Bottom line: Enterprises are attaining double-digit improvements in forecast error rates, demand planning productivity, cost reductions and on-time shipments using machine learning today, revolutionising supply chain management in the process.

Machine learning algorithms and the models they’re based on excel at finding anomalies, patterns and predictive insights in large data sets. Many supply chain challenges are time, cost and resource constraint-based, making machine learning an ideal technology to solve them.

From Amazon’s Kiva robotics relying on machine learning to improve accuracy, speed and scale to DHL relying on AI and machine learning to power their Predictive Network Management system that analyses 58 different parameters of internal data to identify the top factors influencing shipment delays, machine learning is defining the next generation of supply chain management. Gartner predicts that by 2020, 95% of Supply Chain Planning (SCP) vendors will be relying on supervised and unsupervised machine learning in their solutions. Gartner is also predicting by 2023 intelligent algorithms, and AI techniques will be an embedded or augmented component across 25% of all supply chain technology solutions.

The ten ways that machine learning is revolutionising supply chain management include:

Machine learning-based algorithms are the foundation of the next generation of logistics technologies, with the most significant gains being made with advanced resource scheduling systems

Machine learning and AI-based techniques are the foundation of a broad spectrum of next-generation logistics and supply chain technologies now under development. The most significant gains are being made where machine learning can contribute to solving complex constraint, cost and delivery problems companies face today. McKinsey predicts machine learning’s most significant contributions will be in providing supply chain operators with more significant insights into how supply chain performance can be improved, anticipating anomalies in logistics costs and performance before they occur. Machine learning is also providing insights into where automation can deliver the most significant scale advantages. Source: McKinsey & Company, Automation in logistics: Big opportunity, bigger uncertainty, April 2019. By Ashutosh Dekhne, Greg Hastings, John Murnane, and Florian Neuhaus

The wide variation in data sets generated from the Internet of Things (IoT) sensors, telematics, intelligent transport systems, and traffic data have the potential to deliver the most value to improving supply chains by using machine learning

Applying machine learning algorithms and techniques to improve supply chains starts with data sets that have the greatest variety and variability in them. The most challenging issues supply chains face are often found in optimising logistics, so materials needed to complete a production run arrive on time. Source: KPMG, Supply Chain Big Data Series Part 1

Machine learning shows the potential to reduce logistics costs by finding patterns in track-and-trace data captured using IoT-enabled sensors, contributing to $6M in annual savings

BCG recently looked at how a decentralised supply chain using track-and-trace applications could improve performance and reduce costs. They found that in a 30-node configuration when blockchain is used to share data in real-time across a supplier network, combined with better analytics insight, cost savings of $6M a year is achievable. Source: Boston Consulting Group, Pairing Blockchain with IoT to Cut Supply Chain Costs, December 18, 2018, by Zia Yusuf, Akash Bhatia, Usama Gill, Maciej Kranz, Michelle Fleury, and Anoop Nannra

Reducing forecast errors up to 50% is achievable using machine learning-based techniques

Lost sales due to products not being available are being reduced up to 65% through the use of machine learning-based planning and optimisation techniques. Inventory reductions of 20 to 50% are also being achieved today when machine learning-based supply chain management systems are used. Source: Digital/McKinsey, Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? (PDF, 52 pp., no opt-in).

DHL Research is finding that machine learning enables logistics and supply chain operations to optimise capacity utilisation, improve customer experience, reduce risk, and create new business models

DHL’s research team continually tracks and evaluates the impact of emerging technologies on logistics and supply chain performance. They’re also predicting that AI will enable back-office automation, predictive operations, intelligent logistics assets, and new customer experience models. Source: DHL Trend Research, Logistics Trend Radar, Version 2018/2019 (PDF, 55 pp., no opt-in)

Detecting and acting on inconsistent supplier quality levels and deliveries using machine learning-based applications is an area manufacturers are investing in today

Based on conversations with North American-based mid-tier manufacturers, the second most significant growth barrier they’re facing today is suppliers’ lack of consistent quality and delivery performance. The greatest growth barrier is the lack of skilled labor available. Using machine learning and advanced analytics manufacturers can discover quickly who their best and worst suppliers are, and which production centers are most accurate in catching errors. Manufacturers are using dashboards much like the one below for applying machine learning to supplier quality, delivery and consistency challenges. Source: Microsoft, Supplier Quality Analysis sample for Power BI: Take a tour, 2018

Reducing risk and the potential for fraud, while improving the product and process quality based on insights gained from machine learning is forcing inspection’s inflection point across supply chains today

When inspections are automated using mobile technologies and results are uploaded in real-time to a secure cloud-based platform, machine learning algorithms can deliver insights that immediately reduce risks and the potential for fraud. Inspectorio is a machine learning startup to watch in this area. They’re tackling the many problems that a lack of inspection and supply chain visibility creates, focusing on how they can solve them immediately for brands and retailers. The graphic below explains their platform. Source: Forbes, How Machine Learning Improves Manufacturing Inspections, Product Quality & Supply Chain Visibility, January 23, 2019

Machine learning is making rapid gains in end-to-end supply chain visibility possible, providing predictive and prescriptive insights that are helping companies react faster than before

Combining multi-enterprise commerce networks for global trade and supply chain management with AI and machine learning platforms are revolutionising supply chain end-to-end visibility.

One of the early leaders in this area is Infor’s Control Center. Control Center combines data from the Infor GT Nexus Commerce Network, acquired by the company in September 2015, with Infor’s Coleman Artificial Intelligence (AI) Infor chose to name their AI platform after the inspiring physicist and mathematician Katherine Coleman Johnson, whose trail-blazing work helped NASA land on the moon. Be sure to pick up a copy of the book and see the movie Hidden Figures if you haven’t already to appreciate her and many other brilliant women mathematicians’ many contributions to space exploration. ChainLink Research provides an overview of Control Center in their article, How Infor is Helping to Realise Human Potential, and two screens from Control Center are shown below.

Machine learning is proving to be foundational for thwarting privileged credential abuse which is the leading cause of security breaches across global supply chains

By taking a least privilege access approach, organisations can minimise attack surfaces, improve audit and compliance visibility, and reduce risk, complexity, and the costs of operating a modern, hybrid enterprise. CIOs are solving the paradox of privileged credential abuse in their supply chains by knowing that even if a privileged user has entered the right credentials but the request comes in with risky context, then stronger verification is needed to permit access.  

Zero Trust Privilege is emerging as a proven framework for thwarting privileged credential abuse by verifying who is requesting access, the context of the request, and the risk of the access environment.  Centrify is a leader in this area, with globally-recognised suppliers including Cisco, Intel, Microsoft, and Salesforce being current customers.  Source: Forbes, High-Tech’s Greatest Challenge Will Be Securing Supply Chains In 2019, November 28, 2018.

Capitalising on machine learning to predict preventative maintenance for freight and logistics machinery based on IoT data is improving asset utilisation and reducing operating costs

McKinsey found that predictive maintenance enhanced by machine learning allows for better prediction and avoidance of machine failure by combining data from the advanced Internet of Things (IoT) sensors and maintenance logs as well as external sources. Asset productivity increases of up to 20% are possible and overall maintenance costs may be reduced by up to 10%. Source: Digital/McKinsey, Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? (PDF, 52 pp., no opt-in).

Interested in hearing industry leaders discuss subjects like this and sharing their experiences and use-cases? Attend the Cyber Security & Cloud Expo World Series with upcoming events in Silicon Valley, London and Amsterdam to learn more.

Bobby Hellard

Keumars Afifi-Sabet

Keumars Afifi-Sabet

Dale Walker

HSBC has been moving towards a cloud-first world – and the bank's latest endeavour has shed light on how it is pushing ahead in the DevOps sphere.

The company is investing $10 million (£7.8m) in capital investment to CloudBees, the continuous delivery software provider and arbiter of open source automation server Jenkins. 

This is by no means an entirely altruistic act, with HSBC using CloudBees significantly since 2015 in order to bolster its software delivery system. The companies had previously gone public about their relationship; HSBC was at a CloudBees event in April, as reported by Computerworld UK.

Regular readers of this publication will be aware of the bank's cloudy aspirations, in particular its relationship with Google Cloud. In 2017 Darryl West, HSBC CIO, took to the stage at Google Next in San Francisco to discuss the companies' collaboration. West noted that the total amount of data the company held at the time was more than 100 petabytes, and that, having dipped their toes into the Hadoop ecosystem as far back as 2014, it had been a 'tough road' in some places.

Nevertheless, the DevOps side continues to expand. Only last week the company began to advertise for a big data DevOps engineer role. The job, based at Canary Wharf, requires experience on Google Cloud, or other suitable cloud vendor, as well as skills in Java, Scala and Spark on the programming side, alongside SQL, relational database, and Elasticsearch expertise.

"We invest in technologies which are strategically important to our business, and which help us serve our customers better," said Dinesh Keswani, chief technology officer for HSBC shared services. "The DevOps market is growing fast, as organisations like us drive automation, intelligence and security into the way we deliver software. CloudBees is already a strategic business partner of HSBC; we are excited by our investment and by the opportunity to be part of the story of continuous delivery."

From CloudBees' perspective, the investment takes the company's overall funding to more than $120 million. Among the firm's recent bets include the acquisition of Electric Cloud in April, as well as leading the launch of the Continuous Delivery Foundation in March, alongside Google and the Linux Foundation. CEO Sacha Labourey said the funding would be used for growing strategic partnerships and accelerating business growth.

Interested in hearing industry leaders discuss subjects like this and sharing their experiences and use-cases? Attend the Cyber Security & Cloud Expo World Series with upcoming events in Silicon Valley, London and Amsterdam to learn more.

In today’s always-on world, customer expectations have changed. Competitive differentiation is delivered through rapid software innovations, the ability to respond to issues quickly and by releasing high-quality code with minimal interruptions. DevOps isn’t some far off goal; it’s methodologies and practices are a response to this demand. The demand to go faster. The demand for more uptime. The demand to innovate. In this keynote, we will cover the Nutanix Developer Stack. Built from the foundation of software-defined infrastructure, Nutanix has rapidly expanded into full application lifecycle management across any infrastructure or cloud .Join us as we delve into how the Nutanix Developer Stack makes it easy to build hybrid cloud applications by weaving DBaaS, micro segmentation, event driven lifecycle operations, and both financial and cloud governance together into a single unified stack.

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«At the keynote this morning we spoke about the value proposition of Nutanix, of having a DevOps culture and a mindset, and the business outcomes of achieving agility and scale, which everybody here is trying to accomplish,» noted Mark Lavi, DevOps Solution Architect at Nutanix, in this SYS-CON.tv interview at @DevOpsSummit at 20th Cloud Expo, held June 6-8, 2017, at the Javits Center in New York City, NY.

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Sold by Nutanix, Nutanix Mine with Veeam can be deployed in minutes and simplifies the full lifecycle of data backup operations, including on-going management, scaling and troubleshooting. The offering combines highly-efficient storage working in concert with Veeam Backup and Replication, helping customers achieve comprehensive data protection for all their workloads — virtual, physical and private cloud —to meet increasing business demands for uptime and productivity.

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“Cloud-native” is an approach for building applications which often incorporates microservices, containers, and cloud services on dynamically orchestrated platforms. There can be a steep learning curve, but these techniques and technologies fully exploit the advantages of the cloud computing model.

Managing application operations through agile DevOps processes and continuous delivery pipelines empowers organisations to more easily build and run new applications in modern environments such as public, private, and hybrid clouds. 

This dramatic shift in how applications are architected and deployed has huge potential for IT, leading to lower operational costs, boosted performance, greater efficiency, and increased business agility. At a broad level, this translates into acclerated speed to market, the ability to support rapid expansion, and more margin as departments pay for additional resources only as needed.

“Disruption-forward” companies like Airbnb, Netflix, and Uber are grabbing the lion’s share of their respective markets due to their newer, faster, more efficient, user-centric interfaces and services. Built in the cloud and without a physical footprint, these tech-driven businesses routinely and safely pivot and scale to meet customer demands at a pace that most enterprises—struggling to migrate from legacy technology—can only envy from afar.

If it ain’t broke…

The problem is, shifting to cloud-native not only requires fundamental changes to IT architectures, but to the entire IT economy that supports it. That’s a transformation IT department heads, met with shrinking budgets, often aren’t willing to—or simply can’t—entertain.

Pundits have talked digital transformation to death for at least five years—a conversation where cloud-native technologies feature heavily. A few businesses have embraced this opportunity with gusto, pulling ahead of competitors or disrupting markets outright. But most enterprises, especially those most risk-averse, haven’t made many changes. Sadly, the IT mantra, “if it ain’t broke, don’t fix it,” still dominates.

The IT professional is ultimately responsible for keeping senior leadership teams on track when it comes to the technological investments planned to move the needle for their organisations. But making the case for cloud-native isn’t always easy. It requires not only a financial investment, but a significant IT commitment for the build, from HR on a cultural front, and most importantly, recognition from the COO and board that IT truly is a strategic driver for the organisation.

Managing new complexity

With the promise of greater efficiency, flexibility, and scalability, it’s easy to see why ambitious IT pros and strategy leaders among us are pushing our organisations to move towards cloud-native approaches. But these benefits bring new complexity that needs to be managed correctly.

IT pros tasked with managing the shift to a cloud-native architecture need to devise new and creative methods if they are to successfully manage and navigate this shift. It’s not only about putting the right tools in place, but also the need to create a receptive and adaptable system for continued advancements.

Effective monitoring is essential. The traditional success reporting model favoured by IT teams is the SLA (service level agreement). But SLAs only represent the minimal acceptable performance IT is willing to accept, while management is looking for a report of business success and ROI on transformation investments.

For IT pros looking to make a business case for cloud-native, the right monitoring and reporting systems are critical. Reporting only minimal acceptable performance prevents IT from being part of the discussion. IT must run in close alignment with the business to mitigate risk, allow transformation, and prove success to build budgets—and truly change culture.

Skills worth investing

Even once the business case has been made and management has bought in, the skills gap remains one of the biggest but often overlooked considerations when undergoing huge transitions such as this. The 2019 SolarWinds IT Trends Report found 70% of all tech pros surveyed are not confident in having all the necessary skills to successfully manage their IT environments over the next three to five years. In fact, almost one in five (19%) U.K. IT pros felt unequipped to implement or manage automation and orchestration with their current skillset.

Without improvement in time and budget constraints, the majority of tech pros (70%) say they will be unable to confidently manage future innovations. This reality ultimately puts businesses at risk of performance and competitive advantage losses, making the prioritisation of skills and career development for tech pros paramount.

A turning tide

The cloud-native shift is a rare opportunity for companies both new and established. While most IT departments are just becoming comfortable wrangling their hybrid IT environments, the promise of the next step—cloud-native—can’t happen if we’re too narrowly focused on decades-old metrics and reporting. Keeping the lights on and meeting basic SLAs is no longer enough.  

Fortunately, the tide is turning. Despite challenges, companies are making towards cloud adoption investments that have them more than “running to stand still.” The next few years will see those that adopt a cloud native approach truly soar, while those unable to mitigate transformation risk will fall behind. While it represents new skills investment upfront, this approach pays career and corporate dividends in a fast-changing world.

Interested in hearing industry leaders discuss subjects like this and sharing their experiences and use-cases? Attend the Cyber Security & Cloud Expo World Series with upcoming events in Silicon Valley, London and Amsterdam to learn more.