The COVID-19 crisis has been a humbling lesson in the difficulty of epidemiological modeling and predictions, however, the broad implications of its disruptive economic effects were easier to anticipate. In those simple days before the mass lockdowns and layoffs, I opined that the crisis could result in a systemic shock that breaks through business inertia to catalyze multi-dimensional change, concluding that (emphasis added):
The human and economic toll from Black Swan events like the coronavirus epidemic or Great Recession are immense and, for those afflicted and taken by the deadly disease, impossible to measure. Indeed, some businesses also will not survive. For the majority that does, the crisis can stimulate much positive change by cutting through stifling bureaucracies, eliminating moribund practices and inciting radical changes that entail some short-term pain, but yield long-term gain.
The intervening months have provided a steady stream of examples here at diginomica, including the implications of pervasive and sustained WFH policies, increased automation and intelligence in manufacturing and food production, a re-emphasis on network and user security, an acceleration of cloud adoption and more aggressive application of AI to various business processes such as supply chain management (SCM). In almost all of these cases, save the replacement of phone calls and in-person meetings with videoconferences, the crisis accentuated and accelerated pre-existing activities and trends.
One glaring exception to IT budget-cutting
Most of these changes add to already stretched budgets, albeit counterbalanced in the notable case of sustained remote work by lower real estate operating costs. Indeed, the resulting economic destruction — the UK GDP plummeted by one-fifth in April — has forced significant cuts in business employment and investments, including IT. IDC's latest forecast predicts a 5.1% decline in IT spending worldwide, a full 10 points less than its optimistic January estimate, with the cuts spanning virtually every technology segment industry. Indeed, the one area still expected to see growth, infrastructure spending, is primarily the result of an acceleration in cloud usage.
As I detailed a column summarizing recent survey data about IT cloud usage and plans, the COVID-19 lockdowns and economic disruption have caused many organizations to reassess their cloud plans, with almost half of organizations expecting to increase cloud usage above pre-crisis plans, 30% significantly. At least one influential industry analyst, Dan Ives, sees the same trend, saying that the pandemic could mark "a key turning point" in enterprise cloud adoption, adding that his earlier projection that enterprises will use cloud services for 55% of their workloads by 2022 "now appears conservative."
Videoconferencing has been an area of remarkable growth for cloud services, with Zoom reporting revenue 1.7-times that of a year ago, Microsoft seeing Teams daily usage jump by 70% in the month of April and Google adding 3 million Meet users per day. By mid-May, the number of daily Meet users in the UK was 20-times its pre-pandemic level in February.
Despite the success of cloud SaaS in helping organizations adapt to WFH realities, there are myriad other categories of cloud infrastructure and application services that are proving their worth as organizations adapt to both demand and supply disruptions that were unimaginable a mere six months ago.
AI enhancing SCM, manufacturing and logistics services
Online commerce has been a primary beneficiary of the lockdowns and pandemic-induced behavioral changes by consumers. When asked about the company's rapid pivot to online retailing, L'Oreal's chief digital officer recently said:
In e-commerce, we achieved in eight weeks what it would have otherwise taken us three years to do.
Unfortunately, the crisis exposed significant shortcomings throughout product manufacturing and distribution supply chains with the former unable to react to dramatic shifts in product mix from wholesale to retail, while the latter we left with insufficient logistics capacity to handle purchase volumes that sometimes exceeded twice normal levels. Supply chain snafus were complicated by shipping disruptions that resulted in container shipping volume down 20% and air cargo down about 10% in Q1.
As organizations up and down product supply chains seek ways to become more efficient and nimble — able to spot and react to changing market conditions, consumer preferences and localized disruptions — there is more interest and investment in more intelligent SCM, ERP and logistics (warehouse and transportation) management software. Such products increasingly use machine learning (ML) and deep learning (DL) models to analyze myriad data sources, model various scenarios and predict changes in supply and demand.
Expanded use of AI in backend business management systems is led by companies like Aera Technology Blue Yonder, Dematic IQ, High Jump, Llamasoft, Manhattan Associates, Noodle.ai and Swisslog. Guarav Palta, a GM a Noodle.ai, recently explained the motivation behind applying AI to SCM this way (emphasis added):
Rules-based systems for allocation and deployment of products have been rendered largely unusable by extreme variability and noise in the current environment. Existing ERP and Supply Chain Planning systems provide a robust repository of basic supply chain data (eg forecasts, orders, inventory, production, shipments and so on) but use simple rules based on standard assumptions to generate the plan. ...
What planners and operators need are modern AI-powered systems to create and manage demand and inventory plans in such times. They need the help of sophisticated AI inference engines crunching billions of data points to recommend actions, focusing on those SKUs that are predicted to have the greatest financial impact.
SCM software has been traditionally deployed as on-premises software applications, however, as we have seen with SAP, Oracle, Workday and others, the overhead of managing such complicated systems, coupled with the benefits of cloud environments, will lead more organizations to seek SaaS products for their next-generation SCM system. Indeed, Microsoft recently elucidated the advantages of running SAP on Azure by highlighting several compelling points.
- Ability to rapidly adapt to changing conditions, business needs (agility).
- Rapid and virtually unlimited scalability of cloud infrastructure.
- Better infrastructure security, data redundancy and business continuity than most on-premises installations.
- Access to sophisticated data collection and analysis tools to enhance the value of data in SAP.
- Frequent service updates and access to both Microsoft and third-party add-in products.
AI SCM - a small, but growing market
These same benefits apply to other enterprise systems such as SCM and logistics management. As these products incorporate advanced ML and DL models, the complexity and expense of training, testing and deploying AI systems tips the balance further in favor of the cloud. Thus, IDC expects that for Windows-based systems, "a majority of SCM and Data Management workloads are expected to be deployed on cloud-based infrastructure by 2023." By one estimate, the market for AI-based SCM software will grow 40 percent annually for the next several years, hitting $10 billion in sales by 2024. Two other estimates (here and here) project the revenue for SCM AI-as-a-service to be $1 to 2 billion in the same period.
A recent survey of supply chain professionals found that a majority of respondents believed that robotics, automation, predictive analytics and AI all have "potential to disrupt or create competitive advantage." Despite the promise for AI to significantly enhance SCM and logistics performance, adoption remains small. While 59% of these SCM practitioners currently use cloud infrastructure, only 28% have deployed predictive analytics systems and a mere 12% use AI technologies.
The low adoption rate for AI technologies likely results from their novelty and the difficulty of hiring personnel with knowledge in the field. Supply chain disruptions from the pandemic that fuel interest in AI for SCM will eventually eliminate the novelty of AI technology, however, cloud AI services can significantly mitigate the expertise gap.
The pandemic created economic and personal havoc via lockdowns, supply chain disruptions and a rapid contraction in consumer spending, forcing businesses to react in record time. Many have done so by accelerating existing cloud plans, shifting workloads to cloud infrastructure and migrating on-premises applications to SaaS subscriptions. Another technology that has become both critical and urgent is applying AI and other predictive analytic techniques to business processes like SCM, logistics, manufacturing processes and even food production.
As I recently wrote in covering the burgeoning use of AI for food traceability and manufacturing (MES), software to automate food manufacturing, inspection and logistics and increase productivity, efficiency, regulatory compliance and profitability. Applying data-driven, AI-powered systems to SCM and logistics promises the same benefits and cloud infrastructure and services make an ideal delivery platform.