It’s certainly no longer “business as usual” for supply chains. A convergence of factors has placed significant pressure on organizations’ supply chains to address a wide range of new challenges and priorities that, in many cases, existing supply chain capabilities aren’t capable of handling.
Most existing supply chains were built for a different time, when scale was achieved by delivering truckloads of goods en masse to warehouses and then big-box stores. This model relied on a high level of demand predictability (so visibility was less important) and valued efficiency over resiliency. With supply chain disruption everywhere, this model must change—but how?
To rise to the occasion today, companies need a full-scale reconstruction of the supply chain and manufacturing network that will enable them to become more resilient to disruptions, relevant to customers and employees, and responsible toward the environment and society.
At the core of this transformation are data and artificial intelligence (AI), which are uniquely positioned to provide the insights, agility, and speed companies need to build a completely new supply chain that’s fit for today, tomorrow, and years to come.
Building an intelligent supply chain network
Companies that can put data at the core of their supply chain and apply AI at scale can create a connected and truly intelligent supply chain network.
An intelligent supply chain network can benefit companies in numerous ways.
It can help them gain visibility to late-breaking supply disruptions or demand blips, providing the information needed to resolve issues in near real time.
It helps increase agility so companies can deliver on unique customer requirements with speed, specificity, and scale—boosting product availability and service levels, reducing lost sales and inventory costs, and increasing production and fulfillment efficiency.
It can increase resiliency (e.g., maintaining On Time and In Full service metrics) while reducing companies’ carbon footprint and overall sustainability risk.
The below illustrates, at a high level, what an intelligent supply chain looks like.
CENTRALLY CONTROLLED WITH END-TO-END VISIBILITY
CONNECTED AND INTEGRATED THROUGH CLOUD
HYPER-PERSONALIZED AND FLEXIBLE
Supply chain control towers—based on intelligent technologies that capture, process, and use vast sets of structured and unstructured data—enable companies to centralize data and insights and improve decision-making, faster
Digital hubs bring together people, processes, and technologies to create a 360-degree view of the supply network—across all tiers of suppliers and routes to market
A digital twin, which is a virtual replica of the end-to-end supply chain, can help companies:
Drive scenario planning that allows a company to make predictive decisions based on business needs
Optimize existing processes, their inventory positions, and their overall network through advanced modeling
"Stress test" the entire supply chain to identify where it’s vulnerable to disruptions and how to respond
Identify the optimal network design to boost the sustainability of the entire supply chain
Cloud is a secure, scalable, enabler that integrates data across the supply chain
With AI and the cloud, companies can move from manual sales and operations planning (S&OP) cycles to ones that are fully collaborative, data driven, and platform based
Participants can share qualitative information and real-time data from supply chain systems, review reports, and discuss implications all on an ongoing basis
As exceptions are identified or opportunities arise, planners can create resolution options, share with stakeholders, discuss on the collaborative platform, and take immediate action
All qualitative information that’s currently being exchanged—and lost—in phone calls and emails is saved along with the changes
Agile processes and networks enable companies to quickly sense and appropriately respond to changes in customer demand
ML-based tools can provide insights that might go unnoticed otherwise, as the unsupervised learning can identify trends in the high volume of variables and information
Real-time visibility into market data can produce greater insight, variation, and urgency of understanding and meeting demand requirements
Companies can hyper-personalize order fulfillment by channel, service level, and locality
Challenges to leveraging data and supply chain AI at scale
Despite recognizing the power and value of data and AI, companies will likely continue to find it difficult to leverage their investments more broadly. In fact, a full 79% of COOs acknowledge they know how to pilot AI, but struggle to scale it across the business.
Why? A number of obstacles may be holding them back.
Organizations have data sitting in silos across the enterprise. When data is fragmented and disconnected, the ability to apply intelligence, generate insights, and drive value is limited.
Businesses also struggle with the types of data they collect. They either don’t have the right data or the right quality of data to drive the results they’re looking for.
Many companies find it difficult to settle on who drives the larger rollout of AI and who leads the initiative. With uncertainty over who’s “in charge” or pushing for it, AI initiatives could easily flounder.
Companies have so many vendors, technologies, and solutions to choose from that all sound like they promise the same thing, which makes it difficult for companies to determine which one is right for them.
When trying to scale AI, many organizations know they need and are focused on hiring highly technical employees like data scientists. But this technical expertise needs to be paired with knowledge of the business and strategy. Collaboration between these two "worlds" and having a strategy for talent development is necessary for AI to have significant impact.
Breaking through obstacles to achieve greater impact
Overcoming these obstacles isn’t easy—but it must happen for AI to scale and deliver genuine business value. In our experience, three things can help minimize the roadblocks and allow AI to flourish across the enterprise.
1. Strategy and road map: plotting the destination and how to get there
The first thing a company needs for AI to have a large-scale impact is a clear and integrated vision of where the enterprise wants to go with AI—its North Star, so to speak. It can’t be limited to one function, department, or business unit—that’s the antithesis of scaling. Also critical is the ability to translate this vision into the major initiatives the company must executive to achieve the end goal. Both are vital to taking the subsequent steps to build the foundation that enables a company to realize short- and long-term value from AI and, importantly, to get C-level buy-in to fund such a mega-investment.
2. Cloud: harnessing data for a single and trusted source of truth
AI and advanced analytics can process massive and diverse data sets from all functions to provide better visibility across the supply chain. But with more data sources, more computational power and more server capacity will be needed. With the cloud, a company can connect this data to create one single and trusted source of truth. The cloud also enables organizations to tap into new data sources to extend and enhance visibility and, thus, create greater opportunities for AI to deliver value.
3. Talent: building and buying the right skills
As mentioned earlier, many companies find they don’t have the right talent in place to successfully scale the use of AI in supply chain. In fact, Accenture research found that only 38% of supply chain executives feel their workforce is ready to leverage the technology provided to them. Thus, upskilling or reskilling people to be proficient in applying AI to specific use cases that generate significant value is absolutely vital to the scaling of AI.
Ecosystem partners such as technology vendors and consulting firms also can be great sources of important skills, supplying talent who can augment a company’s existing employees where needed. Such companies have already gone through the steep learning curve required to scale AI and learned the lessons. Their insights and guidance can be extremely valuable in helping companies through what’s often a difficult and complex undertaking.
The future of intelligent, self-driving supply chain networks
Intelligent technologies and connected end-to-end data, when combined and scaled, can add immense value to any company’s supply chain. The combination unifies the supply chain, creates new efficiencies and operational capabilities, and unlocks capital to reinvest in new business models that enrich customer experiences, build competitive advantage, and support profitable growth.
Longer term, this powerful combination of technologies and data will fuel a shift toward truly self-driving supply chain networks—which take value and innovation to a whole new level.
AI can automate demand planning, supply planning, inventory optimization, and execution with a focus on making decisions automatically, without human intervention (i.e., a shift from human driving the machines to machines guided by the humans). Meanwhile, ML enables self-learning, predicting, prescribing, and optimizing supply chain performance automatically across functions. And in a self-driving supply chain, automation can flag and resolve exceptions in real time—for example, ML-based algorithms can predict exceptions and supply chain outcomes and if the process changes over time, cognitive computing learns and adapts to it.
AI truly has the potential to transform any supply chain—and in today’s environment, such a transformation isn’t an option anymore. With the right combination of people, processes, and technology, companies can stop piloting AI and start scaling it so the supply chain network can begin to realize its full potential value—both in the short term and longer term.
Explore how Accenture can help power more intelligent supply chains with analytics and AI. Visitwww.accenture.com/ai