Cognitive supply chain and a new way of working has been saved
Perspectives
Cognitive supply chain and a new way of working
Your data and infrastructure with artificial intelligence
Mobile purchasing. Same-day shipping. Real-time problem resolution. The new normal in consumer interactions is speed with agility. Accomplishing it requires organizations to create cognitive supply chains that augment human capabilities with advanced technology and analytics. We call this the Age of With™: human expertise made greater with machines.
A cognitive supply chain with a customer focus
To seize new opportunities—and fend off competitive threats—supply chain leaders need to spot and act upon a host of diverse, long-range issues. This requires the ability to gather and analyze voluminous data across end-to-end processes. Humans can’t do it alone in a timely manner, if at all. But by incorporating new technologies like AI into their supply chain, they can, without breaking a sweat.
Yet many companies underinvest in technologies like supply chain AI and machine learning. While 76 percent of respondents in the Deloitte 2019 Supply Chain and Digital Analytics Survey said developing digital and analytics capabilities was critical to their supply chain strategy, only 44 percent invest at least $5 million annually to develop these capabilities—even though nearly half the respondents expect an 11 to 20 percent return on their existing digital and analytics investments.
Why the disconnect? Part of the problem is the hype surrounding AI. If you expect to push a button and have a machine spit out insights, you’ll be disappointed. In reality, supply chain AI systems are only as good as the information they’re fed. To realize their promise, they need to receive current, accurate data from multiple source systems. Setting up and maintaining these data streams involves much grunt work and can feel daunting.
As a related issue, some companies have difficulty articulating the full value proposition of digital and analytics initiatives. They don’t think big enough. They view technology through the prism of cost reduction or other small-scale goals, not as ways to transform their business.
But this is starting to change.
Cutting costs remains the primary driver for digital and analytics investment, but improving the customer experience is becoming increasingly important as use cases show what’s possible.
What's driving digital and analytics investment
What digital and analytics means for you
While digital and analytics can create value across the entire cognitive supply chain, a handful of applications has emerged as the most promising. These include inventory visibility optimization, real-time manufacturing asset intelligence, and control tower–enabled visibility. This suggests companies are focusing on areas lower in complexity, but still struggling with advanced high-tech capabilities such as supply chain machine learning or AI.
This, too, however, is starting to change. Many business leaders remain focused on digital capabilities that have achieved positive ROI to date, but others are moving in new, more complex directions.
To help decide where to go in your digital and analytics journey, follow these steps:
- Think big. Size up your opportunities. Talk to others in the organization—from logistics and distribution experts to sourcing and procurement staff— to find the biggest needs across your supply network.
- Start small. Prioritize your opportunities based on where you can gain value quickly. Be sure to consider the level of risk and ease of implementation—and think beyond immediate financial benefits.
- Scale fast. Once you’ve achieved some early successes in a pilot environment, design a governance framework and rollout strategy for broader implementation. Then socialize your plan across the company.
New talent for a digital world
The skills needed to run a warehouse are very different from those required to build a warehouse management system. To do the former, you must know the business well. For the latter, business knowledge is the starting point. You also need to understand technology, develop a data model, and connect multiple data sources.
This is but one example of why people with leading-edge digital and analytics skills are in short supply—and generally costly to hire. In our survey, only 21 percent of companies rely most heavily on existing supply chain resources to execute digital and analytics initiatives. Another 82 percent identified internal expertise as their primary talent challenge. As a result, many companies are taking steps to build a more technically and analytically savvy cognitive supply chain workforce.
To make optimum use of AI and analytics tools, the cognitive supply chain workforce needs a blend of hard and soft skills. A thorough understanding of the business—and what drives its financials—remains as important as ever. But to deliver tech-enabled work, people also need curiosity, flexibility and a willingness to do things differently.
Five questions to gauge your readiness to deploy digital and analytics capabilities across a cognitive supply chain network
- Do you understand enough about cognitive technologies to hire someone for a position?
- Are you comfortable setting a digital and analytics agenda for your team?
- Do you have a plan to get your data and infrastructure ready to be leveraged in new ways?
- Have you identified a few high-profile opportunities to tackle first?
- Can you build a more technically savvy workforce with the resources available?
Explore other chapters in the Age of With series
What idea links machines and people? The common answer is versus. But the true role of analytics and AI along with other tools is collaborative. The future of artificial intelligence is a future of human potential: people with machines, doing more than either could alone. Welcome to the Age of With™. Explore the other chapters in this series.
Get in touch
Sam Pearson |
Siddharth Patil |
Chris Noyes |
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