AVEVA: unlocking the benefits of AI in supply chain
Over the past four decades, much of manufacturing production world-wide has been organized in what has become known as Global Value Chains (GVCs). COVID-19 has struck at the core of global value chain hub regions, including China, Europe and the US.
Now, as production comes back up to speed, companies are scrambling for a means of monitoring the inbound flow of product, to figure out how it can be received, stored and shipped at a time when demand for all but the most critical items has ceased. This has prompted many businesses to review their current supply chain processes and to evaluate how they might build in resilience ahead of any future disruption.
Digitalization is essential for industry in this current climate, both to increase margins and operational performance in good times and to adapt in the bad. AI has reached a key juncture where the real-world benefits are instantly recognizable.
Understanding where AI boosts existing processes
In the industrial sector, AI application is supported by the increasing adoption of devices and sensors connected through the Internet of Things (IoT). Production machines, vehicles, or devices carried by human workers generate enormous amounts of data. AI enables the use of such data for highly value-adding tasks such as predictive maintenance or performance optimization at unprecedented levels of accuracy. Hence, the combination of IoT and AI has begun the next wave of performance improvements, especially in the industrial sector. Furthering this automation, AI uses the historical IoT data to analyse trends which can help in streamlining and improving the supply chain process through cutting-edge solutions such as AI-driven operations scheduling. This provides recommendations to humans as to the optimal scheduling sequence, substantially reducing error and inefficiencies. Further, this AI learns as it goes and tailors its guidance to particular situations, gaining intelligence the more it runs. In addition, AI-driven robotic process automation removes the human element from repetitive tasks in varying levels of complexity, thus furthering increases in efficiency and accuracy. Through integrated workflows, much of the supply chain process can be intelligently automated. These types of AI-driven capabilities have the potential to redefine the business supply chain process. In certain industries such as oil & gas, these types of operational gains are more important than ever in today’s climate.
Early adopters of AI have deployed these technologies on-premises, in the cloud, at the edge, and through many types of hybrid architectures. AI itself is not one thing but comprised of several technology types, including neural networks, deep learning, natural language processing, computer vision, unsupervised machine learning, supervised machine learning, reinforcement learning, transfer learning, and others. These various types of AI are applied in different ways throughout the industrial world to create targeted solutions provided as descriptive, predictive, prescriptive, and prognostic analytics.
Overcoming the fear of automation
Beyond deciding where and how to best employ AI, an organizational culture open to the collaboration of humans and machines is crucial for getting the most out of AI. Trust is among the key mindsets and attitudes of successful human-machine collaboration.
Here are some practical steps to consider if a business is looking to explore the implementation of Artificial Intelligence or Machine learning capability into their business process:
- Leverage AI to gain significantly more value out of existing industrial software: SCADA (an acronym for Supervisory Control and Data Acquisition generally refers to industrial control systems) and other types of control systems have become standard practice in most industrial facilities. Real-time and historical data is typically used for trending, reporting, and HMI visualization. AI allows companies to get much more value and insight from this historian data through state-of-the-art technologies such as multi-variate machine learning and deep learning. By integrating software infused with AI into existing industrial IT infrastructures, businesses can greatly amplify the value and ROI by detecting and solving operational and maintenance issues before they become larger problems that often result in unplanned downtime. This alone can increase uptime by 10% annually, resulting in substantial avoided costs and efficiency gains.
- Allow AI to integrate into the core of the supply chain to take advantage of cutting edge capabilities: AI-driven operational scheduling and work process automation can eliminate mistakes and allow industrial companies to get the most out of the resources they have available. Supply chain success is critical to overall business success, and an increase in efficiency can often be the difference between turning a profit or not. AI provides incredible value in this area, and businesses shouldn’t be afraid to leverage its power as an integral part of their supply chain process.
- Use the cloud to ease the implementation of AI, allowing companies to scale: Artificial Intelligence is fast becoming the brains behind the cloud. Consequently, companies can quickly deploy and access a variety of industrial software capabilities that are driven by various types of AI technology. The cloud is the delivery mechanism, and SaaS is the commercial model; however, AI drives much of the value gained. Now more than ever before, AI is becoming more easily accessible and more cost-effective to deploy into industrial environments.
- Bridge the gap between AI and humans: In order to glean maximum value from AI, companies must ensure that they bridge the gap between AI and human understanding. A significant portion of the workforce today is somewhat distrustful or fearful of AI. It is critical that companies do everything they can to ensure that the benefits from AI-infused software are translated into the vernacular of the targeted worker. The benefits provided by AI must be put in context, be useful and actionable. If this does not happen, then much of the value of AI is wasted.
- Be open to continued innovation and change: AI capabilities continue to evolve and improve. Software will become more intelligent through combinations of AI capabilities in order to achieve more sophisticated machine-based thought and reasoning. Amid these changes, companies can reap more and more benefits through deeper insight into cost vs risk decisions, an improved understanding of business processes and associated efficiencies, and better forecasts of future events. By continuing to plan for and incorporate change, companies can take advantage of ever-improving AI capabilities and insight.
Time to reflect and evolve
Businesses now need to be incredibly agile to manage the costs of turning down production, followed by the working capital constraints to then rebuild production levels as economies recover. We are also seeing a period of distrust and disinformation while global supply chains are disrupted, data is key to traceability and provenance ensuring that drugs and food come from authentic sources. Better visibility allows us to understand where resources such as food and pharmaceuticals are and how we can get energy efficiently to those who need it.
Digital transformation stands to provide an immediate and compelling competitive advantage for those quick to adopt – and to demonstrate provenance. Businesses require intelligent software to address industrial pain points for value creation, productivity improvement, insight discovery, risk management, and cost optimization. AI is a key differentiator and a propelling force behind improvements in the supply chain.
NTT DATA Services, Remodelling Supply Chains for Resilience
Joey Dean, the man with the coolest name ever and Managing Director in the healthcare consulting practice for NTT DATA and is focused on delivering workplace transformation and enabling the future workforce for healthcare providers. Dean also leads client innovation programs to enhance service delivery and business outcomes for clients.
The pandemic has shifted priorities and created opportunities to do things differently, and companies are now looking to build more resilient supply chains, none needed more urgently than those within the healthcare system. Dean shares with us how he feels they can get there.
A Multi-Vendor Sourcing Approach
“Healthcare systems cannot afford delays in the supply chain when there are lives at stake. Healthcare procurement teams are looking at multi-vendor sourcing strategies, stockpiling more inventory, and ways to use data and AI to have a predictive view into the future and drive greater efficiency.
“The priority should be to shore up procurement channels and re-evaluate inventory management norms, i.e. stockpiling for assurance. Health systems should take the opportunity to renegotiate with their current vendors and broaden the supplier channel. Through those efforts, work with suppliers that have greater geographic diversity and transparency around manufacturing data, process, and continuity plans,” says Dean.
But here ensues the never-ending battle of domestic vs global supply chains. As I see it, domestic sourcing limits the high-risk exposure related to offshore sourcing— Canada’s issue with importing the vaccine is a good example of that. So, of course, I had to ask, for lifesaving products, is building domestic capabilities an option that is being considered?
“Domestic supply chains are sparse or have a high dependence on overseas centres for parts and raw materials. There are measures being discussed from a legislative perspective to drive more domestic sourcing, and there will need to be a concerted effort by Western countries through a mix of investments and financial incentives,” Dean explains.
Wielding Big Tech for Better Outcomes
So, that’s a long way off. In the meantime, leveraging technology is another way to mitigate the risks that lie within global supply chains while decreasing costs and improving quality. Dean expands on the potential of blockchain and AI in the industry.
“Blockchain is particularly interesting in creating more transparency and visibility across all supply chain activities. Organisations can create a decentralised record of all transactions to track assets from production to delivery or use by end-user. This increased supply chain transparency provides more visibility to both buyers and suppliers to resolve disputes and build more trusting relationships. Another benefit is that the validation of data is more efficient to prioritise time on the delivery of goods and services to reduce cost and improve quality.
“Artificial Intelligence and Machine Learning (AI/ML) is another area where there’s incredible value in processing massive amounts of data to aggregate and normalise the data to produce proactive recommendations on actions to improve the speed and cost-efficiency of the supply chain.”
Evolving Procurement Models
From asking more of suppliers to beefing up stocks, Dean believes procurement models should be remodelled to favour resilience, mitigate risk and ensure the needs of the customer are kept in view.
“The bottom line is that healthcare systems are expecting more from their suppliers. While transactional approaches focused solely on price and transactions have been the norm, collaborative relationships, where the buyer and supplier establish mutual objectives and outcomes, drives a trusting and transparent relationship. Healthcare systems are also looking to multi-vendor strategies to mitigate risk, so it is imperative for suppliers to stand out and embrace evolving procurement models.
“Healthcare systems are looking at partners that can establish domestic centres for supplies to mitigate the risks of having ‘all of their eggs’ in overseas locations. Suppliers should look to perform a strategic evaluation review that includes a distribution network analysis and distribution footprint review to understand cost, service, flexibility, and risks. Included in that strategy should be a “voice of the customer” assessment to understand current pain points and needs of customers.”
“Healthcare supply chain leaders are re-evaluating the Just In Time (JIT) model with supplies delivered on a regular basis. The approach does not require an investment in infrastructure but leaves organisations open to risk of disruption. Having domestic centres and warehousing from suppliers gives healthcare systems the ability to have inventory on hand without having to invest in their own infrastructure. Also, in the spirit of transparency, having predictive views into inventory levels can help enable better decision making from both sides.”
But, again, I had to ask, what about the risks and associated costs that come with higher inventory levels, such as expired product if there isn’t fast enough turnover, tying up cash flow, warehousing and inventory management costs?
“In the current supply chain environment, it is advisable for buyers to carry an in-house inventory on a just-in-time basis, while suppliers take a just-in-case approach, preserving capacity for surges, retaining safety stock, and building rapid replenishment channels for restock. But the risk of expired product is very real. This could be curbed with better data intelligence and improved technology that could forecast surges and predictively automate future supply needs. In this way, ordering would be more data-driven and rationalised to align with anticipated surges. Further adoption of data and intelligence and will be crucial for modernised buying in the new normal.
These are tough tasks, so I asked Dean to speak to some of the challenges. Luckily, he’s a patient guy with a lot to say.
On managing stakeholders and ensuring alignment on priorities and objectives, Dean says, “In order for managing stakeholders to stay aligned on priorities, they’ll need more transparency and collaborative win-win business relationships in which both healthcare systems and medical device manufacturers are equally committed to each other’s success. On the healthcare side, they need to understand where parts and products are manufactured to perform more predictive data and analytics for forecasting and planning efforts. And the manufacturers should offer more data transparency which will result in better planning and forecasting to navigate the ebbs and flows and enable better decision-making by healthcare systems.
Due to the sensitive nature of the information being requested, the effort to increase visibility is typically met with a lot of reluctance and push back. Dean essentially puts the onus back on suppliers to get with the times. “Traditionally, the relationships between buyers and suppliers are transactional, based only on the transaction between the two parties: what is the supplier providing, at what cost, and for what length of time. The relationship begins and ends there. The tide is shifting, and buyers expect more from their suppliers, especially given what the pandemic exposed around the fragility of the supply chain. The suppliers that get ahead of this will not only reap the benefits of improved relationships, but they will be able to take action on insights derived from greater visibility to manage risks more effectively.”
He offers a final tip. “A first step in enabling a supply chain data exchange is to make sure partners and buyers are aware of the conditions throughout the supply chain based on real-time data to enable predictive views into delays and disruptions. With well understand data sets, both parties can respond more effectively and work together when disruptions occur.”
As for where supply chain is heading, Dean says, “Moving forward, we’ll continue to see a shift toward Robotic Process Automation (RPA), Artificial Intelligence (AI), and advanced analytics to optimise the supply chain. The pandemic, as it has done in many other industries, will accelerate the move to digital, with the benefits of improving efficiency, visibility, and error rate. AI can consume enormous amounts of data to drive real-time pattern detection and mitigate risk from global disruptive events.”