Introduction

Data scientists start the supply chain analytics process with inputs regarding factors like inventory, cash flow, service levels, waste levels etc. critical to building a predictive model, which is then tested, validated and tweaked into a real-time supply chain analytics examples model for the business processes and built on effective co-relational factors. A robust model is built with the data engineers, scientists and business users working in tandem. The model is allowed to improve as it ages with exposure to the business processes with the rectification of glitches and improvements along the way affected in real-time to the supply chain analytics meaning.

In this article let us look at:

  1. Definition
  2. Types
  3. Software
  4. How Supply Chain Analytics work?
  5. Features
  6. Uses
  7. History
  8. Future Trends

1. Definition

What is supply chain analytics? SCM-Supply Chain Management uses as an essential part of supply chain analytics, which means the set of processes used by enterprises to gain foresight, insights, predictions etc., from data in the supply chain like data from goods distribution, procurements, processing information etc.

2. Types

The 4 types of supply chain analytics are based on the 4 capabilities of Gartner’s model, namely diagnostic, descriptive, prescriptive and predictive.

  • Analysis of descriptive supply chain models uses reports, dashboards, statistical methods of interpretation etc.
  • Analysis of diagnostic supply chain models uses cause-analysis methods to figure out the why and how of events.
  • Analysis of predictive supply chain model provides foresight, insights, predictions etc., that are data-driven.
  • Analysis of the prescriptive supply chain model helps decide the best-course, automation decisions, evaluation of optimization or logic with embedded decision choices.

The function and form division of supply chain analytics in a consultancy firm’s end-to-end, the ongoing process may have divisions for 

  • Workflow
  • Collaboration
  • Structured data management
  • Decision support
  • Unstructured text mining

Other divisions based on technological improvements in supply chain analysis could be process mining, augmented analytics, graph analytics, etc.

3. Software

The software for supply chain analytics is 2 types, namely BI dedicated tools for analytics using data and supply chain analytics tools from the supply chain and embedded software for supply chain analytics.  Vendors of ERP and customizable models of software from IT consultants that can be integrated into processes of the business are available as supply chain analytics software. CEP solutions in real-time are also available but are un-standardized and offer 1-to-1 integration. SCOR or model of Supply Chain Operations Reference can be used for the standard metrics of benchmarks used in supply chain performance. Stand-alone solutions are also available for the core areas of supply chain analytics like finance, inventory management, demand planning, transportation management, production lines, etc.

4. How supply chain analytics work?

Supply chain analytics analyses data from different business processes and applications, third-party sources, infrastructure and emergent technologies of BI, IoT etc., to help improve strategy and decision-making across the fronts of tactical, strategical and operational processes. It synchronizes the execution and planning of supply chains providing real-time visibility for comparison with the bottom lines and customer impact while making tradeoffs in customer VS cost.

5. Features

Some notable features improving the understanding and insights of the software used in supply chain analytics are-

  • Stream processing from multiple data streams like the application, IoT, third-party data, weather reports etc. 
  • Data visualization where data is transformed through dice and slice operations with different angles.
  • NLP-Natural language processing for unstructured data from various news and data sources.
  • Social media integration to collect sentiment data and improve demand planning. 
  • Location intelligence to optimize distribution from location data.
  • Graphical databases with linked elements for traceability of products, finding connections, pattern identification etc.
  • Digital supply chain twin is the comprehensive model for predictive/prescriptive analysis shared across locations and users.

6. Uses

Supply chain analytics is used in most areas of the chain but notably in Operational and Sales planning, where it is used to drive improvements in risk management, procurement streamlining, accuracy-planning improvements, order management, working capital management and more.

7. History

In 1911 Frederick Taylor propounded some principles usable in supply chain management and industrial engineering, which Henry Ford used to create the more efficient production lines of modern-day supply chain and assembly lines. In 1958 IBM’s Hans Peter Luhn coined the BI- business intelligence data types and analytics for supply chains. Bud Lalonde, in 1963, suggested adding physical distribution management to define business logistics which included manufacturing, procurement and materials management. Stafford Beer, at this time, explored models for viable organizing business information systems with a hierarchy that was structured, and this in the 1980s came to be known as the model for supply chain management.

By the 1990s, the internet was included in the efforts of the British Kevin Ashton, who used RF sensors to identify and capture supply chain data and movement of lipsticks known today as RFID features in the supply chain and its management. David Luckham took it a step further by adding real-time data to the development. Cloud computing was the next big event for the supply chain incorporating services like software, IT infrastructure, data lakes and platforms. This allowed multiple data sources, Big Data Hadoop, deep learning and the IoT to be a part of supply chain analytics and management.

Along with the development of analytical models, supply chain analytics is fast evolving to include integrated models with infrastructure, data structures, data silos, improved CEP models, automated algorithms for supply chains etc. Some of the future trends are expected in the following fields of 

  • Blockchain technology, with its secure transactions and improved traceability and visibility across layers, seem to be able to revolutionalize the supply chain. Its building blocks, automated smart contracts, and its fail-proof verification are some of the great features that can be used here.
  • Hyperautomation technology is set to storm supply chain automation with analytics for process mining analytics to identify the crucial candidates for automation processes.
  • Graph analytics is a prediction tool for enterprise applications that have helped the growth of supply chain management models and is expected to grow manifold.

Conclusion

Supply chain analytics has evolved present in everyday use in modern times. It is poised for growth with newer technologies, uses, and benefits being discovered daily, true to the definition of supply chain analytics.

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