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Conquering The Endless Quest For Supply Chain Visibility

Publisher: PASA

Author: Rajeev Mitroo

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Despite investments in technology and personnel, the ongoing quest for Australian businesses to see, measure and improve what’s happening across the supply chain remains elusive. Achieving end-to-end visibility across the lifecycle – plan, source, make, deliver, return – is still the ultimate goal.

Not achieving it means that supply chain key performance indicators (KPIs) are often based on incomplete, outdated information, resulting from partial visibility across interdependent supply chain dynamics.

In our hyper-competitive global environment, businesses have to respond rapidly to any supply chain event that impacts customer satisfaction, profitability and working capital. End-to-end visibility and timely KPIs are the foundation for quickly making the right decisions to mitigate risk and ensure profitability.

Introducing cognitive automation

Supply chain visibility is difficult to achieve amid growing business complexity and a proliferation of data and applications. It’s not uncommon for a large manufacturer, for example, to run a half-dozen separate ERP systems, and many other applications for warehousing, planning, logistics and more.

Data lakes are frequently built to improve visibility, but often involves raw data that isn’t curated and harmonised, so it’s difficult to glean insights. Data warehouses complemented by business intelligence tools are limited by descriptive analytics and use outdated information. Both approaches require large effort and cost to be implemented, and consequently have a lower time-to-value.

Instead, innovative companies are embracing a new model called cognitive automation that combines near real-time data capture from source systems, as well as artificial intelligence (AI) and machine learning (ML) algorithms to generate actionable KPIs and recommendations on optimal actions.

A cognitive automation platform executes thousands of Google-like data crawls across any number of internal or external systems, then aggregates and normalises data in what’s called a cognitive data layer. AI and ML are applied to produce predictions and recommendations on optimal actions.

Cognitive automation is used by a global healthcare manufacturer, for example, to solve its ATP challenges. Previously, the manufacturer could provide customers with ATP dates only 50 percent of the time, and of those only 80 percent were accurate. These KPIs were the results of limited visibility due to siloed systems.

The manufacturer now provides ATP dates 99 percent of the time, with accuracy improved to 90 percent, thanks to end-to-end visibility across the supply chain that lets it readily determine available supply across the entire network and lead times from production to delivery.

That’s driving top-line revenue and improving customer experience. The visibility it now has sets the manufacturer up to tackle other challenges, such as back orders or demand planning.

Achieving the Holy Grail

End-to-end visibility across the supply chain has been the Holy Grail for years. The goal is now within reach as AI-powered cognitive automation is introduced into complex global supply chains.

But achieving end-to-end visibility isn’t the end of the journey — it’s only the beginning. That’s because it makes possible KPIs rich in context and analytic depth that businesses can act on to achieve cost savings, new revenue and customer satisfaction.

Rajeev Mitroo is managing director of Asia Pacific for Aera Technology. Mitroo leads Aera’s go to market strategy and initiatives across key markets throughout Asia Pacific, working with partners developing self-driving enterprises. Find out more here.

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