Early adoption lessons - AI in the consumer products supply chain
Everyone has some view on artificial intelligence and its potential impact on our lives in the near and distant future. As new advancements continue to be made, we are seeing more practical use cases for this technology in the products we use every day.
Gartner defines artificial intelligence (A.I.) as technology that appears to emulate human performance typically by learning, coming to its own conclusions, appearing to understand complex content, engaging in natural dialogue with people, enhancing human cognitive performance (also known as cognitive computing), or replacing people on the execution of non-routine tasks. In other words, A.I. is a tool that can augment or enhance many of our daily routines, learn and adapt as it goes, and potentially replace the need for human interaction on certain tasks.
Of course, not everyone has such a rosy view on the future implications of artificial intelligence. The renowned theoretical physicist Stephen Hawking, has warned: “In short, the rise of powerful A.I. will be either the best, or the worst thing to ever happen to humanity. We do not yet know which.”
Applications of A.I. continue brewing in business and in the world of supply chain. The main desire from use cases is to get closer to the consumer and enhance their brand experience. There are already some keen lessons in the consumer goods space
Smart Toothbrushes: Many brands recognise there’s a benefit to the consumer if a product is connected. Toothbrushes are becoming data generators and this creates the opportunity to build relationships with their users. Smart toothbrushes connect to an app to monitor brushing activity and enables gamification.
Brushing data can be shared with dentists so they can track patient brushing behaviour over time. Similarly, data sharing with health insurers may reduce premiums and provide users with cost savings. There are other entrants in this market where the product is designed to help users improve their oral health, by providing feedback about brushing techniques collected by integrated artificial intelligence.
Olay Skin Advisor: In a use case of A.I. in demand sensing and shaping, Procter & Gamble has used proprietary artificial intelligence technology to create a Skin Advisor service for its Olay brand. The technology was born out of the frustration women feel when it comes to skincare brands, which have spent years coming up with a myriad of products addressing a variety of skin problems.
Olay’s Skin Advisor is a web based technology platform that uses artificial intelligence to help women learn more about their skin, what it needs and – of course – the right skincare products to help. Users take a no-makeup selfie and fill in a short survey about their current skincare routine, which results in millions of selfies collected and used to create an algorithm to spot the key "aging zones."
Olay is then able to pinpoint exactly where on a person's face might need a product, recommend what they should use as well as advise on when their skin is looking good. The A.I. behind the service will get better at making recommendations as more women upload pictures. It’s already been used over one million times since a beta version was launched last September in North America and with over 80mn women using Olay products around the world, P&G is confident that it will get better with time.
“A.I. is not a technology of the future – it’s transforming our world today,” said Greg Estes, vice president of developer programmes at NVIDIA, whose GPU-accelerated deep learning platform was used to train Olay Skin Advisor’s neural network. “Olay and other leading brands are using A.I. to infuse devices and apps with intelligence, bringing new experiences, unprecedented personalisation and real benefits to people’s lives.”
So what are the early lessons here that businesses need to prepare for? Firstly, as the smart toothbrush example shows, businesses need to be prepared to look at each and every process and then share that information across a wide group of stakeholders to drive the biggest AI benefit.
Secondly, the Olay example shows that not only is interaction with end users vital but that the deployment of AI must be set to work at scale. That opens up a wide set of issues in itself, from communications to drive use and uptake of the technology that will feed AI platforms, to how the data is stored, secured and accessed.
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.”