The document discusses challenges that companies face in scaling digital supply network (DSN) pilots to an enterprise level. It argues that the traditional "fail fast" approach may no longer be appropriate, as some DSN capabilities like predictive maintenance have matured and are proven to provide benefits. Instead, companies can take a more customized approach to piloting and scaling based on the maturity and strategic fit of the use case. Analyzing how other companies have successfully scaled certain DSN capabilities can help inform a targeted path for scaling one's own digital transformation.
Infochimps Survey: What IT Teams Want CIOs to Know About Big Data - Learn the top items that IT team members would like their CIOs to understand concerning their Big Data projects.
The report - CIOs & Big Data: What Your IT Team Wants You to Know - is based on a survey of more than 300 IT department employees, 58% of whom are currently engaged in Big Data projects, and aims to identify pitfalls that implementation teams encounter, and could avoid, if top management had a more complete view.
201308 Deloitte Tech Trends 2013 - Elements of Post Digital.pdf Francisco Calzado
This document provides a summary of the 2013 Technology Trends report from Deloitte. It identifies the main themes of the report as the "Elements of postdigital" which examines how the convergence of analytics, mobile, social, cloud and cyber technologies can help businesses achieve a "Postdigital Enterprise". The summary identifies the 10 trends covered in the report, which are split into 2 categories: "Disruptors" which can create positive disruption, and "Enablers" which are more evolutionary technologies. It provides a brief high-level description of each trend and notes that the report includes examples, perspectives and potential future directions for each trend.
"70% of surveyed executives said the pandemic is likely to accelerate the pace of their digital transformations"
The COVID-19 crisis seemingly provides a sudden glimpse into a future world.
- A world in which digital channels become the primary customer engagement model
- Automated processes become a primary driver of productivity
- Flexible, transparent and stable supply chains
In this unique moment, companies can learn and progress more quickly than ever before!
The Long Road of IT Systems Management Enters the Domain of AIOps-Fueled Auto...Dana Gardner
This document provides a summary of a podcast discussion between Dana Gardner and Doug de Werd about the evolution of IT management. Some key points:
- IT management has evolved over 30 years from managing heterogeneity to now managing complexity across hybrid cloud, multicloud, and SaaS environments.
- Automation is getting a boost from new ML and AI capabilities like AIOps, just as multicloud deployments increase demands.
- HPE OneView provides a core infrastructure management solution that is extending its capabilities through partnerships to integrate with DevOps tools and cloud platforms.
- Intelligence from tools like HPE InfoSight is providing more insights and enabling more autonomous computing models that can self-optimize
Beyond Decision Models – Using Technical and Business Standards to Transform ...Jonathan L'Ecuyer
Brian Stucky presented on using decision modeling and standards like DMN and MISMO to transform financial services. He discussed how DMN can be used to standardize decisions for exchange between organizations and in APIs/microservices. Regulations like HMDA were also discussed, with examples of how decisions could interpret rules and generate reports. Overall standards provide a path for digital transformation through improved transparency, explanation and collaboration across the industry.
A Data-driven Maturity Model for Modernized, Automated, and Transformed ITbalejandre
This document presents a research-based maturity model for measuring organizations' progress in IT transformation. The model segments organizations into four levels of maturity based on surveys of 1,000 IT executives about their infrastructure, processes, and relationships. Only a small percentage have achieved the highest levels of modernized infrastructure, automated processes, and business-IT alignment needed for digital transformation. Higher maturity is correlated with improved agility, efficiency, innovation funding, and business outcomes. Adopting modern data center technologies, automated processes, and DevOps practices can help organizations progress to more mature states.
Jon Cohn Exton PA - Rationalizing Application PortfoliosJon Cohn
The document discusses recommendations for rationalizing an organization's application portfolio. It provides 5 recommendations: 1) Industrialize and standardize applications to reduce redundancy, 2) Consider more radical strategies like enhancing or replacing legacy applications, 3) Leverage next-generation solutions to build business cases, 4) Embed innovation in the application lifecycle, and 5) Use metrics to create mutual understanding between IT and business on priorities. The recommendations aim to help CIOs address the growing complexity of application landscapes and free up resources for innovation.
The document provides an overview and summary of a report on how digital technology impacts and transforms the voluntary sector. The report was independently researched and sponsored by Tata Consultancy Services. It involved a literature review, workshop, mapping over 100 technology projects used by voluntary organizations, and 20 case studies. The report aims to show how technology can help address challenges, identify successful solutions, and barriers/enablers to implementation. It examines issues of adoption, disruption, and achieving technological maturity.
Infochimps Survey: What IT Teams Want CIOs to Know About Big Data - Learn the top items that IT team members would like their CIOs to understand concerning their Big Data projects.
The report - CIOs & Big Data: What Your IT Team Wants You to Know - is based on a survey of more than 300 IT department employees, 58% of whom are currently engaged in Big Data projects, and aims to identify pitfalls that implementation teams encounter, and could avoid, if top management had a more complete view.
201308 Deloitte Tech Trends 2013 - Elements of Post Digital.pdf Francisco Calzado
This document provides a summary of the 2013 Technology Trends report from Deloitte. It identifies the main themes of the report as the "Elements of postdigital" which examines how the convergence of analytics, mobile, social, cloud and cyber technologies can help businesses achieve a "Postdigital Enterprise". The summary identifies the 10 trends covered in the report, which are split into 2 categories: "Disruptors" which can create positive disruption, and "Enablers" which are more evolutionary technologies. It provides a brief high-level description of each trend and notes that the report includes examples, perspectives and potential future directions for each trend.
"70% of surveyed executives said the pandemic is likely to accelerate the pace of their digital transformations"
The COVID-19 crisis seemingly provides a sudden glimpse into a future world.
- A world in which digital channels become the primary customer engagement model
- Automated processes become a primary driver of productivity
- Flexible, transparent and stable supply chains
In this unique moment, companies can learn and progress more quickly than ever before!
The Long Road of IT Systems Management Enters the Domain of AIOps-Fueled Auto...Dana Gardner
This document provides a summary of a podcast discussion between Dana Gardner and Doug de Werd about the evolution of IT management. Some key points:
- IT management has evolved over 30 years from managing heterogeneity to now managing complexity across hybrid cloud, multicloud, and SaaS environments.
- Automation is getting a boost from new ML and AI capabilities like AIOps, just as multicloud deployments increase demands.
- HPE OneView provides a core infrastructure management solution that is extending its capabilities through partnerships to integrate with DevOps tools and cloud platforms.
- Intelligence from tools like HPE InfoSight is providing more insights and enabling more autonomous computing models that can self-optimize
Beyond Decision Models – Using Technical and Business Standards to Transform ...Jonathan L'Ecuyer
Brian Stucky presented on using decision modeling and standards like DMN and MISMO to transform financial services. He discussed how DMN can be used to standardize decisions for exchange between organizations and in APIs/microservices. Regulations like HMDA were also discussed, with examples of how decisions could interpret rules and generate reports. Overall standards provide a path for digital transformation through improved transparency, explanation and collaboration across the industry.
A Data-driven Maturity Model for Modernized, Automated, and Transformed ITbalejandre
This document presents a research-based maturity model for measuring organizations' progress in IT transformation. The model segments organizations into four levels of maturity based on surveys of 1,000 IT executives about their infrastructure, processes, and relationships. Only a small percentage have achieved the highest levels of modernized infrastructure, automated processes, and business-IT alignment needed for digital transformation. Higher maturity is correlated with improved agility, efficiency, innovation funding, and business outcomes. Adopting modern data center technologies, automated processes, and DevOps practices can help organizations progress to more mature states.
Jon Cohn Exton PA - Rationalizing Application PortfoliosJon Cohn
The document discusses recommendations for rationalizing an organization's application portfolio. It provides 5 recommendations: 1) Industrialize and standardize applications to reduce redundancy, 2) Consider more radical strategies like enhancing or replacing legacy applications, 3) Leverage next-generation solutions to build business cases, 4) Embed innovation in the application lifecycle, and 5) Use metrics to create mutual understanding between IT and business on priorities. The recommendations aim to help CIOs address the growing complexity of application landscapes and free up resources for innovation.
The document provides an overview and summary of a report on how digital technology impacts and transforms the voluntary sector. The report was independently researched and sponsored by Tata Consultancy Services. It involved a literature review, workshop, mapping over 100 technology projects used by voluntary organizations, and 20 case studies. The report aims to show how technology can help address challenges, identify successful solutions, and barriers/enablers to implementation. It examines issues of adoption, disruption, and achieving technological maturity.
Jon Cohn Exton PA - Next Gen Enterprise Information TechnologyJon Cohn
This document discusses how startups and microservices are disrupting traditional enterprise IT. Large companies are responding by becoming more nimble through spinoffs and separating legacy products from innovative areas. There is a growing trend toward modular, flexible IT that allows for agility, simplified scalability, and deployment flexibility. Startups focused on areas like Docker, MuleSoft, and AppDynamics that facilitate microservices are poised to capitalize on and further drive this disruption of monolithic vendors.
Jon Cohn Exton PA - Healthcare - Enterprise Architecture Jon Cohn
The document discusses the importance of enterprise architecture (EA) in healthcare transformation efforts. It notes that while EA has helped other industries build new operating models, it is still not widely used or understood in healthcare. The author argues that EA is necessary to orchestrate processes, improve data quality and analytics, control costs and risks, and better plan healthcare delivery transformation initiatives. Without a stronger emphasis on EA, healthcare may not be able to effectively transform to more cost-effective, data-driven models like Accountable Care Organizations.
Strengthening Operational Resilience in Financial Services by Migrating to Go...run_frictionless
Operational resilience is a key area of focus for financial services firms, and could be thought of as the next goal in addressing systemic risk in the financial services sector. Regulators are also increasingly focused on this risk: it is recognised that despite many years of bolstering financial stability by enhancing financial resilience following the financial crisis, the shocks that come from the operational side can be as significant as the shocks from the financial side.
https://runfrictionless.com/b2b-white-paper-service/
This document summarizes predictions from IT experts about trends in enterprise software in 2015. The key trends discussed include:
1. Hybrid cloud architectures becoming more mainstream as companies seek flexibility.
2. Subscription pricing models becoming more common for enterprise software.
3. Increased focus on mobile apps, especially for CRM, as employees spend less time in offices.
4. In-memory computing becoming an important differentiator for ERP vendors.
5. Deeper integration between ERP and other enterprise systems like procurement and HR.
6. Open source solutions continuing to gain ground for applications like data warehousing.
7. Business intelligence software prioritizing ease of use and data visualization.
Growth challenges and opportunities OEMs face in the digital ageInterfacing
Today, mission-critical markets like mining, oil & gas, and manufacturing are all on the verge of being disrupted by digital and IoT technologies. Incumbents are quickly realizing that in order to address the next wave of opportunity, they will have to shift from product-driven organizations to service-driven businesses. We need to work with customers in new ways by embracing the concept strange bedfellows, and developing a new set of software tools that are capable of handling the scale of information and devices that the IoT promises to deliver.
The document discusses the concept of digital transformation that businesses are currently undergoing. It notes that while consultants emphasize the disruptive nature of digital change, digital transformation is actually a process of compromise that involves optimizing business processes, products, people and assets through automation. The key is for businesses to start with a clear ambition of what they want to achieve through digital efforts and then work backwards to implement changes.
CIO Applications Magazine Names Bardess One of the Top 25 ML Solution Providerschrishems1
The investment Bardess has made in Tangent Works InstantML is critical to the industry to address what we see as a current weakness with all other AutoML technologies which is they rely on brute force (heavy compute effort) to select the best algorithm and hyper parameters, at the expense of rapid results.
Tidemark Enterprise Disruption In The Cloud Zd NetGregory Pence
A new startup called Tidemark offers a cloud-based system to help enterprises analyze and act on corporate data in a more flexible way than traditional on-premise software. Tidemark was designed specifically for the cloud model and focuses on business users rather than IT. This allows for easier implementation and use of the software to drive business decisions. While large vendors will eventually become strong cloud competitors, Tidemark has an opportunity to establish itself in the growing cloud market by rapidly innovating and showing the value of its cloud-based approach.
First Word: Unfurling the Blockchain BlueprintCognizant
Organizations worldwide see blockchain as a significant force for their business and industry, and are well on their way to developing a strategy, with an eye on reducing operating costs, automating key business processes and enhancing trust and transparency, our latest study reveals.
Business Cloud: The State of Play Shifts RapidlyCapgemini
The Cloud has become a set of real practical solutions for organizations today.
From 460 interviews with businesses and IT executives, we explored what the situation is with the users of the Cloud. We outline five key findings and provide recommendations to CIOs to create a successful roadmap to cloud adoption.
How More Industries Can Cultivate A Culture of Operational ResilienceDana Gardner
A transcript of a discussion on the many ways that businesses can reach a high level of assured business availability despite varied and persistent threats.
Where is enterprise architecture in healthcareJon Cohn
Integration, interoperability, data quality, process automation analytics -- these ideas are simple to describe but can be quite hard to execute in today's business environments. The road to building information-driven businesses far from well-paved.
The document discusses how cloud technology lends strength to data analytics. It notes that while cloud adoption faces challenges around security, privacy, and integration, cloud analytics are becoming mainstream. The cloud provides scale, elasticity and accessibility of data that supports analytics applications. Issues include complexity of data integration from diverse sources and managing risks of traditional long-term analytics projects. However, packaged cloud-based analytics applications accessed via SaaS can help businesses gain insights more quickly. While security remains a concern, cloud providers and in-house practices can help address issues.
This document discusses the risks of moving data and computing to cloud environments. It notes that there are two types of data - slow data that is less sensitive to latency, and quick data that needs to be processed urgently. Moving to the cloud can increase latency and variability in processing times, which poses risks for quick data processing. The document provides six problems that can result and recommends that CIOs work with cloud providers to understand these issues and ensure the right data is processed in the appropriate locations to balance costs, performance, and risks.
The document discusses how mobile testing has progressed through three phases similar to how web testing evolved:
1) Discovery - Early experimentation with simple mobile apps and minimal testing.
2) Support core business - Mobile now supports key business processes but testing is still limited and siloed.
3) Drive new business - Mobile experiences are now engaging, transactional, and integrated with social media. Testing is structured, methodological and uses strong tooling to address complex challenges like platform fragmentation, varied devices, and user experience.
Finding the right digital tools for your internal communication strategyStephan Schillerwein
This document summarizes a presentation on integrating digital tools into an organization's internal communication strategy. It discusses how work has evolved with digital technologies, presenting statistics on inefficiencies of current practices. The presentation advocates taking a holistic approach to understand user needs rather than starting with specific tools. Examples are given of successful digital transformations that began by understanding existing problems. A framework is provided for assessing an organization's maturity with internal digital tools. While many options exist, choosing tools aligned with core work processes is advised over simplification. Success is defined as focusing on real value for all stakeholders rather than new technologies alone.
What does the future hold for BI, CRM, ERP and enterprise software in 2015? IT experts discuss which enterprise software trends will have the greatest impact on IT organizations in the coming year
Delivering on Digital - The innovations and technologies that are transformin...Deloitte Australia
www2.deloitte.com/au/en/pages/public-sector/articles/delivering-on-digital.html
We now have the digital tools (cloud computing, mobile devices, analytics) and the talent to stage a real transformation in government. A digital mindset is a different way of thinking about customers, products, and process. It’s faster, iterative, and adaptable. And if government adopts it, the changes can be just as revolutionary. This book provides the handbook to make it happen.
This document reports on a pilot study that examined the quantifiable value derived from serendipitous activity within collaborative systems. Interviews with five companies found that four reported value from collaboration tools, though most did not track the actual value from serendipity. On average, measurable serendipitous results occurred within 3 months, yielding an average financial impact of $364,000. The study also outlines six rules for how value arises from serendipity through the creation, sharing, and later use of knowledge within organizations.
This document outlines a 9-step process for digital transformation consisting of 3 main stages: design, architecture, and security. The design stage involves developing an end-game business model, performing a gap analysis, and weighing mergers and acquisitions. The architecture stage defines the optimal IT architecture through a technology audit and building a dual-speed architecture. The security stage establishes a data security strategy, maintains security during transformation, and uses transformation as an opportunity to strengthen security culture. The overall message is that while transformation takes time, companies cannot delay and risk being disrupted by competitors, so they must mobilize resources and recruit leadership to start disrupting themselves.
The document outlines nine key steps that companies can take as part of a digital transformation journey to disrupt themselves before competitors do. The steps include: 1) designing an end-game disruptive business model, 2) analyzing gaps between the current and future models, 3) determining how to execute the transition, 4) architecting new technology, 5) auditing legacy systems, 6) building out a dual-speed IT architecture, 7) establishing a data security strategy, 8) maintaining security during transformation, and 9) using transformation as an opportunity to escalate security standards across the enterprise. Taking these steps can help traditional firms successfully transition to competing in the new digital landscape.
Jon Cohn Exton PA - Next Gen Enterprise Information TechnologyJon Cohn
This document discusses how startups and microservices are disrupting traditional enterprise IT. Large companies are responding by becoming more nimble through spinoffs and separating legacy products from innovative areas. There is a growing trend toward modular, flexible IT that allows for agility, simplified scalability, and deployment flexibility. Startups focused on areas like Docker, MuleSoft, and AppDynamics that facilitate microservices are poised to capitalize on and further drive this disruption of monolithic vendors.
Jon Cohn Exton PA - Healthcare - Enterprise Architecture Jon Cohn
The document discusses the importance of enterprise architecture (EA) in healthcare transformation efforts. It notes that while EA has helped other industries build new operating models, it is still not widely used or understood in healthcare. The author argues that EA is necessary to orchestrate processes, improve data quality and analytics, control costs and risks, and better plan healthcare delivery transformation initiatives. Without a stronger emphasis on EA, healthcare may not be able to effectively transform to more cost-effective, data-driven models like Accountable Care Organizations.
Strengthening Operational Resilience in Financial Services by Migrating to Go...run_frictionless
Operational resilience is a key area of focus for financial services firms, and could be thought of as the next goal in addressing systemic risk in the financial services sector. Regulators are also increasingly focused on this risk: it is recognised that despite many years of bolstering financial stability by enhancing financial resilience following the financial crisis, the shocks that come from the operational side can be as significant as the shocks from the financial side.
https://runfrictionless.com/b2b-white-paper-service/
This document summarizes predictions from IT experts about trends in enterprise software in 2015. The key trends discussed include:
1. Hybrid cloud architectures becoming more mainstream as companies seek flexibility.
2. Subscription pricing models becoming more common for enterprise software.
3. Increased focus on mobile apps, especially for CRM, as employees spend less time in offices.
4. In-memory computing becoming an important differentiator for ERP vendors.
5. Deeper integration between ERP and other enterprise systems like procurement and HR.
6. Open source solutions continuing to gain ground for applications like data warehousing.
7. Business intelligence software prioritizing ease of use and data visualization.
Growth challenges and opportunities OEMs face in the digital ageInterfacing
Today, mission-critical markets like mining, oil & gas, and manufacturing are all on the verge of being disrupted by digital and IoT technologies. Incumbents are quickly realizing that in order to address the next wave of opportunity, they will have to shift from product-driven organizations to service-driven businesses. We need to work with customers in new ways by embracing the concept strange bedfellows, and developing a new set of software tools that are capable of handling the scale of information and devices that the IoT promises to deliver.
The document discusses the concept of digital transformation that businesses are currently undergoing. It notes that while consultants emphasize the disruptive nature of digital change, digital transformation is actually a process of compromise that involves optimizing business processes, products, people and assets through automation. The key is for businesses to start with a clear ambition of what they want to achieve through digital efforts and then work backwards to implement changes.
CIO Applications Magazine Names Bardess One of the Top 25 ML Solution Providerschrishems1
The investment Bardess has made in Tangent Works InstantML is critical to the industry to address what we see as a current weakness with all other AutoML technologies which is they rely on brute force (heavy compute effort) to select the best algorithm and hyper parameters, at the expense of rapid results.
Tidemark Enterprise Disruption In The Cloud Zd NetGregory Pence
A new startup called Tidemark offers a cloud-based system to help enterprises analyze and act on corporate data in a more flexible way than traditional on-premise software. Tidemark was designed specifically for the cloud model and focuses on business users rather than IT. This allows for easier implementation and use of the software to drive business decisions. While large vendors will eventually become strong cloud competitors, Tidemark has an opportunity to establish itself in the growing cloud market by rapidly innovating and showing the value of its cloud-based approach.
First Word: Unfurling the Blockchain BlueprintCognizant
Organizations worldwide see blockchain as a significant force for their business and industry, and are well on their way to developing a strategy, with an eye on reducing operating costs, automating key business processes and enhancing trust and transparency, our latest study reveals.
Business Cloud: The State of Play Shifts RapidlyCapgemini
The Cloud has become a set of real practical solutions for organizations today.
From 460 interviews with businesses and IT executives, we explored what the situation is with the users of the Cloud. We outline five key findings and provide recommendations to CIOs to create a successful roadmap to cloud adoption.
How More Industries Can Cultivate A Culture of Operational ResilienceDana Gardner
A transcript of a discussion on the many ways that businesses can reach a high level of assured business availability despite varied and persistent threats.
Where is enterprise architecture in healthcareJon Cohn
Integration, interoperability, data quality, process automation analytics -- these ideas are simple to describe but can be quite hard to execute in today's business environments. The road to building information-driven businesses far from well-paved.
The document discusses how cloud technology lends strength to data analytics. It notes that while cloud adoption faces challenges around security, privacy, and integration, cloud analytics are becoming mainstream. The cloud provides scale, elasticity and accessibility of data that supports analytics applications. Issues include complexity of data integration from diverse sources and managing risks of traditional long-term analytics projects. However, packaged cloud-based analytics applications accessed via SaaS can help businesses gain insights more quickly. While security remains a concern, cloud providers and in-house practices can help address issues.
This document discusses the risks of moving data and computing to cloud environments. It notes that there are two types of data - slow data that is less sensitive to latency, and quick data that needs to be processed urgently. Moving to the cloud can increase latency and variability in processing times, which poses risks for quick data processing. The document provides six problems that can result and recommends that CIOs work with cloud providers to understand these issues and ensure the right data is processed in the appropriate locations to balance costs, performance, and risks.
The document discusses how mobile testing has progressed through three phases similar to how web testing evolved:
1) Discovery - Early experimentation with simple mobile apps and minimal testing.
2) Support core business - Mobile now supports key business processes but testing is still limited and siloed.
3) Drive new business - Mobile experiences are now engaging, transactional, and integrated with social media. Testing is structured, methodological and uses strong tooling to address complex challenges like platform fragmentation, varied devices, and user experience.
Finding the right digital tools for your internal communication strategyStephan Schillerwein
This document summarizes a presentation on integrating digital tools into an organization's internal communication strategy. It discusses how work has evolved with digital technologies, presenting statistics on inefficiencies of current practices. The presentation advocates taking a holistic approach to understand user needs rather than starting with specific tools. Examples are given of successful digital transformations that began by understanding existing problems. A framework is provided for assessing an organization's maturity with internal digital tools. While many options exist, choosing tools aligned with core work processes is advised over simplification. Success is defined as focusing on real value for all stakeholders rather than new technologies alone.
What does the future hold for BI, CRM, ERP and enterprise software in 2015? IT experts discuss which enterprise software trends will have the greatest impact on IT organizations in the coming year
Delivering on Digital - The innovations and technologies that are transformin...Deloitte Australia
www2.deloitte.com/au/en/pages/public-sector/articles/delivering-on-digital.html
We now have the digital tools (cloud computing, mobile devices, analytics) and the talent to stage a real transformation in government. A digital mindset is a different way of thinking about customers, products, and process. It’s faster, iterative, and adaptable. And if government adopts it, the changes can be just as revolutionary. This book provides the handbook to make it happen.
This document reports on a pilot study that examined the quantifiable value derived from serendipitous activity within collaborative systems. Interviews with five companies found that four reported value from collaboration tools, though most did not track the actual value from serendipity. On average, measurable serendipitous results occurred within 3 months, yielding an average financial impact of $364,000. The study also outlines six rules for how value arises from serendipity through the creation, sharing, and later use of knowledge within organizations.
This document outlines a 9-step process for digital transformation consisting of 3 main stages: design, architecture, and security. The design stage involves developing an end-game business model, performing a gap analysis, and weighing mergers and acquisitions. The architecture stage defines the optimal IT architecture through a technology audit and building a dual-speed architecture. The security stage establishes a data security strategy, maintains security during transformation, and uses transformation as an opportunity to strengthen security culture. The overall message is that while transformation takes time, companies cannot delay and risk being disrupted by competitors, so they must mobilize resources and recruit leadership to start disrupting themselves.
The document outlines nine key steps that companies can take as part of a digital transformation journey to disrupt themselves before competitors do. The steps include: 1) designing an end-game disruptive business model, 2) analyzing gaps between the current and future models, 3) determining how to execute the transition, 4) architecting new technology, 5) auditing legacy systems, 6) building out a dual-speed IT architecture, 7) establishing a data security strategy, 8) maintaining security during transformation, and 9) using transformation as an opportunity to escalate security standards across the enterprise. Taking these steps can help traditional firms successfully transition to competing in the new digital landscape.
Accelerating Time to Success for Your Big Data Initiatives☁Jake Weaver ☁
1. The document discusses the challenges of implementing big data initiatives, including sizing infrastructure, finding skilled professionals, and managing changing priorities over time.
2. It recommends partnering with a managed services provider to simplify big data implementation and gain expertise, flexibility, and time-to-market benefits.
3. The CenturyLink big data solutions suite includes managed Hadoop and analytics platforms to optimize data storage, integration, and analysis for customers.
Transformer in chief The new chief digital officerCliff Busse
The role of the chief digital officer (CDO) is changing dramatically and now demands new skills. The CDO must lead comprehensive digital transformations across the entire company rapidly. To succeed, the CDO must integrate digital into all aspects of business strategy, obsess over customers to drive change, and build agility through speed, data, and networks both internal and external. CDOs are judged by their ability to guide projects and deliver results.
McK - 'Transformer in chief'- The new chief digital officerSotiris Syrmakezis
The document discusses the changing role of the chief digital officer (CDO). The CDO role is no longer focused on basic digital capabilities, but is now responsible for comprehensive digital transformations across a company. Today's CDO must coordinate changes to how a company works and develop new business lines quickly. The document outlines five key skills needed for CDOs to drive successful digital transformations: 1) integrating digital into all aspects of business strategy; 2) obsessively focusing on customers; 3) building agility, speed and data usage; 4) extending networks outside the company; and 5) developing digital talent.
The new digital era and the promise of complete machine to machine transformation isn't a mystery, but does require mastery of some "easier said than done" IT CPMO practices. IT executives, consultants, and change agents will benefit from using these CPMO transformation strategies
Organizational Change Management: A Make or Break Capability for Digital SuccessCognizant
To realize the full benefits of digital transformation programs, businesses must manage the impact of digital change on their operational structure, culture and employees.
This document outlines a 9-step process for digital transformation comprised of 3 stages: start your own disruption, design, and architecture & security. The start stage involves cataloging existing digital initiatives, using cloud capabilities for experimentation, and learning from digital disruptors. The design stage consists of developing an end-game business model, performing a gap analysis, and weighing mergers & acquisitions. The architecture & security stage defines the optimal IT architecture, audits legacy technologies, builds out a dual-speed architecture, establishes a data security strategy, maintains security during transformation, and leverages transformation as a security opportunity.
Digital disruption is a top-of-mind issue in the C-suites of every industry. Senior executives of traditional firms are looking over their shoulders and wondering if they are in the crosshairs of a digital insurgent.
The document discusses the role of the CIO in leading organizations through the Postdigital era, where five forces of analytics, mobile, social, cloud, and cyber are converging and disrupting businesses. It argues that CIOs are uniquely positioned to catalyze change across organizations and help others understand how to leverage new digital opportunities. While CIOs have traditionally faced challenges balancing operational responsibilities with innovation, the current environment presents both risks and opportunities. Forward-thinking CIOs who rise to provoke disruption through these forces may take on expanded leadership roles, such as a Chief Digital Officer, and drive competitive advantage for their organizations.
A Portfolio Strategy To Execute Digital TransformationCapgemini
Senior Executives in pretty much all industries have now elevated digital transformation to the top of their strategic agenda. And they’re right to do so. The risk of falling behind the curve is so great that senior leaders are not debating whether digital technologies will affect their competitive position, but rather how to conduct an effective digital transformation and how fast it can be done.
However, an organization’s determination to get on the front foot with a bold digital strategy often falters when it comes up against the multi-dimensional complexity of the questions it faces and the risks it must manage. Should we prioritize short-term improvements at the expense of potentially larger strategic shifts? How fast will our industry be disrupted: months, years, or even decades? What level of risk are we willing to take on innovative new business models? Can we deliver our digital strategy in house or do we need to partner?
1) Organizations want to achieve business value from data-derived insights in four key ways: efficiency/cost reduction, growth of existing business streams, growth through new revenue streams from market disruption, and monetization of data itself through new business lines.
2) Most organizations are adopting an incremental approach to realizing this value, first proving value through use cases, then expanding to pilots in a line of business, and eventually achieving enterprise-wide adoption. This allows them to set a strategic direction while delivering value incrementally.
3) Current business intelligence technology like enterprise data warehouses are not meeting organizations' needs to democratize access to data and analytics. Decision-makers need the ability to rapidly create insights aligned with
Big & Fast Data: The Democratization of InformationCapgemini
Moving from the Enterprise Data Warehouse to the Business Data Lake
Is it possible that ubiquitous analytics represents the next phase of the information age? New business models are emerging, enabled by big data that business leaders are eager to adopt in order to gain advantage and mitigate disruption from start-ups and parallel industries. The winners are likely to be those that master a cultural shift as well as a technology evolution.
Our view is this will be realized through the alignment of a business-centric big data strategy, combined with democratization of the analytical tools, platforms and data lakes that will enable business stakeholders to create, industrialize and integrate insights into their business processes.
Innovative approaches are needed to free up data from silos whilst encouraging both the sharing and the continuous improvement of insights across the business. While it will be evolution for some, revolution for others; the risk of status quo is not just the loss of opportunity but also a widening gap between business and the internal technology functions.
https://www.capgemini.com/thought-leadership/big-fast-data-the-democratization-of-information
IW14 Keynote, Michelle Shuttleworth, Deloitte ConsultingSoftware AG
Presentation Title: Tech Trends 2014; Inspiring Disruption
Michelle Shuttleworth, Director, Deloitte Consulting
Innovation World 2014 conference, Oct 13-15, 2014, at the Hyatt Regency New Orleans, produced by Software AG. Three days of vision, inspiration and insight. Innovation World is THE global event for digital leaders who are driven to leverage the Software AG Suite: Alfabet, Apama, ARIS, webMethods, Software AG Live, Terracotta and Adabas-Natural.
1. The document discusses key aspects of digital transformation including focusing on speed, data, and ecosystems. It emphasizes the importance of building digital capabilities like customer experience, operations, and business models.
2. Transformation requires changes in information technology, strategy, and organizational agility. Companies should move along a continuum from pre-digital to digital pure play.
3. Accelerating transformation involves increasing speed through shorter feedback loops, leveraging large amounts of available data, and developing partnerships within ecosystems. Digital thread and twin approaches can also drive continuous improvement.
Hybrid Architecture - Is Cloud the Inevitable Best Practice?Christopher Reece
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1. FEATURE
Forget fail fast
How a customized and adaptive strategy can
drive your digital supply network
Richard Bradley and Meg Alderman
A DELOITTE SERIES ON INDUSTRY 4.0, DIGITAL MANUFACTURING ENTERPRISES, AND DIGITAL SUPPLY NETWORKS
2. 2
D
IGITAL SUPPLY NETWORKS (DSNs) offer a
profound vision: the transformation of linear
supply chains into dynamic, interconnected,
and open systems. By integrating information from
many sources and locations, businesses can attain a
holistic view of what’s happening, where and when.
In short, a DSN can transform the way organiza-
tions make, move, and manage goods and services.
To test such new capabilities, organizations often
begin with small pilots to prove the business case
for a large technological investment. Where they
find themselves challenged is scaling those pilots
to an enterprise level. To simply pilot is not enough,
and failing to scale successful programs leaves sig-
nificant opportunities untapped.
As they seek to scale DSN capabilities, most
business leaders don’t always have to navigate un-
charted territory these days. Many DSN applications
that were once considered bold leaps of faith have
evolved into standard capabilities. Leveraging the
proven business cases of others, who have success-
fully scaled these capabilities into enterprisewide
solutions, can help create a more informed, targeted
path for an organization’s own evolution. It can also
provide a roadmap to scaling that doesn’t have
to involve failure. On the other hand, identifying
which innovations have not been truly proven may
help leaders understand how—and when—to invoke
a more traditional approach to piloting, learning,
and scaling.
The following sections explore and analyze
these criteria that can help organizations scale
successfully:
• Understand why the mantra of “fail fast” may no
longer be appropriate in many cases;
• Understand potential DSN tactical archetypes;
• Build archetype-appropriate strategies for suc-
cess at scale; and
• Build a programmatic approach to DSN plans.
As companies begin to use digital supply networks, they face challenges in
scaling pilots to an enterprise level. Moving beyond the mantra of “fail fast” to
a more customized approach may be the solution.
A BRIEF LOOK AT THE DIGITAL SUPPLY NETWORK
In Deloitte’s first publication of this series, The rise of the digital supply network, we examined how
supply chains traditionally are linear in nature, with a discrete progression of design, plan, source,
make, and deliver. Today, however, many supply chains are transforming from a static sequence to
a dynamic, interconnected system—the digital supply network—that can more readily incorporate
ecosystem partners and evolve to a more optimal state over time. Digital supply networks integrate
information from many different sources and locations to drive the physical act of production
and distribution.1
In figure 1, the interconnected lattice of the new digital supply network model is visible, with digital
at the core. There is potential for interactions between each node and every other point of the
network, allowing for greater connectivity among areas that previously did not exist. In this model,
communications are multidirectional, creating connectivity among traditionally unconnected links in
the supply chain.
Continued
Forget fail fast
3. 3
A BRIEF LOOK AT THE DIGITAL SUPPLY NETWORK, CONT.
Source: Deloitte analysis.
Deloitte Insights | deloitte.com/insights
FIGURE 1
Shift from traditional supply chain to digital supply network
Sensor-driven replenishment
3D printing
Develop Plan Source Make Deliver Support
Quality sensing
Cognitive planning
Digital
core
Dynamic
fulfillment
Digital
development
Synchronized planning
Connected
customer
Smart
factory
Intelligent supply
Traditional supply chain
Digital supply networks
How a customized and adaptive strategy can drive your digital supply network
4. 4
When building DSN capabilities,
business leaders often start with
a lofty view of innovation, and
are encouraged to think big, start
small, and scale fast.
Brave new world: Finding
a path to DSN success
DSNs allow businesses to create new sources
of revenue by providing new and faster access to
markets. They can also support the production of
smart products, enable more informed decisions,
and allow systems to adapt and learn from the
world around them, driving more flexible processes.
What’s more, the interconnected, smart technolo-
gies that drive DSNs—a phenomenon known as
Industry 4.0—can lead to changes that could
positively affect the broader world, socially and
economically. But the path to successfully imple-
menting advanced technologies and scaling DSN
capabilities is often considered somewhat nuanced.
In a recent Deloitte global survey of more than
1,600 C-suite executives in 19 countries, only 18
percent said they felt their organizations were
highly prepared to address smart and autonomous
technologies; only 8 percent felt there was a strong
business case for new solutions.2
Some executives
pointed to a lack of internal alignment about which
strategies to follow, as well as inadequate collabora-
tion with external partners, “short-termism”, and
few plans for how to scale or where in their business
to do so. In other words, it seems that many orga-
nizations reportedly struggle not only to make the
business case for new solutions, but to scale them
at an industrial level. In another study, numerous
executives reported that their digital transforma-
tion initiatives were much more often driven by a
desire to improve current processes than a need to
seek innovation or new business models.3
When building DSN capabilities, business
leaders often start with a lofty view of innovation,
and are encouraged to think big, start small, and
scale fast.4
Thinking big means considering the to-
tality of what might be possible, immersing oneself
in innovation and understanding the ecosystem.
Starting small requires taking that big-picture
thinking and applying it to an initial pilot project,
starting with one or two transformations and giving
the innovators permission to fail as they learn the
ropes. Scaling fast is perhaps the most challenging,
and possibly the most critical aspect:
knowing when—and how—to scale
those small pilots up to the organiza-
tional level, prove success, and market
that success. Behind this view is the
idea of failing fast, which has long been
a mainstay of technology companies
and has pervaded the general business
consciousness with respect to building
new technology capabilities.5
Beyond “fail fast”: Learning
from others to move
beyond the mainstay
Many organizations pursue an intensive and
immersive experience with emerging Industry 4.0
technologies, then identify use cases to pilot and
expect them to fail fast. This can set the expectation
that a use case can—and should—require minimal
investment, with the project ended as soon as the
hypothesis has been disproved. Complementing
this process is the notion of “failing smarter”: failing
fast but learning from the experience as part of a
continuous improvement strategy.6
The individuals
testing new business models should have the
license to either gain market traction or fail smartly.
True innovation can often require such permission
to fail; otherwise, innovators may feel the risks of
trying something truly new are simply too high.
At a certain point, however, some use cases
evolve and should no longer be regarded as inno-
vation with an apt “fail fast, fail smart” mentality.
Forget fail fast
5. 5
Instead, they should be fundamental bases of opera-
tions. The rapid rise of 4.0 technologies may seem
dizzying, but some DSN use cases have matured;
what were once considered unproven innovations—
“killer apps” such as predictive maintenance and the
digital twin, among others—are now largely proven,
or even standard, capabilities for many. Failing to
build these tools can mean leaving opportunities
untapped.
Predictive maintenance, for example, can re-
liably increase uptime and availability in most
industries, leading to positive impacts within the
first year. Unplanned downtime can decrease and
maintenance costs shrink in a matter of months.
Reduced labor costs and higher-quality products
are additional benefits.7
In fact, there are very few
instances where predictive maintenance cannot
have a positive return on investment; organizations
can choose from many successful, real-world use
cases to learn and build a convincing business case
for investment, then develop a model for their own
efforts.8
In another example, blockchain and aug-
mented or virtual reality (AR/VR), among other
technologies,9
are being deployed within supply
chains, in various ways and at various levels10
(see
sidebar, “Technology vs. use case”). Their potential
benefits have been well documented in many
business models and industries—particularly
manufacturing, industrial products, and oil and
gas exploration. Examining how others have scaled
these types of capabilities into enterprisewide
solutions can help inform a targeted path for your
business’s own evolution.
Selecting the right strategy:
A new model for scaling fast
A
S FORMERLY CUTTING-EDGE capabilities
could become increasingly proven and
fundamental to DSNs, decision-makers can
replace the fail-fast model with more customized
piloting and scaling, based on two key aspects: the
maturity of the use case and the position of
the use case within the organization’s business
model. This new approach allows organizations to
build a constellation of pilots that can deliver rapid
business impact. By changing the lens used to view
what your business is piloting, you can put the
focus on configuring approach and capabilities to fit
strategy and priorities.11
MATURITY OF THE USE CASE
Maturity can be assessed in distinct ways.
One way is the extent to which a use case has
been deployed industrially and in a production
environment—has the capability been used in
the real world? A second is the maturity of the
quantitative benefits from such a deployment—have
we seen measurable results over a long enough
period to assess its impact?
In general, a use case can be considered mature
if it:
1. Hasbeendeployedindustriallyandatproduction
scale;
2. Has delivered a positive net return on investment
to date; and
3. Is expected to continue that return on investment
at scale.
There is one belief that use cases need to
have been deployed in a comparable industry for
organizations to assess their maturity and apply the
results for their own use. However, recent research
has shown that you need not limit your organization
to analogous peers; looking across industries for
use cases is positively linked to more successful
innovation.12
For mature use cases, outcomes and
returns on investment can be industry agnostic.
True innovation can often
require permission to fail;
otherwise, innovators
may feel the risks of
trying something truly
new are simply too high.
How a customized and adaptive strategy can drive your digital supply network
6. 6
TECHNOLOGY VS. USE CASE
In the past 18 months, blockchain has been widely discussed and seems to have sparked significant
enthusiasm as an important technology. It has not been uncommon for many organizations
to gravitate to blockchain’s market potential and embrace pilots that feature the use case as a
secondary consideration to the technology itself. The challenge to adjust these priorities appears
all too real: In the rush to build a proof-of-concept for an exciting new technology, the original
challenge—and the use case or capability that may help solve it—can be quickly obfuscated by the
technology itself. The effects are also significant; applying immature technology to a mature use case
could be detrimental, with technology that could be applied to other, critical use cases disregarded
as unable to live up to its promise.16
The problem could lie in confusing the steps at the pilot planning stage. It is rarely good practice
to pick a technology, and then pick a use case. Instead, consider starting by absorbing a deep
understanding of the issue you are trying to solve or the opportunity you are trying to harness,
which is of primary importance; the choice of technologies that can enable solutions would follow. In
other words, before implementing a new, specific technology, take a step back and think strategically
about what is needed and why.
Even if you encounter a lack of deployment in
comparable industries, you can build a compelling
business case for piloting and scaling DSN
capabilities.
POSITION OF THE USE CASE:
CORE VS. PERIPHERY
Assessing the position of a use case means
evaluating where it sits within the business model.
The core of an organization is where resources
are presently deployed: typically, the primary
capabilities and main revenue streams. The
periphery represents the peripheral areas of the
organization, where new business models with high
growth potential are explored.13
More simply put,
the core of a business is typically focused on internal
productivity, whereas the periphery is generally
focused on driving revenue from externally focused
activities.
Building the right approach:
Assessing both aspects
to scale smartly
Combining a use case’s maturity and position
can determine the approach to scaling DSN capabil-
ities for your business. It’s an approach that mimics
the adoption curve for Industry 4.0 in general:14
As
organizations begin to scale and monetize use cases,
they typically focus first on improving operations
and productivity—the same things they have always
focused on, but aiming for better and faster results.
Only after they have mastered those relatively
lower-hanging fruit, and built a strong foundation
of proven DSN use cases, do most businesses tend to
progress to capabilities that generate new streams
of revenue and lead to new business models.15
To build a strong DSN foundation on top of
proven use cases, decision makers can use four
archetypes (figure 2) to help identify the business’s
current position, and gauge the capabilities and jus-
tification needed to pilot and scale more-innovative
use cases.
Forget fail fast
7. 7
Archetype A Archetype B Archetype C Archetype D
Typical P&L
impact
Operations and
productivity
Operations,
productivity, and
revenue
Revenue Revenue and new
business models
Typical
examples
• Predictive
maintenance
• Demand-sensing
• Control towers
• Quality-sensing and
prediction
• Autonomous
trucks and delivery
optimization
• Automated sales
forecasting and
replenishment
• AR/VR
• Dynamic load-
balancing and
replenishment
• Model-based design
and manufacturing
• Data as product or
service
• Ultra-delayed
differentiation
• Predictive after-market
maintenance and
service
• Make-to-use with 3D
printing
• Direct-to-consumer
drone delivery
• AR-enabled customer
support
Internal
sponsor/s
Supply chain and
operations
Supply chain and
operations
CEO and
operations
CEO and
operations
Key criteria
for success
at scale
• Focus on vendor
selection and technical
maturity
• Clear statement of
intended value of use
cases
• Trusted integrator
partner with clear
experience
• Clear focus on
skills and talent
development
• Focus on the use case,
not the technology
• Strong links to
innovation
• Forward-thinking
supply chain and
operations leaders
• Early market traction
in new business model
• Supply-chain
organization with
influence
• Clear C-suite
sponsorship
• Focus on skills and
talent development,
especially cross-
functional skills
transfer
• Ability to fail fast
with embedded
culture of continuous
improvement
• Clear C-suite
sponsorship
• Strong internal
stakeholder alignment
and links to startup
environment
Source: Deloitte analysis.
FIGURE 2
Tactical archetypes of DSN use cases, categorized by maturity and position
Mature
Emerging
Core Periphery
Archetype A
Money on the table
Archetype B
Future-proofing
operations
Archetype C
To the office of
the CEO
Archetype D
Fail fast,
fail smart
How a customized and adaptive strategy can drive your digital supply network
8. 8
ARCHETYPE A: MONEY ON
THE TABLE
Archetype A denotes mature, proven use cases
at the core of a business: the low-hanging fruit
of an organization’s DSN. Because the use cases
often focus on improving current processes, they
typically have an almost immediate impact on an
organization’s bottom line—specifically, a reduction
in the cost of goods sold through manufacturing or
supply-chain efficiencies.
A broad range of use cases can fall into this group,
including predictive maintenance, demand sensing,
control towers, and quality sensing and prediction,
all of which have already shown business benefits
at scale across industries.17
Many automotive com-
panies, for example,
are using tools such
as predictive quality
analytics at scale to
identify warranty
issues before they
turn into major
recalls; predictive
maintenance use
cases can also be
considered compel-
ling and are used
by many organi-
zations to reduce
downtime and lost
production.18
Given the multitude of proven initiatives, use
cases in this category can be considered “money
on the table,” and should be a core component of
a DSN strategy. By studying the successes of others
who have built and scaled these capabilities, orga-
nizations can be well-positioned to achieve success
without the need to first “fail fast.” To scale effec-
tively, select vendors with proven technical maturity
and partners with clear, documented experience;
align leaders on a clear direction and statement of
intent about why they are pursuing a use case; and
develop the skills and talent to implement and use
the capabilities successfully.
ARCHETYPE B: FUTURE-
PROOFING OPERATIONS
Archetype B consists of use cases being devel-
oped or driven by parallel advances in technology,
and affecting the core of a business, specifically
operations. This can mean a blurred line between
technology and use case, particularly as organiza-
tions may still be trying to decide the most relevant
areas for a technology. In this way, use cases of
this type are somewhat less proven, but still driven
by increasingly accepted, and even standard,
technologies.
Artificial intelligence (AI) is an example of a tech-
nology that is fostering DSN use cases, particularly
in planning. Statistical forecasting has long been
a mature use case,
typically enabled by
a range of supply-
chain planning
technologies, but de-
velopments in AI and
cognitive computing
have begun to drive
more predictive and
intelligent planning
capabilities.19
Such
technology is also
being used to power
driverless trucks in
warehousing and lo-
gistics strategies.
Although the use case of intelligent, automated
sales forecasting and replenishment is not yet fully
mature, it offers a potentially significant opportu-
nity for scaling because it drives the core business of
an organization. This could be particularly relevant
as more organizations connect their systems to
develop DSNs, thereby harnessing a broad scale and
scope of data over time and geography, to enable in-
telligent forecasting. In addition, some mature use
cases in this category do make convincing business
cases for such scaling; for example, one organization
realized a 40 percent improvement in the quality of
its sales forecasts across 600,000 SKUs.20
As organizations begin to
scale and monetize use cases,
they typically focus first on
improving operations and
productivity—the same things
they have always focused
on, but aiming for better and
faster results.
Forget fail fast
9. 9
AR/VR is another example. NASA has been
researching virtual reality since the mid-1990s, to
train astronauts in spacewalking, but you don’t have
to be part of NASA to realize the potential business
benefits.21
Proven AR/VR use cases are prolifer-
ating: for quality control and to help workers spot
defects;22
in logistics, inventory management, and
order picking;23
for maintenance; in training;24
and
for end-to-end visibility throughout the DSN.25
Airbus used AR/VR to reduce the inspection time
for 60,000 to 80,000 brackets in the fuselage of
one of its jetliners, to three days from three weeks.26
Within the automotive industry, some major brands
are using virtual reality to assess prototype builds,
and applying it in sales: Customers can now try out
different features of cars before buying.27
For organizations that seek to scale use cases
to “future-proof” core DSN operations, consider fo-
cusing on the use case, not the technology, to ensure
you are steering toward applications that can propel
the business forward. Then begin to move toward
an incubator-like approach, as seen in the fail-fast
model, emphasizing innovative, startup-driven uses
of technology that can power capabilities that are
core to the business.
ARCHETYPE C: TO THE
OFFICE OF THE CEO
Archetype C focuses on applying tested, proven
use cases to new business models: pursuing new
forms of revenue at the periphery of the organi-
zation, rather than streamlining operations and
productivity at the core. In this archetype, the focus
on new revenue models elevates the DSN beyond
the supply chain to decisions made by the chief
operating officer (COO) and the CEO. Success in
this archetype requires significant collaboration
across an enterprise, whereby the chief supply chain
officer brings operations expertise to support the
company’s growth into new product segments and
markets. In this way, decision-making and business
processes could change, and an organization could
move closer to the connectivity and end-to-end vis-
ibility, which are the hallmarks of a fully realized
DSN.
Given the focus on revenue growth, use cases in
this category tend to focus on the customer, whether
via new products and services or aftermarket
support. Two examples are data from connected
systems offered as a product/service unto itself, and
ultra-delayed differentiation.28
To succeed at scale with use cases in this cat-
egory, consider the importance of the first-mover
advantage. Gain early market traction in new busi-
ness models, empower the supply chain to engage
in technology investment discussions,29
ensure
C-suite buy-in for scaling new initiatives, and focus
on developing skills and talent to manage new ca-
pabilities—specifically, cross-functional skills, given
the broader need for collaboration in this archetype.
ARCHETYPE D: FAIL FAST, FAIL SMART
Archetype D brings emerging use cases together
with peripheral business models, representing typi-
cally the riskiest, most innovative components of a
DSN. In these cases, a return to the “fail fast, fail
smart” mentality is recommended, using small-scale
pilots to build a business case. In 2017, Iceland’s
leading e-commerce platform, AHA, partnered with
Israeli company Flytrex to pilot direct-to-consumer
drone delivery.30
Although the use case for drones
is relatively mature, its expansion to direct-to-con-
sumer deliveries is far from proven, not least due to
the evolving nature of regulations. Another example
in this category can be found in make-to-use appli-
cations of additive manufacturing (AM), extending
beyond proven AM use cases of prototyping and
specific production runs. AR-enabled customer
support also qualifies as an Archetype D use case.
Given that use cases in this category are often
completely new, and rely on relatively unproven
applications of new technologies, both the CEO and
COO will likely be involved in decisions to invest.
To nurture success, foster a culture of continuous
improvement, allow teams to fail and learn, and en-
courage a startup mindset and environment. Clear
C-suite sponsorship can be critical here, as well as
strong stakeholder alignment on the goals for each
endeavor.
How a customized and adaptive strategy can drive your digital supply network
10. 10
Select a winning constellation
to scale successfully
It is important to approach each initiative indi-
vidually,gaugingitsrespectivematurityandposition
within the organization. However, to deliver a truly
connected, “always on” DSN frequently requires
multiple initiatives. In other words, businesses may
have use cases in multiple archetype categories si-
multaneously, based on their needs, risk appetites,
and internal and external factors. Each typically
needs a comprehensive and connected strategy for
scaling. Consider taking advantage of the following
methods to help build a balanced portfolio of scal-
able results:
1. Use the DSN strategic choice cascade, as de-
scribed in Deloitte’s The rise of the digital
supply network,31
to decide where to differ-
entiate: speed, agility, service, cost, quality,
or innovation.
2. Identify relevant areas of focus for supply-
chain transformation and configuration that
could support the selected differentiation areas.
Consider the archetype model to segregate and
prioritize use cases to develop accordingly, for
maximum success.
3. Build a balanced portfolio of DSN tactics that
support those transformation/configuration
areas. These tactics would depend on many
factors, both internal (e.g., current performance
metrics and risk appetite) and external (e.g.,
competitive pressure and the current regulatory
landscape).
An organization in an industry characterized by
wafer-thin margins may choose to adopt a tactic
portfolio predominantly comprising Archetype
A use cases, with a few resources dedicated to
Archetype B. Similarly, an organization whose
supply chain is not a core part of the business may
wholly focus on Archetype A. A business with strong
efficiencies, substantial existing investments in its
DSN, and supply chain and operations representa-
tion at the C-suite level may focus on Archetype C,
taking its proven use cases internally and devel-
oping new business models. Archetype D adoption
Forget fail fast
11. 11
may be best suited to innovative organizations that
are open to embracing risk. Whatever the specific
needs of your business’s stakeholders, consider
tailoring your constellation of initiatives to them,
laying the foundation for scalable results.
Conclusion
In the aforementioned Deloitte global survey of
C-suite executives, 87 percent said they believed
Industry 4.0 will lead to more social and economic
equality and stability. They regard technology as
an equalizer, providing more access to education,
jobs, and financing across geographies and social
groups.32
DSNs can provide an effective way to
harness these technologies to bring about more con-
nected organizations.
Organizations seeking to build DSNs should
adopt a customized strategy that builds upon the
lessons of others’ successes—not just in analogous
industries but further afield. Rather than using
a blanket approach to starting small and cycling
through failures, tailor DSN tactics by archetype,
based on the maturity of the use case and its posi-
tion within the organization’s business model. This
focus on archetypes means that the right lens is
trained on success from the start, by matching
strategies to specific needs. First and foremost, you
should become intimately familiar with the issue
you are trying to solve, or the opportunity you are
trying to harness; from there you can choose from
the myriad of technologies that can bring about real
and lasting improvements.
How a customized and adaptive strategy can drive your digital supply network
12. 12
1. Mark Cotteleer and Brenna Sniderman, Forces of change: Industry 4.0, Deloitte Insights, December 18, 2017.
2. Punit Renjen, “Industry 4.0: Are you ready?,” Deloitte Review 22, January 22, 2018.
3. Deloitte Insights, The Industry 4.0 paradox, October 10, 2018.
4. Adam Mussomeli, Doug Gish, and Stephen Laaper, The rise of the digital supply network, Deloitte University Press,
December 1, 2016.
5. James B. Stewart, “A fearless culture fuels U.S. tech giants,” New York Times, June 18, 2015.
6. Rita Gunther McGrath, “Failing by design,” Harvard Business Review, April 2011.
7. Ian Richter, “Quantifying the benefits of predictive maintenance: 8 benefits,” Seebo IoT Academy, May 3, 2018.
8. Xiaoning Jin et al., “The present status and future growth of maintenance in US manufacturing: results from a
pilot survey,” Manufacturing Rev 3, no. 10 (2016): DOI: 10.1051/mfreview/2016005.
9. We make the distinction between technology and use case. Predictive maintenance is a DSN use case, which
could be enabled by a variety of technologies. Blockchain and augmented/virtual reality are technologies, which
may underlie a variety of use cases.
10. Deloitte, Using blockchain to drive supply chain transparency, 2017; Abe Eshkenazi, “Using augmented reality to
improve supply chains,” APICS Supply Chain Management Now, December 16, 2016.
11. Mussomeli, Gish, and Laaper, The rise of the digital supply network.
12. Ellen Enkel and Oliver Gassmann, “Creative imitation: Exploring the case of cross-industry innovation,” R&D Man-
agement, 40, no. 3 (2010): pp. 256–70 (special issue: “The future of open innovation,” edited by Ellen Enkel, Oliver
Gassmann, and Henry Chesbrough, June 2010).
13. Deloitte has examined in depth the notion of scaling at the edges. For further reading, see Deloitte Center for the
Edge, Scaling edges: a pragmatic pathway to broad internal change, 2012.
14. Eli Tidhar, Jeremy Siegman, and Dan Paikowsky, Toward the next horizon of Industry 4.0: Building capabilities
through collaborations and startups, Deloitte Insights, August 1, 2018.
15. Deloitte Insights, The Industry 4.0 paradox, October 10, 2018.
16. John Lucker, Susan Hogan, and Brenna Sniderman, Fooled by the hype: Is it the next big thing or merely a shiny new
object?, Deloitte Insights, July 30, 2018.
17. For specific examples of the multitude of proven outcomes from predictive maintenance, demand sensing, and
AR/VR, see: Chris Coleman et al., Making maintenance smarter, Deloitte University Press, May 9, 2017; Tim Gaus,
Ken Olsen, and Mike Deloso, Synchronizing the digital supply network, Deloitte Insights, May 22, 2018; and Allan
V. Cook et al., Digital reality: The focus shifts from technology to opportunity, Deloitte Insights, December 5, 2017.
18. Chris Coleman et al., Making maintenance smarter.
19. Gaus, Olsen, and Deloso, Synchronizing the digital supply network.
20. Sean Monahan and Michael Hu, “A.I. and the path to breakthrough supply chain planning,” Supply Chain Manage-
ment Review, January 25, 2018.
21. Erin Carson, “How NASA uses virtual reality to train astronauts,” TechRepublic, May 19, 2015.
Endnotes
Forget fail fast
13. 22. Daniel Segovia et al., “Augmented reality as a tool for production and quality monitoring,” Procedia Computer
Science, 75 (2015): pp. 291–300, 2015 International Conference on Virtual and Augmented Reality in Education.
23. Jay Samit, “5 ways augmented reality is disrupting the supply chain,” Fortune, March 1, 2018.
24. Tony DeMarinis, Lynne Calligaro, Cary Harr, Joe Mariani, Real learning in a virtual world: How VR can improve learn-
ing and training outcomes, Deloitte Insights, August 14, 2018.
25. Samit, “5 ways augmented reality is disrupting the supply chain.”
26. Ian Wright, “What can augmented reality do for manufacturing?,” engineering.com, May 11, 2017.
27. Christian Titze, Samantha Searle, and C. Dwight Klappich, “The 2018 top 8 supply chain technology trends you
can’t ignore,” Gartner, January 9, 2018.
28. For further information about opportunities in these areas, see: Mark Cotteleer and Brenna Sniderman, Forces
of change: Industry 4.0; John van Wyk, Peter Brooke, and Justine Bornstein, Selling Industry 4.0, Deloitte Insights,
March 29, 2018; and Gianmarco Monsellato et al., Tax governance in the world of Industry 4.0, Deloitte Insights,
August 27, 2018.
29. Deloitte Insights, The Industry 4.0 paradox, October 10, 2018.
30. Scott Bretton, “AHA and Flytrex team up to offer drone delivery in Iceland,” Ecommerce Guide, August 29, 2017.
31. For more information about the strategic choice cascade, see Mussomeli, Gish, and Laaper, The rise of the digital
supply network.
32. Punit Renjen, “Industry 4.0: Are you ready?.”
14. 14
RICHARD BRADLEY is a director at Deloitte Consulting AG and leads Deloitte’s Digital Supply Network
(DSN), as well as the Strategy and Smart Operations team in Switzerland. With over 15 years of experi-
ence in digitally enabled supply chain transformation and as a thought leader and speaker on digital
supply chain topics, he is passionate about bringing disruptive topics to business, breaking down
barriers to adoption, and harnessing business impact across all areas of the value chain. He is on
LinkedIn at https://www.linkedin.com/in/rbradley2017/.
MEG ALDERMAN is a senior consultant with Deloitte Switzerland’s Digital Supply Network (DSN), as
well as the Strategy and Smart Operations team. She joined the Swiss practice from the United States
after working extensively on topics as diverse as supply chain, strategic risk, and enterprise risk. Her
unique experiences have driven a passion for problem-solving in the digital age and managing risk
driven by disruption, innovation, and change. She is on LinkedIn at https://www.linkedin.com/in/
meg-alderman-cscp-csm-348a3025/.
The authors would like to thank Georg Ziegner and Alex Nedelchev of Deloitte Consulting AG; Adam
Mussomeli, Stephen Laaper, and Aaron Parrott of Deloitte Consulting LLP; and Brenna Sniderman
of Deloitte Services LP.
About the authors
Acknowledgments
Forget fail fast
15. 15
Contacts
Adam Mussomeli
Digital Supply Networks leader
Principal
Deloitte Consulting LLP
+1 203 905 2646
amussomeli@deloitte.com
Stephen Laaper
Digital Supply Networks leader
Principal
Deloitte Consulting LLP
+1 312 513 7900
slaaper@deloitte.com
Richard Bradley
Director
Deloitte Consulting AG
D: +41 58 279 8712 | M: +41 795 707 556
ribradley@deloitte.comh
Deloitte’s Center for Integrated Research focuses on developing fresh perspectives on critical business
issues that cut across industry and function, from the rapid change of emerging technologies to the
consistent factor of human behavior. We uncover deep insights and look at transformative topics in new
ways, delivering new thinking in a variety of formats such as research articles, short videos, or in-person
workshops.
About the Center for Integrated Research
Deloitte Consulting LLP’s Supply Chain and Manufacturing Operations practice helps companies
understand and address opportunities to apply Industry 4.0 technologies in pursuit of their busi-
ness objectives. Our insights into additive manufacturing, the Internet of Things, and analytics
enable us to help organizations reassess their people, processes, and technologies in light of
advanced manufacturing practices that are evolving every day.
How a customized and adaptive strategy can drive your digital supply network