Supply Chain Analytics- for Intelligent Manufacturing  – Learnxt

Supply Chain Analytics- for Intelligent Manufacturing 

Supply Chain Analytics- for Intelligent Manufacturing _440 x 280_website (copy)

Supply Chain Analytics- for Intelligent Manufacturing 

Supply chain Analytics aims at strengthening operational efficiency and overall effectiveness by enabling informed data-driven decisions across the organisation. With accurate Supply Chain Analytics, the manufacturers have a real-time update to make the data more strategic and operational that helps them to take more tactical and practical decisions. It also assists the whole supply chain right from sourcing, manufacturing, distribution, and logistics
What are the Challenges for a Typical Supply Chain?
Some of the most common challenges that typical supply chain come across are:
  • Limited synchronisation between planning and execution.
  • Lack of real-time data visibility across the organisation along with human errors in all business channels. 
  • Regular stock-out issues cause massive fluctuation in inventory levels.
  • Lack of flexibility in the network and distribution footprint makes it difficult to prioritise between the cost of setting up systems and the customer service levels.
  • Price volatility and production line imbalances trigger problems in logistics as well.
How does Supply Chain Analytics add value to the supply chain?
According to a recent study conducted by Gartner, around 29% of organisations achieved high levels of ROI by using supply chain analytics compared with 4% that achieved no ROI. Supply Chain Analytics can help manufacturers overcome the challenges mentioned above with a clear, connected, and holistic view of the entire supply chain that includes minimum efforts and investment.
It provides excellent value to the supply chain by:
  • Authorising better sourcing decisions based on supplier performance. 
  • Reducing the chances of future losses by carefully examining the current and future production scale.
  • Providing details of the exact root cause of past events.
  • Effectively discovering development opportunities based on relevant data.
  • Analysing how product changes impact production costs.
Opportunities that Supply Chain brings:
  •  Sales, and overall planning – Usually, sales and inventory are the most data-driven tasks as these require a wide range of inputs from ERP (Enterprise Resource Planning) and other SCM tools. With Supply chain Analytics, manufacturers unfold great potential to redefine the planning process by more effectively using both external and internal sources, thus making real-time demand and supply, shaping a reality. 
  • Sourcing- Often data on procurement volumes and suppliers are also gathered on some specific activities in the sourcing process. However, using accurate supply data manufacturers can also influence the classic spend analysis and supplier performance review. Also, these processes can be analysed in real-time to identify deviations from other standard delivery patterns. 
  • Manufacturing– Big data and analytics are the most excellent and useful tool for manufacturers to help streamline operational efficiency with minimal time and effort. For instance, energy-intensive production runs can be scheduled to utilise the fluctuating electrical prices. Also, data on manufacturing parameters include assembly operations or dimensional differences between parts which can also be analysed to arrive at the root cause of the defects. 
  • Warehousing-Mostly logistics function is cost-centric and organisations focus on technologies that provide a competitive advantage. But warehousing has also witnessed a boom using ERP Data. New Technologies, data analytics techniques are creating new opportunities for warehousing as well. Newly advanced 3D technologies can also help in optimising warehouse design and stimulate new configurations of existing space to improve storage efficiency further and picking productivity. 
  • Transportation- Several Truck operators are already using analytics. From using fuel consumption analytics to improving driving efficiency in deploying GPS technologies to reduce waiting of deliveries to the customer based on their Geo-location and traffic data, they are using analytics to improve their operational efficiency. 
  Main Features of useful Supply Chain Analytics
  1. Connected-The analytics solution should be able to access unstructured data from various resources across the entire supply chain stream without hassle in the connectivity.
  2. Collaborative-  The solution aims to improve the collaboration with suppliers, partners by leveraging the power of cloud-based commerce networks to enable more collaborations and engagement.
  3. Cyber-aware– The end solution should smart enough to protect its systems from cyber hacks and intrusions that should be an organisation-wide concern.
  4. Comprehensive-Analytics capabilities must be well scaled with data in real-time. These insights should well-incorporated with any latency in the value stream.
A report by McKinsey states, “IBM has helped develop links between production planning and weather forecasts for bakeries. By incorporating temperature and sunshine data, baking companies can more accurately predict demand for different product categories based on factors that influence consumer preferences. Amazon, meanwhile, has patented an ‘anticipatory shipping’ approach, in which orders are packaged and pushed into the delivery network before customers have ordered them.”

Role of AI in Supply Chain Analytics

Adding AI into the supply chain creates a bundle of opportunities for manufacturers through real-time tangible benefits. AI helps to create end-to-end supply chain visibility, provides actionable analytics insights, reducing the manual grunt work, and facilities informed decisions for manufacturers. 
In the digital era, the main objective for supply chain analytics is to build a collaborative platform and to bring everyone involved in the supply chain altogether to make more accurate and fast decisions with the changing marketing requirements. 
Navigate towards Supply Chain Analytics with SRM University and LEARNXT:
With the evolution of data structures, data infrastructure, analytics models, and the ability to amalgamate data, supply chain analytics will continue to evolve. Also, AI-based techniques will continue to improve the ability of people to generate accurate, useful, and predictive insights that can be set in into workflows easily.
SRM Group has launched LEARNXT, a global digital learning brand empowering people to achieve their dreams through education. It is the place where you can learn more to be more. Accredited degrees of many universities are offered under the aegis of this brand. This is the country’s one-of-a-kind initiative where the advanced curriculum is clubbed with industry- recommended content. LEARNXT, in collaboration with the country’s top-ranked SRM University, is offering India’s first University accredited Data science and analytics-focused Degrees and Diplomas.
Supply chain analytics work more efficiently with artificial intelligence for intelligent marketing. AI, along with the supply chain, is working in reshaping the industry; it is helping us in reshaping our skills and efficiency as well.
Stay tuned to LEARNXT to know how supply chain analytics is navigating businesses around the world.

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