Nowe Technologie Archive

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Growing your own agility coaches to adopt new ways of working

Agile coaches play a vital role in enterprise-wide agile transformations. To develop enough coaches, companies should create specialized training academies.

Companies are increasingly looking to infuse agility into their operating models. However, as organizations attempt to scale these efforts across their entire business, new challenges that simply didn’t exist at the micro level are beginning to surface. These challenges are especially prevalent where traditional organization silos need to interact.

The big realization for many companies is that scaling agile is not simply a matter of replicating agile practices across more teams. This is why trying to adapt project-management offices (PMOs) to support agile projects or bringing in more scrum masters is unlikely to be effective (see sidebar, “The scrum master’s role in scaling agile”). Rather, agility as an operating model requires the rewiring of core enterprise-wide processes. With this comes a need for the organization to operate differently.
The degree of change required to adopt agile ways of working across an entire organization is simply too large to repurpose existing roles and structures. Only by investing in agility coaches—and a comprehensive program to identify, train, and support them—can companies expect to scale and sustain agile across the enterprise.

To ensure the success of the agility coaching academy, it is critical to have the right support and leadership structure. Typically, the academy is led by a full-time executive who reports to either the CHRO or some other member of the C-suite depending on who is really driving the agile transformation—it could be the CIO, the head of transformation, or the COO. The academy lead is accountable for the following:

  • Setting the strategy and defining the delivery road map for the academy
  • Running the day-to-day operations of the academy, such as building and refining the academy backlog
  • Leading the recruitment of coaches
  • Overseeing learning and development of the trainee agility coaches, and administering the learning and development of graduated coaches
  • Defining the evaluation criteria and mechanisms to measure effectiveness of the agility coaches
  • Deploying the right agility coaches to the right areas and teams
  • Overseeing performance evaluations for the agility coach cohort

More: https://www.mckinsey.com/business-functions/

By Amit Anand, Sahil Merchant, Arun Sunderraj, and Belkis Vasquez-McCall

About the authors: Amit Anand is a senior expert in McKinsey’s Sydney office, Sahil Merchant is a partner in the Melbourne office, Arun Sunderraj is a digital expert in the New York office, and Belkis Vasquez-McCall is a partner in the New Jersey office.

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The Company of the Future

In the coming decade, companies will increasingly need to compete on the rate of learning. Technology promises to play a critical role: artificial intelligence can detect patterns in complex data sets at extreme speed and scale, enabling dynamic learning. This will allow organizations to constantly adapt to changing realities and surface new opportunities, which will be increasingly important in an uncertain and fast-changing environment.

But for companies to compete on learning, it is not enough to merely adopt AI, which alone can accelerate learning only in individual activities. As with previous transformative technologies, unlocking the full potential of AI—and of humans—will require fundamental organizational innovation.

In other words, to win the ’20s, leaders will need to re-invent the enterprise as a next-generation learning organization.

The next-generation learning organization will need to be redesigned to fulfill several key functions: (See Exhibit 1.)

  • Learning on All Timescales. The growing opportunity and need to learn on faster timescales, driven by technological innovation, is well known—algorithmic trading, dynamic pricing, and real-time customized product recommendations are already a reality in many businesses. But it is perhaps under-appreciated that slow-moving forces are also becoming more important. For example, trade institutions, political structures, wealth stratification, and social attitudes are slowly changing in ways that could have a profound impact on business. Gone are the days when business leaders could focus only on business and treat these broader variables as constants or stable trends. But such shifts unfold over many years or even decades. In order to thrive sustainably, businesses must learn on all timescales simultaneously.
  • Combining Humans and Machines Optimally. Machines have been crucial components of businesses for centuries—but in the AI age, they will likely expand rapidly into what has traditionally been considered white-collar work. Instead of merely executing human-directed and designed processes, machines will be able to learn and adapt, and will therefore have a greatly expanded role in future organizations. Humans will still be indispensable, but their duties will be quite different when complemented or substituted by intelligent machines.
  • Integrating Economic Activity Beyond Corporate Boundaries. Businesses are increasingly acting in multicompany ecosystems that incorporate a wide variety of players. Indeed, seven of the world’s largest companies, and many of the most profitable ones, are now platform businesses. Ecosystems greatly expand learning potential: they provide access to exponentially more data, they enable rapid experimentation, and they connect with larger networks of suppliers of customers. Harnessing this potential requires redrawing the boundaries of the enterprise and effectively influencing economic activity beyond the orchestrating company.
  • Evolving the Organization Continuously. The need for dynamic learning does not apply just to customer-facing functions—it also extends to the inner workings of the enterprise. To take advantage of new information and to compete in dynamic, uncertain environments, the organizational context itself needs to be evolvable in the face of changing external conditions.
  • By reconceiving the external and internal workings of the organization as a flexible, evolving ecosystem, businesses can handle much greater dynamism and complexity. This requires subjecting all aspects of the organization to market forces, enabling it to learn and adapt in response to new opportunities. And it requires internal systems that adjust automatically to new information, allowing learning and resource reallocation to occur at algorithmic speed. When combined, these capabilities can create a “self-tuning enterprise” that constantly learns and evolves according to its environment. (See Exhibit 3.)To harness the power of ecosystems throughout and beyond the organization, leaders must:
    • Engage external partners to create a shared vision of the future.
    • Develop capabilities for collaboration and information sharing at scale—for example, platforms and APIs.
    • Redesign internal processes to be more adaptive and data-driven, allowing the organization to become “self-tuning.”

By reconceiving the external and internal workings of the organization as a flexible, evolving ecosystem, businesses can handle much greater dynamism and complexity. This requires subjecting all aspects of the organization to market forces, enabling it to learn and adapt in response to new opportunities. And it requires internal systems that adjust automatically to new information, allowing learning and resource reallocation to occur at algorithmic speed. When combined, these capabilities can create a “self-tuning enterprise” that constantly learns and evolves according to its environment. (See Exhibit 3.)

To harness the power of ecosystems throughout and beyond the organization, leaders must:

  • Engage external partners to create a shared vision of the future.
  • Develop capabilities for collaboration and information sharing at scale—for example, platforms and APIs.
  • Redesign internal processes to be more adaptive and data-driven, allowing the organization to become “self-tuning.”

By Allison Bailey, Martin Reeves, Kevin Whitaker, and Rich Hutchinson

More: BCG https://www.bcg.com/

The BCG Henderson Institute is Boston Consulting Group’s strategy think tank, dedicated to exploring and developing valuable new insights from business, technology, and science by embracing the powerful technology of ideas. The Institute engages leaders in provocative discussion and experimentation to expand the boundaries of business theory and practice and to translate innovative ideas from within and beyond business. For more ideas and inspiration from the Institute, please visit Featured Insights.

 

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Seven key trends shaping maritime transport

UNCTAD’s Review of Maritime Transport 2018 identifies seven key trends that are currently redefining the maritime transport landscape and shaping the sector’s outlook. These trends, presented in no particular order, entail challenges and opportunities which require continued monitoring and assessment for effective and sound policymaking.

1) Protectionism
On the demand side, the uncertainty arising from wide-ranging geopolitical, economic, and trade policy risks as well as some structural shifts, constitutes a drag on maritime trade. An immediate concern are the inward-looking policies and rising protectionist sentiment that could undermine global economic growth, restrict flows and shift trade patterns.

2) Digitalization, e-commerce and the implementation of the Belt and Road Initiative
The unfolding effects of technological advances and China’s ambitious reordering of global trade infrastructure will entail important implications for shipping and maritime trade. The Belt and Road Initiative and growing e-commerce have the potential to boost seaborne trade volumes, while the digitalization of maritime transport will help the industry respond to the increased demand with enhanced efficiency.

3) Excessive new capacity
From the supply-side perspective, overly optimistic carriers competing for market share may order excessive new capacity, leading to worsened shipping market conditions. This, in turn, will upset the supply and demand balance and have repercussions on freight-rate levels and volatility, transport costs, as well as earnings.

4) Consolidation
Liner shipping consolidation through mergers and alliances has been on the rise over recent years in response to lower demand levels and oversupplied shipping capacity dominated by mega container vessels. The way this affects competition, and the potential for market power abuse by large shipping lines as well as the related impact on smaller players, remains a concern.

5) The relationship between ports and container shipping lines
Alliance restructuring, and larger vessel deployment is also redefining the relationship between ports and container shipping lines. Competition authorities and maritime transport regulators should also analyze the impact of market concentration and alliance deployment on the relationship between ports and carriers. Areas of interest span the selection of ports-of-call, the configuration of liner shipping networks, the distribution of costs and benefits between container shipping and ports, and approaches to container terminal concessions.

6) Scale
The value of shipping can no longer be determined by scale alone. The ability of the sector to leverage relevant technological advances is as increasingly important.

7) Climate change
Efforts to curb the carbon footprint and improve the environmental performance of international shipping remain high on the international agenda. The initial strategy adopted in April 2018 by the International Maritime Organization to reduce annual greenhouse gas emissions from ships by at least 50% by 2050, compared to 2008, is a particularly important development. On the issue of air pollution, the global limit of 0.5% on sulphur in fuel oil will come into effect on 1 January 2020. To ensure consistent implementation of the global cap on sulphur, it will be important for ship owners and operators to continue to consider and adopt various strategies, including installing scrubbers and switching to liquefied natural gas and other low-sulphur fuels.

Source: https://unctad.org; Photo: Marek Grzybowski

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Kedro, McKinsey’s first open-source software tool

 

QuantumBlack, the advanced analytics firm we acquired in 2015, has now launched Kedro, an open source tool created specifically for data scientists and engineers. It is a library of code that can be used to create data and machine-learning pipelines. For our non-developer readers, these are the building blocks of an analytics or machine-learning project. “Kedro can change the way data scientists and engineers work,” explains product manager Yetunde Dada, “making it easier to manage large workflows and ensuring a consistent quality of code throughout a project.”

McKinsey has never before created a publicly available, open source tool. “It represents a significant shift for the firm,” notes Jeremy Palmer, CEO of QuantumBlack, “as we continue to balance the value of our proprietary assets with opportunities to engage as part of the developer community, and accelerate as well as share our learning.”

The name Kedro, which derives from the Greek word meaning center or core, signifies that this open-source software provides crucial code for ‘productionizing’ advanced analytics projects. Kedro has two major benefits: it allows teams to collaborate more easily by structuring analytics code in a uniform way so that it flows seamlessly through all stages of a project. This can include consolidating data sources, cleaning data, creating features and feeding the data into machine-learning models for explanatory or predictive analytics.

More: www.mckinsey.com; https://github.com/quantumblacklabs/kedro

  What are the main features of Kedro?

1. Project template and coding standards

  • A standard and easy-to-use project template
  • Configuration for credentials, logging, data loading and Jupyter Notebooks / Lab
  • Test-driven development using pytest
  • Sphinx integration to produce well-documented code

2. Data abstraction and versioning

  • Separation of the compute layer from the data handling layer, including support for different data formats and storage options
  • Versioning for your data sets and machine learning models

3. Modularity and pipeline abstraction

  • Support for pure Python functions, nodes, to break large chunks of code into small independent sections
  • Automatic resolution of dependencies between nodes
  • (coming soon) Visualise your data pipeline with Kedro-Viz, a tool that shows the pipeline structure of Kedro projects

Note: Read our FAQs to learn how we differ from workflow managers like Airflow and Luigi.

4. Feature extensibility

  • A plugin system that injects commands into the Kedro command line interface (CLI)
  • List of officially supported plugins:
    • (coming soon) Kedro-Airflow, making it easy to prototype your data pipeline in Kedro before deploying to Airflow, a workflow scheduler
    • Kedro-Docker, a tool for packaging and shipping Kedro projects within containers
  • Kedro can be deployed locally, on-premise and cloud (AWS, Azure and GCP) servers, or clusters (EMR, Azure HDinsight, GCP and Databricks)

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Six governing considerations to modernize marketing

Most chief marketing officers (CMOs) understand that the utilization of data, analyses, and algorithms to personalize marketing drives value. Concept tests are becoming more efficient, customer approaches are being accelerated, and revenues are quadrupling in certain channels (Exhibit 1). All the evidence suggests that marketing functions should invest in, collect, and analyze available data to support their decision making.

We strive to provide individuals with disabilities equal access to our website. If you would like information about this content we will be happy to work with you. Please email us at: McKinsey_Website_Accessibility@mckinsey.com

No wonder, then, that one in three CMOs is driving a digitization initiative with high personal involvement, according to a McKinsey survey. Despite notable successes, digital marketing has often stalled in a trial phase for years in many companies. Why is that? We find that the managers responsible often blame it on culture and legacy behavioral patterns (Exhibit 2). These soft factors lie far ahead of technical issues, such as IT infrastructure and data availability, which is not surprising. It is easy enough to buy a new server for the customer database, and even new customer-relationship-manager software is quickly installed. But how does one change the attitudes and behaviors of those who use the technology?

We strive to provide individuals with disabilities equal access to our website. If you would like information about this content we will be happy to work with you. Please email us at: McKinsey_Website_Accessibility@mckinsey.com Based on our experience from a multitude of digital engagements, modernizing the marketing organization to unlock the full potential of the digital revolution requires business leaders to address six considerations.

1. How to centralize guidance and oversight

2. How to bring together marketing and IT (heart and brain)

3. How to build collaboration and agility

4. How to reinvent HR to meet talent demands

5. How to build flexibility into resource planning

6. How to make cultural change a continuous task

Modernizing marketing is a process that relies on multiple factors for success. Only by understanding what these are and by focusing on how to address them can marketers hope to get real value from digital. An earlier version of this article was published in the December 2018 issue of McKinsey’s German-language consumer journal Akzente.

About the authors: Patrick Guggenberger is a consultant in McKinsey’s Vienna office, Miriam Lobis is a partner in the Berlin office, and Patrick Simon and Kai Vollhardt are partners in the Munich office.

More: www.mckinsey.com/industries/