The modern global supply chain is defined by scale: billions of transactions and terabytes of data across multiple systems, with businesses generating more every moment. Traditional supply chain management (SCM) practices are quickly being outmatched by the ceaseless onslaught of information and artificial intelligence.
Over the years, Australian organisations have deployed a broad range of transactional and analytic systems for supply chain operations. While gains have been made, supply chains remain far from the ideal of real-time visibility and data-driven decisions that define the supply chain of the future.
When a problem arises with inventory costs or availability, financial and demand planners dive into Excel or legacy SCM tools in an attempt to pinpoint the issues. This approach is like looking for the proverbial needle in a haystack. The sheer volume, velocity and variety of data defy human efforts to understand the dynamics and steer the ship.
Supply chains have grown infinitely more complex in today’s faster digital environments. Soaring data volumes and diversity are overwhelming the relatively static and simplistic business rules of legacy transactional and analytic applications. More partners, products and geographies complicate the dilemma.
As a result, too many supply chains run on guesswork decisions based on information that’s often outdated and contradictory. An inaccurate forecast, for example, can very quickly have a cascading negative impact throughout a supply chain. Companies risk delays, needless costs and revenue loss, by continuing to rely on status quo processes and decade-old software.
One of the problems is that most analytic solutions stop at the ‘understand’ stage, leaving the business user to figure out what to do. It’s important to take an integrated approach to data from across the enterprise to provide prescriptive recommendations for business users. This helps companies move beyond understanding problems to actually taking actions that improve business performance.
“Despite the hype around AI, it’s already delivering the insights and optimisations that many supply chains desperately need.”
Enter artificial intelligence
Artificial intelligence (AI) is poised to transform supply chains with breakthrough capabilities to process huge amounts of real-time data and make intelligent recommendations to usher supply chains into a truly data-driven future.
Innovative organisations are applying AI and machine learning against vast sets of supply chain data to unearth insights into problems and performance that are effectively beyond the reach of even the most skilled planning professionals.
Organisations in pharmaceuticals, consumer packaged goods, manufacturing and other industries, see the tremendous promise that AI holds and are looking to move beyond relatively simplistic SCM tools built on static business rules that inhibit the ability to optimise and scale.
German pharmaceuticals firm Merck, for example, plans to deploy AI and predictive analytics throughout its healthcare division’s entire supply chain by the end of 2019. The company is currently using analytics software to mitigate supply shortages, predict spikes in demand and bottlenecks with about 100 products related to fertility drugs. The company plans to expand its pilot program to 5,000 products by the end of this year, which will add significant competitive advantage.
Closer to home, the Australian Government is getting behind local businesses to support projects with a specific focus on AI, committing $25 million in additional funding for the Cooperative Research Centres Program. Developing Australia’s AI capability will support business innovation by enabling SME to develop and leverage new technologies, products, processes and services for global supply chains, ensuring Australia is a world leader in competitive global markets.
A smarter supply chain
How will intelligent supply chains actually work? Rather than the limiting rules of traditional software, AI relies on machine learning algorithms to learn and refine in real time as it crawls internal and external data sets. That could include inventory data, supplier performance, demand fluctuations, and even weather or road conditions.
AI combines this disparate knowledge to make recommendations or decisions on optimal actions. Think of it almost as a self-driving business based on cognitive automation — the ability to learn, think and take actions.
Take the available-to-promise (ATP) function, which is an important concept in supply chain management that responds to customer order inquiries based on resource availability. In traditional software, ATP is fundamentally a rules-based calculation based on theoretical lead times and allocation rules that are incredibly variable and volatile. Using those data points in ATP calculations can result in wrong ATP dates.
In contrast, AI can automatically generate a ‘supply chain map’, showing everything about an order, including allocated quantity and expected delivery date. It delivers highly accurate recommendations and predictions based on machine learning and data science, not simple rules-based ATP calculations.
ATP is just one example. AI’s powerful cognitive automation capabilities can be applied to all supply chain processes, from demand and supply forecasting to inventory optimisation, manufacturing performance, procurement automation and supplier reliability assessments.
Getting started with cognitive automation
The AI-driven supply chain is a journey — and like any journey, it needs a roadmap and a driver.
Supply chain managers at innovative companies like Merck move through five key levels on their way to intelligent, data-driven operations:
- Understanding: leverage AI to fully understand the true operative states of your supply chain.
- Recommendations: use the AI system for recommendations on key risks and opportunities.
- Predictions: gain insights with predictions and probabilities based on AI’s continuously evolving machine learning.
- Augmented decisions: AI suggests optimal decisions that require human review and approval.
- Autonomous decisions: AI makes decisions autonomously without human intervention.
Moving through various levels of understanding and decision making, supply chains can ultimately reach completely autonomous decision making, leaving room for experts and managers to focus on strategy and growth. So where should you start?
First, think through specific uses for applying AI in your supply chain, whether it’s demand forecasting, inventory planning, manufacturing, or ATP/CTP (capable to promise) optimisation. From there, assess each of the business rules for those disciplines and how to make them more algorithmic with data science.
Once you’ve mastered the basics, it’s important to operationalise AI into your business. AI cannot be a side project — when done right, it should be embedded within your processes.
Despite the hype around AI, it’s already delivering the insights and optimisations that many supply chains desperately need. By embracing it vigorously, we enter a brave new world with boundless possibilities for supply chains to run as efficiently as proverbial clockwork.
Rajeev Mitroo is managing director of Asia Pacific for Aera Technology. For more information visit https://www.aeratechnology.com/.