Tag Archives: Productivity

Updates from Chicago Booth

When giving feedback, focus on the future
By Sarah Kuta -When managers give performance-improvement feedback to employees, they presumably want the conversations to result in positive changes—not to inspire defensiveness, excuses for poor performance, or skepticism of the managers’ point of view.

Offering forward-looking feedback can help keep such conversations productive, suggests research by Humanly Possible’s Jackie Gnepp, Chicago Booth’s Joshua Klayman, Victoria University of Wellington’s Ian O. Williamson, and University of Chicago’s Sema Barlas.

Performance-improvement feedback often fails when managers spend too much time diagnosing or analyzing what went wrong in the past, according to the researchers. When managers and employees talk about possible next steps and solutions, however, employees tend to be more receptive to the feedback and more likely to intend to act on it, the researchers find.

Recipients respond just as well to predominately negative feedback as they do to positive feedback, so long as the conversation focuses primarily on how the recipient can best move forward, the research suggests. more>


Updates from Chicago Booth

What does business owe society?
Experts from across the globe consider the Friedman doctrine and the social responsibilities of contemporary companies
By Amy Merrick – Fifty years ago, the late Milton Friedman, an economics professor at the University of Chicago who would go on to win the Nobel Prize in Economic Sciences, challenged the argument that businesses have obligations separate from their responsibility to make as much money as possible for shareholders.

In his op-ed for the New York Times, published on September 13, 1970, Friedman declared, “There is one and only one social responsibility of business—to use its resources and engage in activities designed to increase its profits so long as it stays within the rules of the game, which is to say, engages in open and free competition without deception or fraud.”

Friedman was pushing back against the idea that businesses should avoid price increases to hold down inflation, pay more toward mitigating pollution than the law required, or hire less-qualified workers to reduce poverty. He did not say that businesses should profit by any means necessary; he wrote that corporate executives should conform “to the basic rules of the society, both those embodied in law and those embodied in ethical custom.”

Today, some executives and economists reject Friedman’s conception of businesses’ social obligations. Last year, the Business Roundtable, which represents the CEOs of some of America’s largest companies, updated its statement on the purpose of a corporation, emphasizing a “fundamental commitment to all of our stakeholders” and outlining a list of responsibilities: deliver value to customers, invest in employees, deal fairly and ethically with suppliers, support the communities in which they work, and generate long-term value for shareholders. Some have argued that global challenges such as COVID-19 and climate change are existential threats that require leadership from businesses—for the sake of their own survival, as well as that of the broader society. more>


Updates from McKinsey

Scaling a start-up: Launching innovative products in international markets
Expanding internationally is a major step in the life of a start-up, one involving a number of important considerations. Here’s how Impossible Foods is tackling the challenge.

Key insight #1: International product launches in smaller markets foster agility and offer invaluable learnings.

Key insight #2: When prioritizing resources between growing existing products and launching new ones, learn how to say ‘not now’ rather than simply ‘no.’

Key insight #3: International launch teams need to be strategic, flexible, and plugged into the local market and culture.

Key insight #4: Different markets call for different start-up playbooks.

Key insight #5: Dealing with the business impact of COVID-19 requires the flexibility to adapt to current conditions while still pursuing long-term goals.

By Tomas Laboutka and Nick Halla – For many start-ups, the decision to expand internationally is not straightforward. CEOs must choose between the growth-opportunity costs in their home market and the resources required for the new markets. The product has to be ready and the teams set up to handle the increased complexity. What was your decision-making process? When did you know you were ready?

I think it’s a bit different for every business. For us, it was really driven by our mission to produce and deliver the most delicious meat, fish, and dairy foods the world has ever experienced—all made from plants, with a much lower environmental impact to help restore the planet’s biodiversity. We launched in the United States in 2016, but Asia consumes 44 percent of the global meat production, so we knew we needed to address the Asian markets early.

We entered Hong Kong in April 2018 and saw it as a great first international market that was small enough so we could pivot and change relatively quickly. Hong Kong is a mecca for global cuisine, so we had a large “lab” to start learning about consumers and their preferences. A lot of the aspects that we designed into product 2.0, from cooking performance to consumer tastes, we learned in Hong Kong. Our product 1.0 needed to be more robust in cooking so it could handle the diverse cooking methods and different types of cuisine. Impossible 2.0 helped us tap into local culture and accelerate growth. Additionally, we started learning how to run a global business and how to operate as a global company. As we continue to scale, we now know how to run the processes so we can build in the necessary complexity early on.

Obtaining organization-wide support to launch internationally was critical for our success. Food-industry market launches require total commitment, as opposed to doing a pilot test in consumer tech, where the scale and complexity are so much smaller. We needed to move fast. We had to make a call to go for it, even though we couldn’t check all the boxes up front on the first launch and had to learn a lot as we progressed. more>


Updates from McKinsey

How to build a data architecture to drive innovation—today and tomorrow
Yesterday’s data architecture can’t meet today’s need for speed, flexibility, and innovation. The key to a successful upgrade—and significant potential rewards—is agility.
By Antonio Castro, Jorge Machado, Matthias Roggendorf, and Henning Soller – Over the past several years, organizations have had to move quickly to deploy new data technologies alongside legacy infrastructure to drive market-driven innovations such as personalized offers, real-time alerts, and predictive maintenance.

However, these technical additions—from data lakes to customer analytics platforms to stream processing—have increased the complexity of data architectures enormously, often significantly hampering an organization’s ongoing ability to deliver new capabilities, maintain existing infrastructures, and ensure the integrity of artificial intelligence (AI) models.

Current market dynamics don’t allow for such slowdowns. Leaders such as Amazon and Google have been making use of technological innovations in AI to upend traditional business models, requiring laggards to reimagine aspects of their own business to keep up. Cloud providers have launched cutting-edge offerings, such as serverless data platforms that can be deployed instantly, enabling adopters to enjoy a faster time to market and greater agility. Analytics users are demanding more seamless tools, such as automated model-deployment platforms, so they can more quickly make use of new models. Many organizations have adopted application programming interfaces (APIs) to expose data from disparate systems to their data lakes and rapidly integrate insights directly into front-end applications. Now, as companies navigate the unprecedented humanitarian crisis caused by the COVID-19 pandemic and prepare for the next normal, the need for flexibility and speed has only amplified.

For companies to build a competitive edge—or even to maintain parity, they will need a new approach to defining, implementing, and integrating their data stacks, leveraging both cloud (beyond infrastructure as a service) and new concepts and components. more>


Updates from McKinsey

An operating model for the next normal: Lessons from agile organizations in the crisis
Companies with agile practices embedded in their operating models have managed the impact of the COVID-19 crisis better than their peers. Here’s what helped them cope.
By Christopher Handscomb, Deepak Mahadevan, Lars Schor, Marcus Sieberer and Suraj Srinivasan – For many companies, the first, most visible effects of the COVID-19 pandemic quickly created a challenge to their operating and business models. Everything came into question, from how and where employees worked to how they engaged with customers to which products were most competitive and which could be quickly adapted. To cope, many turned to practices commonly associated with agile teams in the hope of adapting more quickly to changing business priorities.

Agile organizations are designed to be fast, resilient, and adaptable. In theory, organizations using agile practices should be perfectly suited to respond to shocks such as the COVID-19 pandemic. Understanding the experiences of agile—or partially agile—companies during the crisis provides insights around which elements of their operating models proved most useful in practice. Through our research, one characteristic stood out for companies that outperformed their peers: companies that ranked higher on managing the impact of the COVID-19 crisis were also those with agile practices more deeply embedded in their enterprise operating models. That is, they were mature agile organizations that had implemented the most extensive changes to enterprise-wide processes before the pandemic.

That suggests implications for less agile companies as economies reopen. Should they set aside the agile practices they adopted during the pandemic and return to their traditional operating models? Or should they double down on agile practices to embrace the more fundamental team- and enterprise-level processes that helped successful agile companies navigate the downturn?

We analyzed 25 companies across seven sectors that have undergone or are currently undergoing an agile transformation. According to their self-assessments, almost all of their agile business units responded better than their nonagile units to the shocks associated with the COVID-19 pandemic by measures of customer satisfaction, employee engagement, or operational performance.

Executives emphasized that the agile teams have continued their work almost seamlessly after the shock, without substantial setbacks in productivity. In contrast, many nonagile teams struggled to transition, reprioritize their work, and be productive in the new remote setup. The alignment between agile teams’ backlogs and their business priorities allowed them to shift focus quickly. more>


Updates from McKinsey

Energizing industrial manufacturing through active performance management
A new approach can help industrials gain greater visibility into performance and capture lasting gains.
By Ryan Fletcher, Kairat Kasymaliev, Abhijit Mahindroo, and Nick Santhanam – Along with its severe human toll, the spread of the coronavirus has exacerbated long-standing challenges in businesses worldwide. For industrial companies—especially those with high-mix, low-volume manufacturing—COVID-19 has increased the already-widespread problems with shop-floor productivity. As supply-chain disruptions affect shipment of critical parts, industrials are struggling to meet their promised customer delivery dates. Within plants, physical-distancing requirements and line closures are disturbing some workflows. These delays often prevent industrials from delivering critical products, including sanitization tools and other equipment to help their customers both fight and recover from the pandemic.

For many years, industrials have deployed lean levers and performance-management initiatives to improve productivity and expand margins. They usually achieved good initial results, but their gains frequently vanished or decreased as managers became distracted and employees returned to their old ways. Some industrials have also offshored production to reduce costs but then encountered substantial challenges and high start-up costs when they tried to replicate their capabilities and skills in new locations. With recent tariffs and travel disruptions creating unprecedented uncertainty, more businesses are considering reshoring production to increase their resilience and flexibility in the “next normal.” That makes manufacturing performance even more important.

A new approach to productivity—active performance management (APM)—may be more likely to deliver lasting gains than previous methods. It focuses on three areas where most current solutions fall short: real-time performance visibility, daily performance planning, and end-to-end accountability. When high-mix, low-volume industrials incorporate APM into their standard workflows, they typically improve productivity by 30 to 50 percent within eight to 12 weeks of deployment while simultaneously increasing on-time deliveries and unlocking additional capacity.

Surprisingly, many high-mix, low-volume manufacturing operations lack real-time visibility into performance. Instead, they review key performance indicators (KPIs) weekly or even monthly. This frequency may be enough to drive performance in high-volume environments, where variability is low and processes are predictable, but it is not well suited to the complexity of a high-mix factory.

Without real-time transparency, supervisors are unlikely to discuss and address issues until their impact has snowballed. more>


Updates from Siemens

Redefine the Line: How automotive trends are changing the ways we move from point A to B
By Tarun Tejpal – The automotive industry has been one of the most dynamic and exciting incubators of technological and product innovation in the modern world. A unique mix of investment, consumer interest, and industry competition has driven this dynamism with a constant search for the next feature, style, or capability to capture the public imagination. At the 1964 New York World’s Fair, General Motors (GM) hoped to capture such interest with the Firebird IV concept car. GM explained, then, that the Firebird IV “anticipates the day when the family will drive to the super-highway, turn over the car’s controls to an automatic, programmed guidance system and travel in comfort and absolute safety at more than twice the speed possible on today’s expressways.” (Gao, Hensley, & Zielke, 2014).

GM’s vision of the future was striking and exciting, but the technology did not yet exist to make it a reality. Ford took a different approach to generating buzz in the market, focusing on the present. Instead of forecasting a future of self-driving cars and super highways, Ford launched a car for “young America out to have a good time”: the Mustang (Gao et al., 2014). It engaged the new generation by providing both transportation and personal expression in a stylish, highly configurable, and inexpensive package. Ford estimated it would sell 100,000 Mustangs, but one year after the launch it had sold over 400,000 (Gao et al., 2014).

Vehicles are now a central feature of everyday life. Since 1964, global vehicle sales have grown by nearly 3 percent on average each year, nearly double the rate of population growth, resulting in one billion vehicles on the road today (Gao et al., 2014).

However, large-scale trends, such as a surging Chinese automotive market, electrification, and urbanization, are beginning to affect the form and function of vehicles and personal mobility systems. more>


Updates from McKinsey

How to restart your stalled digital transformation
Most digital initiatives sputter before they take full effect. A new survey finds that organizations stand a good chance of recovering lost momentum because slowdowns typically happen for reasons within their control.
McKinsey – At organizations pursuing digital transformations, more than seven in ten survey respondents say the progress of these efforts has slowed or stalled at some point. In the latest McKinsey Global Survey on the topic, we set out to understand what organizations can do to prevent burnout or to restart their engines if burnout occurs during these transformations, which previous research has found have a lower success rate than do more traditional transformations. The good news is that in most cases, organizations can prevent or overcome a loss of momentum.

More than 60 percent of respondents who report stalled digital transformations attribute the problem to factors that—with the right discipline and focus—organizations can control in the near term to medium term. This finding runs counter to widespread assumptions that external pressures, such as market disruptions or regulatory changes, pose the biggest threats to digital initiatives. More commonly reported sources of derailed progress include resourcing issues, lack of clarity or alignment on a company’s digital strategy, and poor quality of the digital strategy to begin with.

If a digital transformation stalls, the results suggest that organizations can regain momentum by implementing rigorous change-management and internal-communications programs and clarifying the transformation’s projected impact, which can help build alignment and commitment. For scaling digital programs beyond the pilot phase—the first stumbling block in a transformation’s execution—clarity on the time frame and expected economic impact is important, as is partnering with operations. Should an intervention be needed to reenergize a transformation, having the CEO step in appears to be advantageous. Further lessons come from respondents at companies that avoid stalling in the first place. They often say their organizations maintain momentum by obtaining strong alignment and strategic clarity before a transformation gets under way. more>


The future will be shaped by what global productivity growth does next

By Warwick J. McKibbin and Adam Triggs – Productivity growth is a shadow of its former self. It’s one-tenth of what it was 40 years ago in advanced economies, and even emerging economies are struggling to replicate the growth of the past. As the fundamental driver of long-run living standards, weak productivity growth is a serious problem. Lower living standards, bigger budget deficits, fewer jobs, lower wages, and higher inequality await if things don’t improve.

What is most striking about this period of low productivity is that it coincides with enormous advances in technology. An extra 3.5 billion people have gained access to the internet. The processing power of computers has increased exponentially while their cost and size have plummeted. Smartphones have multiplied, and online businesses have flourished. Email, GPS and advanced software have become widespread. The sharing economy is unlocking the full potential of idle cars and empty rooms and houses. Information and communication technologies (ICT) and artificial intelligence (AI) have reshaped many industries. The accumulated history of human knowledge is now at our fingertips.

Robert Solow famously remarked that “you can see the computer age everywhere but in the productivity statistics.” Economists have put forward a variety of explanations for the so-called “Solow paradox,” each of which implies a radically different path for productivity growth in the future. Our chapter in the just-published book “Growth in a Time of Change” models each of these possible scenarios to explore what the world might look like depending on who turns out to be correct.

Let’s start with the optimists. Some economists, like the 2018 Nobel Laureate William Nordhaus and Iraj Saniee and his co-authors at Nokia Bell Labs, point to historical data showing long lag times between technological advances and increases in productivity. For these economists, a big surge in productivity is just around the corner.

If the optimists are correct and global productivity growth takes off rapidly, many of the world’s problems go away. Investment, wages, and employment rise sharply. GDP increases and inequality declines. While all sectors experience an investment boom, the durable goods sector experiences the largest increase. The sharp increase in investment sees an increased demand for investment goods, particularly durable manufactured goods and the energy and mining resources required to produce them. Countries that export durable manufactured goods (such as Germany) and energy and mining resources (such as Australia) benefit significantly. Secular stagnation becomes a thing of the past.

But new challenges emerge. The global economy is a closed system, so the resources to finance this boom in investment and production must come from somewhere: either from increased government savings or from reductions in current consumption. If governments don’t act, or if financial market rigidities prevent access to global capital markets, consumption can fall. The shock also triggers transitions that require the redeployment of labor and capital from declining sectors to booming ones. Rigid labor markets and oligopolistic product markets hamper this adjustment. Thus, the full benefits of the boom can be squandered, and its benefits may be short-lived and distributed more unequally between capital and labor.

Now consider the pessimists. Some economists, notably Northwestern University’s Robert Gordon, argue that the technological advances in recent decades won’t deliver the sort of productivity increases that we saw from the inventions of the last century. Facebook and Netflix are great, but they are no match for electricity and indoor plumbing. more>

Why hiring the ‘best’ people produces the least creative results

By Scott E Page – The complexity of modern problems often precludes any one person from fully understanding them. Factors contributing to rising obesity levels, for example, include transportation systems and infrastructure, media, convenience foods, changing social norms, human biology and psychological factors.

Designing an aircraft carrier, to take another example, requires knowledge of nuclear engineering, naval architecture, metallurgy, hydrodynamics, information systems, military protocols, the exercise of modern warfare and, given the long building time, the ability to predict trends in weapon systems.

The multidimensional or layered character of complex problems also undermines the principle of meritocracy: the idea that the ‘best person’ should be hired. There is no best person. When putting together an oncological research team, a biotech company such as Gilead or Genentech would not construct a multiple-choice test and hire the top scorers, or hire people whose resumes score highest according to some performance criteria. Instead, they would seek diversity. They would build a team of people who bring diverse knowledge bases, tools and analytic skills. That team would more likely than not include mathematicians (though not logicians such as Griffeath). And the mathematicians would likely study dynamical systems and differential equations.

Believers in a meritocracy might grant that teams ought to be diverse but then argue that meritocratic principles should apply within each category. Thus the team should consist of the ‘best’ mathematicians, the ‘best’ oncologists, and the ‘best’ biostatisticians from within the pool.

That position suffers from a similar flaw. Even with a knowledge domain, no test or criteria applied to individuals will produce the best team. Each of these domains possesses such depth and breadth, that no test can exist.

Consider the field of neuroscience. Upwards of 50,000 papers were published last year covering various techniques, domains of inquiry and levels of analysis, ranging from molecules and synapses up through networks of neurons. Given that complexity, any attempt to rank a collection of neuroscientists from best to worst, as if they were competitors in the 50-metre butterfly, must fail.

What could be true is that given a specific task and the composition of a particular team, one scientist would be more likely to contribute than another. Optimal hiring depends on context. Optimal teams will be diverse.

Yet the fallacy of meritocracy persists. Corporations, non-profits, governments, universities and even preschools test, score and hire the ‘best’. This all but guarantees not creating the best team.

Ranking people by common criteria produces homogeneity. And when biases creep in, it results in people who look like those making the decisions. That’s not likely to lead to breakthroughs. more>