Optimize your Logistics Network with Augmented Analytics
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Optimize your Logistics Network with Augmented Analytics

Logistics Network Design is one of the most critical and foundational aspects of an organization’s Logistics strategy. Manufacturing organizations invest a significant percentage of their capital and intellectual resources in executing the network design and ensuring higher adaptability if the logistics requirement changes in the future. New technology, new product launches, new customer segments, competitor actions, commodity prices, mergers & acquisitions, sustainability changes, and prevalent political, economic, and regulatory climate are among the known factors that introduce inefficiencies, obsolescence, and lack of agility in the incumbent design and demand constant monitoring and optimization in order to meet the target service levels across multiple customer segments in different markets.

In the last five years, massive growth in the e-commerce sector and rise of the digital phenomenon often called “The Amazon Effect“, has significantly impacted the overall behavioral economics in the market and made the consumer more demanding in terms of responsiveness, convenience, higher service levels as well as lower cost of ownership. The change in the market dynamics has made the usage of LSPs/3PLs more attractive for in-house logistics teams.

According to Grandview Research, the global third-party logistics market size was valued at USD 830.99 billion in 2019 and is expected to grow at a compound annual growth rate (CAGR) of 8.3% from 2020 to 2027.

Currently, the Manufacturing and eCommerce sectors are outsourcing most of their logistics activities to 3PLs owing to the benefits offered in terms of higher supply chain visibility, lower transportation cost, better inventory positioning at different echelons, vendor management, business process development, shorter lead time, and stricter compliance to the promised service levels.

AI-based Augmented Analytics brings efficiency and business continuity to logistics networks

The coronavirus pandemic has put the global supply chain network under a lot of stress. Worldwide demand and supply imbalances have challenged the incumbent network design’s effectiveness and efficiencies for many global manufacturing enterprises. There are 4 different types of risks Manufacturing and eCommerce organizations are facing right now ? i.e. Demand Risk, Supply Risk, Process Risks, and Financial Risks. Unfortunately, current business continuity plans are not sufficient to reduce these operational risks under the current crisis.

Consumers today are expecting flexibility in Omnichannel fulfillment and Manufacturers are looking for global visibility on inventory so that the entire network — whether it’s a large distribution center or a store — they’re all enabled as potential inventory fulfillment points. Today, it’s no more a question of being able to ship from any network asset; the goal is to be ready to ship any item, any format, from nearly anywhere. The barriers to doing so can be physical — related to inventory, training, and supplies — or digital, relating to warehouse management software (WMS).

To overcome these risks and barriers, more and more organizations are investing in Machine Learning and AI-based platforms to assess network alternatives and using approaches like Optimization, Simulation, and Heuristics to find the most optimal cost efficiency at the selected strategic service levels. At Course5 Intelligence, we work closely with the client’s in-house logistics team and leverage Course5 Discovery, an AI-powered Augmented Analytics solution to deliver end-to-end exception handling, and give near-real-time prescriptive recommendations to operational teams on how to optimize the network design and make it adaptable to current scenarios.

Course5 Discovery gives operational teams prescriptive recommendations on how to optimize their network design.

The COVID-19 crisis has not only accelerated e-commerce penetration across the world but also created massive opportunities through Supply Chain Digitization, Digital Twins, and AI-Powered Augmented Analytics to generate near-real-time operational insights for Sourcing, Planning, Warehousing, and Transportation teams so that the risks associated with Demand, Supply, Process, and Finance can be mitigated, and organizations can make their logistics network more resilient and responsive.

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Sushant Ajmani

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Sushant Ajmani

Sushant is a seasoned digital analytics professional who has been working in the industry for over 23 years. He has worked with over 180+ global...

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