Predicting a Recession
It’s overdue. Predicting the onset of a recession is difficult, but a downturn likely will arrive soon, with the current economic expansion now more than 10 years old, long by historical standards.
Signs of overleverage in the corporate sector, combined with geopolitical uncertainty – including the China-US trade war, Brexit and economic instability in some European countries – suggest the next recession is not far off.
For corporate leaders, however, the exact timing and duration of a recession matter less than being ready to seize the moment early, when they have more options. Getting ahead of the curve avoids the painful alternative – being forced to react hastily in a crisis. Bain & Company research shows that well-prepared companies emerged as winners during and after past recessions. They managed a strong defense and offense in parallel, reining in costs while simultaneously reinvesting in growth.
The next downturn will figure as just one element roiling the global economy. Several structural changes will combine to sound the starting gun to a new business cycle, including:
The end of the nontech business.
An array of evolving technologies will substantially alter customer behavior and demand in many sectors, disrupting both volume and price. In the automotive industry, shared mobility services and the shift to autonomous and electric vehicles could gut the economic returns of many manufacturing plants and assets in six to eight years – just one product cycle. In retail, digital-first insurgent brands with healthy balance sheets may take even more market share in a downturn, compounding the damage to many traditional retailers.
At the same time, new technologies are ramping up efficiencies in areas such as supply chain and manufacturing. Automation technologies, in particular, will accelerate to help companies address the dwindling supply of labor as more baby boomers move into retirement and labor force growth slows.
The end of low-interest rates.
Interest rates still hover near a six-decade low (see Figure 1). Even if central bankers hold rates low during a downturn to help stimulate their economies, we expect to see rates eventually rise. This potential change in the interest rate environment will be a new regime for most management teams and should prompt them to take a multiyear view of their capital structure and the timing of investments. A higher cost of capital will put pressure on capital spending, so if companies want to invest in technology, growth opportunities or acquisitions, the time is now.
Downturns Upend the Playing Field
These long-term trends will harden the divide between winners and losers, favoring those who act before the downturn. Headed into the global financial crisis a decade ago, a group of almost 3,900 companies worldwide that we ran through Bain’s Sustained Value Creators analysis posted double-digit earnings growth, on average, from 2003 to 2007. As soon as the storm hit, performance diverged sharply: The winners grew at a 17% compound annual growth rate (CAGR) during the downturn, compared with 0% among the losers. What’s more, the winners locked in gains to grow at an average 13% CAGR in the years after the downturn, while the losers stalled at 1%.
Read more at Beyond the Economic Downturn: Recession Strategies to Take the Lead Now!
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How To Improve Supply Chains With Machine Learning: 10 Proven Ways
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, revolutionizing 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 analyzes 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 revolutionizing 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.
- 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.
- 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.
- Reducing forecast errors up to 50% is achievable using machine learning-based techniques.
- DHL Research is finding that machine learning enables logistics and supply chain operations to optimize capacity utilization, improve customer experience, reduce risk, and create new business models.
- Detecting and acting on inconsistent supplier quality levels and deliveries using machine learning-based applications is an area manufacturers are investing in today.
- 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.
- 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.
- Machine learning is proving to be foundational for thwarting privileged credential abuse which is the leading cause of security breaches across global supply chains.
- Capitalizing on machine learning to predict preventative maintenance for freight and logistics machinery based on IoT data is improving asset utilization and reducing operating costs.
Read more at How To Improve Supply Chains With Machine Learning: 10 Proven Ways
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