinsey says the companies who have already engaged in leveraging Digital Technologies have managed key trade-offs on barriers to better performance : raising risk, lack of collaboration and low CEO involvement.

The fall of this decade has seen major shift in the supply demand dynamics, the traditional supply chain solutions are not equipped to handle complex scenarios due to lack of visibility. There is a competitive demand for real-time responsiveness which can be addressed by the combination of Data Science and Emerging Technologies connecting customer service with Social and Mobile platforms that are cloud enabled on one hand and strengthening visibility into operations with IOT based real-time analytics. The icing on the cake is emergence of machine learning which is all set to address the tactical challenges and give signature ready recommendations for decision makers to gain maximum mileage.

Supply chain management will gradually be disrupted by the rising adoption of IoT and advanced analytics.  The challenge faced by the supply chain practitioners and players is inability to take advantage of technology to the fullest extent while they are trying to simultaneously integrate their supply chain systems across a much wider range of information sources due to lack of 360 degree view of both business and customer.

  1. Strategic Planning: The very first component of the supply chain, Strategic Planning, comprises of Strategic supply design and Strategic sourcing. Here real time analytics can be used for Supply Chain alert monitoring and the insights will flow into Long term planning, Bid Management, Contract management, Catalogue management and Source determination, real time information can be accessed by decision makers that are geographically dispersed to take collaborative decision on a cloud enabled mobile application. 
  2. Demand Planning: The Next component of the supply-chain value stream is Demand Planning the three crucial capabilities here are Forecasting, Promotions and Demand Consensus. Advanced real-time analytics help with Macro calculations and planning bill of materials leading to collaborative demand planning and characteristic based forecasting.
  3.  Supply Planning: The heartbeat for any supply chain is the supply planning which comprises of capabilities like Safety stock planning, supply network and outsourcing decisions, Distribution planning, Customer and Supplier collaboration. The best component in the value chain where Emerging technologies can be used with the fullest potential is here, the power of cloud enabled CRM systems and Communities connecting the enterprise giving a 360 degree view to the entire ecosystem including the suppliers, customers, partners and the enterprise following with the power of advanced analytics and big data enabled deep learning algorithms come handy. Some examples where one can leverage advanced real-time analytics are in the area of multi-level supply demand matching, whereas the artificial intelligence neural network algorithms can be plugged into supply network planning.

Procurement, Warehousing and Order Fulfilments are three equally crucial parts of the supply chain operations that are interlinked and often become bottleneck in the supply chain due to lack of visibility. In a connected supply chain environment the intelligent algorithms need to be plugged in here such that all three areas are interconnected and all repetitive processes can be automated through RPA, what with NLP loaded Analytics providing intelligence and insight into the process.

4. Transportation: The last component of the value chain is Transportation. Often the biggest complaint from the players of supply chain is “Visibility to the Tail”. This is already being accomplished by real time analytics, bringing in a predictive model that combines features like load consolidation, intelligent route optimization, carrier selection and shipment trending which are few elements of the Transport planning while the Transport execution can feed in data from the shipping interfaces capable of distance determination services. The last inputs come from the freight-costing component that strengthens the predictive model and the machine learning algorithm continuously churns the data and gives real-time insights for both strategists and operations.

Hence, Analytics working in tandem with Cloud, Mobility, and AI, can play a very critical role to bring in great value add to the Real Time Supply Chain scenarios for all those who embrace and leverage. This is even more critical with markets moving towards a more C2C value chain where the customer experience and expectations are fast changing. Retailers, Warehouses, Suppliers, Logistics, and all the agents in the Supply Chain have to work in an orchestrated mode need to be on their toes to remain competitive and relevant to the market expectations.