Business Process Optimization

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Why Hypotheses Beat Goals

  • Blog
  • Read Time: 6 min 

Companies that aggressively pursue learning must accept the possibility of failure. But simply setting goals and being nonchalant if they fail is inadequate. Instead, companies should focus organizational energy on hypothesis generation and testing. Hypotheses force individuals to articulate in advance why they believe a given course of action will succeed. A failure then exposes an incorrect hypothesis — which can more reliably convert into organizational learning.

Machine Learning in the Travel Industry: The Data-Driven Marketer’s Ticket to Success

Leading marketers in the travel sector are using machine learning not only to measurably improve business outcomes but to fundamentally redefine what those outcomes should be. Travel marketers who take advantage of the large volumes of data their organizations collect will continue to pull ahead of their rivals.

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AI-Driven Leadership

  • Blog
  • Read Time: 7 min 

Not many companies are there yet, but there’s a developing framework for what it takes to lead an AI-driven company. Leaders at the forefront of AI have seven key attributes: They learn the technologies; establish clear business objectives; set an appropriate level of ambition; look beyond pilots and proofs of concept; prepare people for the journey; get the necessary data; and orchestrate collaborative organizations.

Can IT Be Too in Sync With Business Strategy?

IT alignment can produce inertia — unless it’s accompanied by the right culture. Sure, closely aligning IT with the rest of a company’s strategy can cut costs and improve the ability to collect data, facilitating the creation of early-warning systems and operational dashboards. But a less regimented approach has its place, too, allowing responses to changing business and economic conditions that are swift and creative.

Following the Digital Thread: Revolutionizing Supply Chains

  • Video | Runtime: 0:05:53

  • Read Time: 2 min 

In Part 1 of our eight-part video series, we explore the real power of the digital thread, which lies not just in a “cradle-to-grave” virtual rendering of the manufacturing process, but also in the possibility of taking the lessons learned from analyzing product performance and applying them to future generations of the manufacturing process and product design.

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Winning With Open Process Innovation

Managers in manufacturing companies often keep process innovation activities tightly under wraps. Some companies have good reasons for keeping process innovations concealed. However, the authors’ research suggests that for most manufacturers, such defensiveness deprives companies of a valuable source of ideas for productivity improvement. Many manufacturers, they argue, can benefit from sharing process innovations rather than keeping them secret.

Your Company Doesn’t Need a Digital Strategy

As sexy as it is to speculate about new technologies such as AI, robots, and the internet of things, the focus on technology can steer the conversation in a dangerous direction. Because when it comes to digital transformation, digital is not the answer. Transformation is. In various industries, including banking, paint, and shipbuilding, digital leaders are finding that technology’s value comes from doing business differently because technology makes it possible.

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When Jobs Become Commodities

Most of us view our jobs as specialized or somehow differentiated, but the world of business and management increasingly feels otherwise. For many organizations today, the next big driver of job commoditization is automation driven by smart machines. Simply put, if a job is viewed as a commodity, it won’t be long before it’s automated. The key for workers whose jobs have traditionally seemed safe: Highlight the tasks that require a human touch.

The Fundamental Flaw in AI Implementation

Many managers are excited about smart machines but are struggling to apply machines’ limited intelligence. Indeed, computers can process data just fine, but to generate competitive advantage from machine learning applications, organizations must upgrade their employees’ skills. Companies will also need to redesign employee accountabilities to empower and motivate them to deploy smart machines when doing so will enhance outcomes.

Participant Questions From the Recent Data and Analytics Webinar: Round 2

On March 15, 2017, MIT SMR held a webinar to share insights from our report, “Analytics as a Source of Business Innovation.” Many participants asked questions during the webinar that we didn’t have time for, so we decided to answer them in blog format instead. This post is the second set of responses.

Questions and Answers About Analytics as a Source of Business Innovation

On March 15, 2017, MIT SMR held a webinar to share insights from our report, “Analytics as a Source of Business Innovation,” which summarizes our findings about the increased ability to innovate with analytics and its benefits across industries. Many participants asked questions during the webinar that we didn’t have time for, so we’ll answer some of them in blog format instead.

Showing 1-20 of 110