Jul 7, 2021

The Integrated Supply Chain: AI, ML, and Human Intelligence

Supplychain
ArtificialIntelligence
MachineLearning
DigitalSupplyChain
Elise Leise
7 min
AI and ML
AI will never be a silver bullet, but with proper data and augmented with human intelligence, it will revolutionise supply chain agility and resilience

According to IBM’s Cognitive Computing Study, 92% of outperforming manufacturing executives said AI and cognitive computing would enhance their future performance. Advances in artificial intelligence will tie everything together: SCM and ERP systems, smart factories, and autonomous warehouses; humans will make complex decisions, changing course as needed. 

At least, this is one potential future. Right now, supply chains are in the midst of deciding how to best upgrade their visibility and agility. Pre-pandemic, we globalised, unleashing threads of data, teams, goods, and transport networks across the globe. On the flip side, we also sped up procurement times, multiplied errors, and obscured the truth of modern slavery in sub-tier supplier networks. And during COVID-19, everyone saw the result: massive supplier shortages, backed-up transport networks, and miscalculated ESG metrics. 

Now, as companies recover from the pandemic, artificial intelligence (AI) and machine learning (ML) may equip supply chain teams to cope with similar events. According to Gartner, this isn’t far into the future: by 2024, 75% of companies will transfer AI from pilot programmes to their main operations. But we’re obviously not at that point yet. So what’s the holdup? 

Artificial Intelligence and Machine Learning

First, let’s break down two commonly—and understandably—confused terms. 

Artificial Intelligence 

Question: How do we build intelligent machines that simulate human intelligence? 

Artificial intelligence is the big “umbrella concept”. According to BMC, AI exists on several levels: 

  • Reactive Machines, where AI makes basic inferences
  • Limited Memory; where AI can store and learn from data
  • Theory of Mind, where AI starts to interact with human emotions
  • Self-Aware, where AI recognises its own existence

 

Machine Learning 

Question: How can we build machines that learn from data without explicit programming? 

Machine learning is a small subset of artificial intelligence. It’s an application of AI in which unsupervised machines can learn from data without human supervisors watching their every move. ML programmes centre on four primary tasks: 

  • Gathering data
  • Training models
  • Deploying models
  • Maintaining/upgrading systems 

 

AI Supply Chain Applications
 

Now that we’ve established the basics of AI and ML, how do they relate to the supply chain? The answer’s quick and easy. By allowing humans to view and alter operations how and as they wish, AI touches every field of supply chain management: procurement, manufacturing, fulfilment, logistics, inventory, and ESG compliance. 

Ten years ago, IBM predicted that the supply chains of the future would be instrumented, interconnected, and intelligent: Sensors and RFID tags would track goods worldwide; connectivity would enable global supplier networks; and advanced analytics would help decision-makers evaluate their risks and constraints.  

Several of the firm’s predictions have proved prescient, as AI and ML start to influence three major supply chain divisions. 

  • Manufacturing: Companies invest in cloud applications, 3D printing, and IoT applications to speed up and make their operations more sustainable
     
  • Procurement: Companies use risk management platforms to tackle regulations and sustainability, connect with collaborators, and follow through on ESG goals
     
  • Product Development: Companies test out automated robotics in factories, labelling facilities, and shipment loads—increasing efficiency and accuracy

Now that companies recognise how quickly global financial, economic, and social collapse can decimate local supply chains, many will invest in risk mitigation before the next global disaster. According to IBM’s Cognitive Computing report, CPOs report that their top three tech investment initiatives will be cognitive computing, cloud, and predictive data analytics. 56% of companies said that cognitive computing would transform their forecasting capabilities, 55% said that it would do the same for risk and security management, and 55% said that it could redefine the customer experience. 

If a global disruption occurs again in the next decade—which, no doubt, it will—AI can provide visibility into international supply chains, target risk factors, suggest resilient suppliers when old ones fall through, remap transport routes, and help companies maintain their sustainability standards. 

A Renewed Commitment to ESG
 

AI- and ML-based automation tools, through searching and tracking patterns, can help companies keep their environmental, social, and corporate governance commitments. In 2021, most major multinational corporations have declared strong support for the United Nations’ Sustainable Development Goals and global ESG standards. Yet, not all claims are backed with concrete data. Company KPIs, in some cases, represent little more than an exercise in greenwashing. 

As often as not, this isn’t the company’s fault. Opaque procurement processes and multi-tier supplier networks make it intensely difficult for any human to sort through, analyse, and make sense of ESG performance data. But regardless of cause, the result is just as detrimental. Companies, without meaning to, mislead consumers and investors—and perpetuate injustice in the parts of the world that need it most. 

Even international watchdog agencies lack perfect visibility. According to Terence Tse, a professor at the ESCP Business School and Executive Director of Nexus FrontierTech, ratings can be deceptive. He cited UK-based Boohoo, a fast-fashion retail startup that MSCI rewarded with its second-highest ESG rating. 

By sourcing from a Leicester factory, the company relied on a supplier that paid its workers as little as £3.50 per hour and refused to provide PPE during the pandemic. 

Such a case begs the question: how can companies ensure that they truly meet their supply-chain labour standards? AI technology startups like Tse’s Nexus FrontierTech claim that the answer is AI and ML: the future of visibility and agility. Yet others aren’t quite convinced. Part of it is the all-too-human fear of growing obsolete. 

The Human Element
 

“[Humans have] natural hesitations to believe machines that don’t have years of experience in manufacturing and supply chains”, explained Richard Lebovitz, President and CEO of LeanDNA. Yet few AI and ML leaders suggest that we get rid of all humans and let machines rule the world. 

Artificial intelligence, though it continues to make strides towards greater intelligence, is still a tool in the hands of professionals. Its goal is not to replace humans but to increase speed and accuracy in a multitude of mindless job specialisations, such as crunching numbers, handling data, and vetting suppliers. 

As AI takes on these functions, humans will regain the ability to turn their attention—and their unique cognitive skills—to tasks that add value. Yet Pervinder Johar, CEO of Blume Global, cautioned against moving too fast, noting that incremental steps are critical to an organisation’s success. “[AI] must demonstrate that its automated recommendations are reliable and backed by best practices. Without that trust and context, teams simply won’t adopt new technology”. 

Overall, the best path forward is to combine humans and AI. Machines might be able to rationalise their way through complex supplier data, but in collaborating, negotiating, and navigating complex supplier relations, humans still have the advantage. “Bottom line: AI is not a replacement for human judgement”, said Pervinder Johar, CEO of Blume Global. “People are vital to the successful implementation of any automated system—and in supply chains especially, AI should never work in isolation”.

Future Adoption: What’s Holding Us Back?
 

Right now, the main issue facing AI and ML in the supply chain is that no machine can fix bad, corrupted, or biased data. And most corporations don’t have great data. It’s not unusual for naming conventions to differ across countries, regions, and companies, and the lists of categorical acronyms—CDP, GRI, SASB, ISO—blur together. 

Part of the issue is a shortage of multi-tier supply chain visibility. “The #1 challenge...is the absence of data governance policies that are enforced and adhered to across an OEM’s (original equipment manufacturer’s) extended supply chain”, said Trevor Stansbury, founder and CEO of Supply Dynamics. “As crazy as it sounds, General Electric doesn’t make engines. Caterpillar doesn’t make heavy earth-moving equipment. Boeing doesn’t make aeroplanes. And Ford doesn’t make cars. Their tier-one through tier-n suppliers do”. 

Supply chain management, at the end of the day, is intrinsically linked to relationship management. And it will be humans who develop, inquire, and build the supplier connections that drive our supply chains forward. On this playing field, only companies who take advantage of supplier intelligence platforms and strengthen ties with sub-tier suppliers will score points. To be properly used, AI and ML need accurate, granular intelligence. 

This is a major part of the reason why AI adoption has been slightly slower than any of us thought. AI relies on us, humans, to provide large, well-standardised datasets, to eliminate data silos, and to supplement its analyses with excellent supplier communication. To apply AI and ML, then, companies must train their employees in ethics, decision-analysis, and context-based strategy; evaluate cloud-based, cognitive AI solutions; and invest in supplier relations. Only then can they fuel their machines—and supply chains—with the proper data.

 

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Jul 30, 2021

QR technology: The Bridge to a Sustainable Fashion Industry

ESG
Sustainability
CircularSupplyChain
Technology
6 min
Supply Chain Digital discusses how the fashion industry supply chains can create a more circular economy and be more sustainable with QR code technology

As one of the top polluting industries, Sara Swenson, Global Senior Manager Sustainability at Avery Dennison explains “there's debate whether it's the third, fourth or fifth most polluting industry, but it's generally well known that about 4% of global carbon emissions and about 20% of water pollution comes from the fashion supply chain. So obviously it’s massive. On the US side, we dispose of about 70 pounds, which is about 30 to 32 kilogrammes clothes every year. So it's a growing impact that has dramatically started impacting the world.”

Could Technology be the Answer to Sustainability Challenges?


Year after year, over 100 billion new garments are made, with US$450bn worth of textiles thrown away around the world. The emergence of a ‘fast fashion’ society has resulted in the average person not only buying 60% more clothes than in 2000 but also discarding more. On average, a family in the Western World throws away 30kg of clothing a year, with only 15% being recycled or donated. 

“Over the past 20 years, environmental issues have ramped up and ‘fast fashion’ is partly to blame,” says Swenson. “Fast fashion has changed the mindset of how quickly styles and consumers want to update their clothing lines. But over the past 20 years, consumers purchased about 60% more clothes than we did in 2000 and we're not circulating those materials back in. They're really going in a linear fashion: take, make and waste out. 

“We've really switched from having high quality garments to lower quality, more plastic based garments, and out of those that are manufactured every year, about 30% are just overstocked, they're never even sold. So there's all these waste stitches along the supply chain that need to be figured out, and then the recirculation of those raw materials back into the supply chain. None of that's happening with fast fashion, because everything is done so quickly and consumers want new products so much faster than ever before.”

Adopting a circular economy approach, instead of a linear one can help the fashion industry to become more sustainable. “A Circular economy is really about designing out that waste and pollution that I was talking about within the supply chain, and then keeping those products and materials in use for as long as possible, and then regenerating them back into the supply chain at the end of their life,” says Swenson who strongly believes that this is important to do, “because A: we all know the risks to the environmental factors, and then B: customers and consumers want us to solve these problems. We're getting more and more educated consumers that are willing to dive into the data. Brands are no longer able to greenwash and say, ‘Hey, we're doing something sustainable’, they actually have to prove they’re doing something sustainable with the data that backs it up or approves it.” 

Mobile Technology: The Future of Sustainable, Transparent and Ethical Fashion


With 60% of consumers valuing brands that are transparent about their operations, fashion brands are turning to mobile technology such as QR Codes and NFC tags to provide their customers with end-to-end information on the product they have purchased from raw materials and production, right through to distribution and beyond. 

“Technology is probably going to be the easiest way to create data to show that brands are making more sustainable actions, that they are not just greenwashing their sustainability progress. It also gives supply chain stakeholders the right to ask questions and engage, as well as consumers to understand ‘if you make this choice in how you're going to dispose of our garments, this is going to be your environmental impact. So it provides the right data that's available to both the consumer and the brand and other stakeholders to make those choices,” says Swenson.

“Right now we're asking stakeholders to make choices without data and without an easy solution. Consumers are not going to go through extensive links to find the right recycler, or find the right reseller. But if that information is at the tip of their fingers, on the garments that they can access, then they're much more likely to make those appropriate environmental decisions as well.”

With it still being legislation to have physical care and contents information written on a garment, Swenson adds that “many brands are now adding a QR code with information such as how to better wash your garment, how to take care of it so that it has a longer life, the benefits of high quality garments that you want to dispose of, but is still good quality to resell, how to brand authenticate it, and then how it can be recycled at its end of life.”

Whilst Swenson explains that “labels are by no means the solution that is going to solve everything in the apparel supply chain, it is the place that most people go to find more information on their environment.”

Fashion brands adopting QR and NFC technology include PANGAIA, Sheep Inc., and Skopes.

PANGAIA


In May 2021, materials science company - PANGAIA - partnered with EON to create ‘digital passports’ for its products. The lifestyle products brand uses QR code technology to accelerate greater transparency, traceability and circularity in the fashion industry, inspiring responsible consumer choices. 

QR codes are printed directly onto the care labels unlocking a bespoke digital experience when scanned with a mobile phone. The experience takes the customer on a journey from the product's origin through to purchase, dyeing, production, distribution, transportation and aftercare. 

The digitalisation of this experience allows customers to be updated in real-time, bridging the gap towards a full circular model, providing authenticity and visibility of lifecycle data.

Sheep Inc.


Also partnering with EON, Sheep Inc. - the world’s first carbon-negative fashion brand - is leveraging a bio-based NFC tag that provides each customer with a unique ID to trace and discover their product's supply chain journey.

The knitwear company leverages this technology to communicate with their customers the product’s carbon footprint at each stage of its supply chain journey from raw materials to manufacturing, distribution, and approximate usage. 

“Finding out how well or badly a brand has behaved shouldn’t have to turn into an exploratory mission. It should be instantly visible when you go to buy a garment.” commented Edzard van der Wyck, CEO and Co-Founder of Sheep Inc., on the partnership. “We need to get to the stage where brands give customers the full, non-redacted picture of the journey and the impact behind the things they buy.”

Skopes


In 2020, Leeds-based brand - Skopes - coincided with the launch of its first sustainably sourced suit collection - made using plastic bottles - with its use of care labels with QR codes allowing customers to see exactly how and where their suits are made.

“We are really keen to reduce our environmental impact and have developed this collection diligently with Lyfcycle over the past 18 months,” commented Nick McGlynn, head of buying at Skopes, on the launch. “The aim with Lyfcycle is to create a fully self-sufficient, transparent loop of sustainable and traceable sourcing, production and delivery,” adds McGlynn.

Concluding on the future for this technology Swenson says, “the industry has made huge strides, and I think with technology and the availability of tracing and triggers on garments to hold that data, I think it really helps jump the industry forward into providing some actionable data that can be used to showcase a lot of their great efforts that are going unnoticed now, or focus on what they're not doing and that they need to increase, increase what they are doing because  it's not working for their consumers or garments aren't getting where they need to go. So some pretty exciting stuff is finally happening in this.”
 

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