The massive digital impact on the industrial companies, economy and job market

We are already moving from an Outcome-based Economy (connected ecosystems, platform enabled market place, pay-per-outcome) to an Autonomous Pull Economy (continuous demand sensing, end-to-end automation, resource optimization, waste minimization and workforce transformation).

Key digital enablers

Digital transforms physical industries through connected products, systems, processes, people and artificial intelligence:

-          Cloud with its inexpensive & abundant storage enables aggregation of data streams from a large variety of sources.

-          Advanced Analytics offers descriptive and increasingly prescriptive decision support leveraging algorithms, automation, deep domain expertise such in material science, electrical engineering, etc.

-          Real-time Analytics enables real-time response to cyber physical systems.

-          Ubiquitous connectivity extends to physical products, infrastructure and all types of things.

-          Machines, devices, facilities, fleets and networks connect through intelligent sensors, software applications and controls.

-          New technical data standards and technical architecture bring together the different players across the ecosystem.

-          Data-driven decision making (DDD) – research indicates that companies, which use it considerably more competitive and profitable

The opportunities to aggregate and optimize

The learning experience of each machine can be aggregated into a single information system that accelerates learning across the entire machine portfolio and even entire network of the overall organization. Learning exponentially increases.

An aggregate view across machines, components, sub components and even materials enables optimal products, parts and other inputs delivered, at the right time to the right location, in the most efficient way.

With big data and new data compression techniques plant managers can track massive data streams of all devices continually and correlate diverse data from different devices & source to generate valuable insights for improvements formerly impossible. New visualization abilities, growing knowledge banks, etc. further improve decision making.

The cloud allows to overcome traditional information & data silos within organizations. It allows enables to bridge former boundaries among organizations, industries and locations.

Real-time diagnostics and predictive analytics will reduce maintenance costs and prevent machine breakdowns before they occur, avoid capital damage, revenue loss and accidents. Engineers can question systems on irregularities and receive intelligent response within seconds. Fleet and logistics will be optimized in real-time, improving the entire supply chain.

Everybody speaks about the Consumers, but the impact on the industrial companies is at least as big!

Industrial Internet advances will enable enhanced asset reliability by optimizing inspection, maintenance and repair processes. It will improve operational efficiencies across all operations down to the very device level.

GE has done a tremendous work on analyzing the impact of digital to the industrial sector. They estimate the potential benefit of digital (they call it Industrial internet) to the global economy worth $80 trillion by 2025, approximately one half of the entire global economy!

Just a one percent productivity increase in the commercial aviation industry, for example, would translate to $30 billion over 15 years. And this only counts fuel cost savings!

A one percent productivity gain in the global gas-fired power plant fleet could yield $66 billion savings in fuel consumption. Health care savings could amount $63 billion. World rail networks $27 billion. Similar savings and productivity gains apply to all industries…

Manufacturing becomes important again

Research proves that Services becomes the major GDP contributor and driver in developing countries. Among the leading nations it is between 72 percent (Japan) and 80 percent (USA).

However, manufacturing is important to sustain wages and living standard of the overall population. Industrial nations need to revisit this issue and re-build their manufacturing sector.

The Industrial Internet allows again to effectively compete with developing nations’ low manual costs.

Important success drivers

Security

Robust cyber security is essential to manage vulnerabilities and protect sensitive information and intellectual property as well as personally identifiable information (PII). It must cover the devices, networks and the cloud with vulnerability lifecycle management, end-to-end protection, intrusion detections/ prevention systems, firewalls, logging and network visibility, and sufficient security training for engineers, management and users.

Data needs to be encrypted on the devices as well as in the transmission of data.

Every player in the ecosystem has a role to play: Technical vendors (product design, supply chain, embedded security features), Asset Owners/ Operators (secure facilities and networks, cooperate with regulators and law enforcement), Regulators & Policymakers, Academics (train specialized people such as digital-mechanical engineers, data scientists, etc.)

Data Centers

The data is increasingly exponentially. From 2012 to 2025 the data will multiply by perhaps 40 times! The majority of data centers to process it in 2025 have yet to be built.

Job losses and new roles

Digitalization will bring an unprecedented change in the global job market. Many traditional professions and positions will be taken over by programs, robots and other emerging technologies. Automation will eliminate many people in the low skill levels. But also very educated people will be affected. (Read my blog on IBM Watson and the health care industry).

With the elimination of jobs new roles are emerging: Next generation engineers (blend traditional engineering skills with informatics & computing to serve as digital engineers), data scientists (who can blend statistics, data engineering, pattern recognition, advanced computing, uncertainty modelling, visualization) and user interface experts (industrial design of human-machine interaction, operation through gesturing and facial recognition, etc.)

The difference between Industrial revolution and the Industrial Internet

The instrumented industrial machine systems have connected with the physical & human networks and entered into a continuous cycle of communication, exchange and mutual learning.

While the industrial revolution focused on resources and physical objects, the Industrial Internet focuses on innovation, knowledge, software and intelligent systems & devices.

Network, fleet, asset and facility optimization happens through intelligent devices, systems and decision making.

Potential Problems & Challenges

The Industrial Internet promises us operational improvements across all industries worldwide. Based on the benefits of the Internet we can extrapolate the positive outcomes of the Industrial Internet:

-          Cost-deflation (similar as when companies adopted ICT equipment)

-          Labor productivity growth (1996 -2004 it generated 3.1 percent on average)

-          Average GDP growth could be 25 to 40 increase (based on a productivity increase of 1 percent)

However, all calculations are based on the assumption that labor and capital would accumulate at the same pace. That is not realistic, given that perhaps 20 to 25 percent of jobs may become obsolete by 2025. And it is simply not sensible to believe that shop keepers, manual labor workers and other less skilled people can be up-skilled enough to stay competitive. What to do with these people?

 This article incorporates thoughts, research & content of GE as well as of other thought leaders


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From Supply Chains to Digital Supply Network and Digital Collaboration Networks

Digital has a massive impact on the supply. Adding digital technology to enhance is not enough. Companies must re-invent their supply chains making their underlying DNA digital.

The Supply “chain” has evolved from linearly thought process to a circular concept (Circular Economy) to a now system of networks (Digital Supply Network).

This new Digital Supply Network allows to add/ drop partners and contributors fast and fill any disruptions with immediate alternatives.  It maximizes access to information, knowledge, innovation, resources, materials, infrastructure, services, products, etc.  It enables to respond to market opportunities at lighting speed and scale large without requiring upfront large investments. Cloud and XaaS services offering often almost immediately pay per use solutions.

Digital Supply Networks require a new vision and way of thinking. Traditional, text book supply chain methodology breaks down.

Disruptive technologies such mobile, social media, cloud, big data & analytics enable unprecedented opportunities to gain a holistic and detailed understanding of the real-time situation and take the best decision for the particular moment. Like smart IT networks automatically reconfigure themselves when one node breaks, so digital supply networks will equally and easily adjust. Mass production and mass customization are now fully possible fast and very efficiently.

Digital Supply Networks will then finally evolve into Digital Collaboration Networks, where there will not be one major driving company that procures from “outside” to produce/ deliver their products and services, but rather where a collection of individuals and organizations collaboratively add and receive inputs and support to create a collection of outputs, products and services.

What companies can/ should do?

 -       Follow a systematic approach

-       Re-visit your business strategy and align to the vast changing market, customer and competitive environment

-       Identify core strengths and capabilities across the key value chains.

-       Develop suitable Operating Model, architect a blueprint for the future.

-       Drop none-value adding work. In-source and outsource accordingly.

-       Streamline your processes, people and metrics accordingly.

-       Drive value transformation and optimization across the entire organization

-       Extend to your effort to your partners and tier 1 suppliers

-       Use data & fact based approaches (Lean Six Sigma, etc.), digital technologies (Big Data & Analytics, AI, Visualization etc.) to analyze complex cause-and-effect interrelationships and to gain an “end-to-end”/ holistic understanding for executive planning & decision making.

-       Think and optimize in concentric overlapping life cycles of products, services, innovation,

-       Focus on execution excellence, but in an agile, experimental way of continuous improvement

Specific examples of how digital impacts key functions within the organization

Procurement

Companies need to blueprint the future procurement operating model, optimize spend demand management through zero-based budgeting, sourcing for direct/core categories, relationship management and risk strategies. They need to improve total value of ownership by reducing overhead expenses and COGS; develop new cash flow streams through supplier innovation; reduce environmental and community development costs; decrease working capital.

The procurement operating model blueprint maps how a client’s procurement organization will operate across their organization, process, talent, and technology (and digital) landscape in order to implement their strategy and achieve the targeted operational and financial improvements.

Companies need to better plan and optimize their supply chains. At British Telecom we drove the reduction of the large supplier base with the objective to selectively focus on less suppliers, but developing more partners that would help BT achieve its strategic objectives and to achieve more collaboration, value contribution, innovation, speed, delivery capability and operational excellence.

Product lifecycle management (PLM)

Traditional product value chains used to be linear. Now they are fast-moving product development value networks, encompassing an extended ecosystem of partners, suppliers, manufacturers and customers—all influencing the product lifecycle.

Digital enablers and new technology paradigms have become part of the product development and lifecycle management process, utilizing big product data and digital infrastructures

Digital technologies can now reduce marginal cost of supply to near zero. Companies like YouTube, Kickstarter, Airbnb and Uber show you don’t need to own assets to provide trusted access. The cost for each of these companies to add a new room, video or car is near zero.

Digital allows the industry’s value chain to be completely unbundled. There are thousands of start-ups attacking existing markets of incumbents.

Customers use digital to change the way they interact with products, companies are looking for ways to use digital to develop them. “Words like ‘gym’ or ‘shops’ are nouns that describe a business but no longer define it. Digital is making the nouns a lot less relevant than the verbs.

Companies must take a holistic, transformational perspective on Innovation, Product Development and PLM. They strengthen outcome-driven business discipline that harmonizes people, process, data and systems. Everything should be driven by a measurable, outcome-driven business case.

R&D and Advanced R&D

When working with Huawei on developing and enhancing its Advanced R&D capabilities years ago, we looked at industry best practices to drive transformation. Today Huawei is an industry leader in new annual patents. Huawei eagerly learnt from the best and become a star.

Digital calls companies to rework their global operating model in R&D; planning and procurement; manufacturing locations; talent acquisition, retention and growth. The focus must be to develop and launch the right product, at the right time for the right cost.

 Product portfolio management

Companies need to adjust and optimize their product portfolio. It requires reducing complexity (using insight driven customer demand) and cost-to-serve to improve margins and minimize product costs.

Frugal Innovation, a term coined in developing countries, is a great concept that applies also to the developed markets. Basically, it focuses on just the features that a potential customer is just willing to pay and dropping other cost drivers that are just waste.

XaaS is more than just a new way to deliver technical capabilities, it is a major shift in the fundamental business model of the industry. XaaS allows companies to sell differently, to an expanded set of buyers, with a different value proposition – all of which much be backed up by a transformed set of commercial capabilities.

It also allows to reorganize engineering capabilities in order to increase developmental agility while maintaining quality and predictability.

In a time when consumers increasingly want to rent or pay for use, rather than own, companies need to adjust their business models. It may require organizations to diversify their business and play with numerous business models.

Ensure strategic alignment between the application landscape and business imperatives

• Reduce application redundancies and drive standardization

• Assess and address technology risks

+++To share your own thoughts or other best practices about this topic, please email me directly to alexwsteinberg (@) gmail.com.

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Key trends in mobile and making the right technology decisions

Mobile Trends

Mobile companies are focusing on connecting the next billion consumers. In many developing countries most people by-pass the computer and go directly mobile.

Our generation Z has already grown up with the intuitive understanding of click & find, click & know, and click & buy. Mobile is increasingly becoming the starting point for everything, not just an add-on. Many young people already consider their mobile device as a remote control to life.

The car is becoming a mobile device. Mobile is set to transform the banking, financial & payment markets. Mobile wallets are already replacing cash.

Mobile devices are becoming integral part of the consumer process: Wearables (smart glasses, watches, wristbands, body implants), Appcessories (mobile devices with computational abilities to collect and analyze data about the world), medical smart phones (serving as wellness gurus, performing self-diagnosis, reminding about exercise and medication), etc.

People expect the content to be increasingly personalized, hyper relevant and automated.  

Brands recognize the opportunity to reach the right people, with the right message, at the right time to achieve maximum impact. They will focus on privacy, permission and preference of their target audience. People have a much lower tolerance for unsolicited messages arriving at their mobile device (considered part of their being and personality).

Building a long-term strategy for mobile from a technical perspective

Integrated mobile commerce capabilities are a must for companies. Selecting the right technology approach is critical to drive user experience, cross channel activity, profitability, etc.

Select the right mobile environment

Mobile sites appear in the browser of any internet enabled device, which most likely can access it. No need for users to download anything. Content is automatically formatted to the device. HTML5 gives mobile Website increasingly App-like capabilities.

Mobile applications require download from the market place. The applications native capabilities provide enhanced functionality such as caching for off-line usage, GPS location service, scanning…

Usually companies do not have to make large investments in new technologies to support mobile expansion. Companies can leverage their core technologies including e-commerce platform, merchandising tools, product information, Content management system user reviews.

Four options to build your mobile environment

Companies can either develop and implement a mobile solution themselves or use outside help from various sources. Here, are four main options organizations may consider:

1)      Create and manage home grown solution.

a.       It allows for tight integration and full control.

b.      But it requires much technical skills, ramp up time and upfront cost.

2)      Engage a mobile service provider.

a.       You can obtain a fully outsource mobile solution, including development, hosting. Expertise is acquired, existing internal skills and capabilities can be leveraged. Maintenance and long-term support are guaranteed. There a short ramp up time and modest upfront costs.

b.      But there is duplication of website data and configuration. Often lack of integration with existing infrastructure. High long-term TCO. A first workable solution, but unlikely to achieve a consistent user experience due to lack of integration with primary website and existing e-commerce solutions.

3)      Engage a software provider that has expanded into mobile

a.       Tight integration with existing infrastructure. Robust capabilities & features; integration of existing infrastructure & tools. Ease of maintenance and long-term support.

b.      Software provider’s mobile IP & technology expertise may not be cutting edge. There are upfront costs.

4)      Engage an agency

a.       Full service design & implementation (even manage customers, if desired). Advanced, custom-designed features with highly differentiated user experience; rich environment and integration with existing infrastructure.

b.      High-cost engagement, expensive to manage, difficult to change user experience; loss of in-house control, longer lead times.



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To share your own thoughts or other best practices about this topic, please email me directly to alexwsteinberg (@) gmail.com.

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Big Data & Analytics - the full view (upon request)

Upon request, I have put together the five parts of the previous published Big Data series and combined into one documents. This offers you to read everything in one place. Please share your thoughts and best practices with me. You always may email me directly to alexwsteinberg@gmail.com
Big Data Series - Part 1   Technical challenges
Big Data requires to learn much about data as an asset and analytics. Data is the most precious asset in an organization, the currency of the enterprise.
Companies’ data ecosystems have become complex and littered with silos. A large majority of companies is still not able to make full use of Big Data advantages.
There are many challenges with Big Data: Lack of knowledge, varying definitions & expectations, different views about data sources and use cases, ignorance about valuable data sources, technologies, etc.
Companies must understand data across the entire data supply chain and their individual stages: Identifying & leveraging data sources, importing, enhancement of data value, combination with other data, generation of insight, and taking of specific actions.
This means: companies must mobilize data across the enterprise; deeply understand, analyze and determine value of respective data; understand business use case and data patterns to determine appropriate actions.
It requires companies to commit to continuous discovery, experimentation, testing, learning, adapting and innovation.
There are many approaches, solutions and technologies presently offered in the Big Data domain and quickly evolving. Companies need to be aware of the different options and their pros & cons to combine those to an overall solution.
Continue part 2 out of 5  
Big Data Series – Part 2 - Traditional data approaches not enough anymore
Given the varying types, sources and sheer size of data today the traditional approach of collecting data in a staging area, transforming into desired format, loading in mainframe/ data ware house and then delivering requested data to users on a point by point query does not work well any more.
Companies must perform calculations, run simulations models, compare statistics at fast speed to generate insights. Real-time analytical tools able to pre-process streaming data and correlate data from internal and external sources, offer interesting opportunities, but also complex challenges.
Data acceleration enables massive amounts of data to be ingested, processed, stored, queried and accessed much faster. It ensures multiple ways for data to come into the company’s data infrastructure and be referenced fast.
Data acceleration leverages hardware and software power through clustering and helps correlate different data sources, including localization. It improves interactivity by enabling users and applications to connect to the data infrastructure in universally accepted ways and ensuring that user queries are delivered as quickly as required.
Continue part 3 out of 5
Big Data Series – Part 3 Six technology components for Data Acceleration
There are at least six key technology components to build a supporting architecture: Big Data platforms, Ingestion solutions, Complex event processing, In-memory databases, Cache clusters and Appliances. Each component helps with data movement (from source to where needed), processing and interactivity (the usability of the data infrastructure).
Big Data platform (BDP)
BDP is a distributed file system and compute engine. It contains a big data core, a computer cluster with distributed data storage and computing power. Replication and sharding partitions very large databases into smaller, more easily to manage parts in order to accelerate data storage.
Newer additions enable more powerful use of core memory as a high-speed data store. These improvements allow for in-memory computing. Streaming technologies added to the core can enable real-time complex event processing. In-memory analytics support better data interactivity.
Further enhancements to the big data core create fast and familiar interfaces with data on the cluster. The core stores structured and unstructured data, but requires map/reduce functionality to read. Query engine software enables the creation of structured data tables in the core and common query functionality (SQL etc.)
Ingestion
Collecting, capturing and moving data from its sources to underlying repositories used to be done traditionally through the extract, transform and load ETL method. Today the priority is not the structure of the data as it enters the system, but assuring that all data is gathered covering different increasing data types & sources and quickly transported to areas where it can be processed by users. Ingestion solutions cover both static and real-time data. The data the gathered by the publisher and then send to a buffer/ queue, where the user can request the data.
Complex Event Processing (CEP)
After data ingestion the CEP is responsible for preprocessing and aggregation (& triggering events). It tracks, analyzes and processes data of events and derives conclusions. CEP derives data from multiple sources and combines historic as well as fresh data in order to infer patterns and to understand complex circumstances. Its engines pre-process fresh data streams from its sources, expedite processing of future data batches, match data against pre-determined patterns and trigger events based on detected patterns.
CEP offers immediate insight and enables fast action taking. In-memory computation allows to run Data movement and processing in parallel, increasing speed. CEP solutions add computing power by processing the data before it is submitted to the data stores or file systems.
In-memory databases (IMDB)
IMDBs are faster than traditional databases, because they use simpler, internal algorithms and executive fewer central processing unit instructions. The database is preloaded from disk to memory. Accessing data in memory eliminates the seek-time involved in querying data on disk storage. The applications communicate through SQL, which receives records in the RAM and triggers the query optimizer.
IMDBs constrain the entire database to a single address space. Any data can be accessed within microseconds. The steadily falling RAM prices favor this solution.
Cache Clusters
They are clusters of servers in which memory is managed by a central software designed to transfer the load from upstream data sources (databases) to applications and users. They are typically maintained in-memory and can offer fast access to frequently accessed data. They sit between the data source and the user. Traditionally they accommodate simple operations such as reading and writing values. They are populated when a query is sent from a data user to the source. Prepopulating data into a cache cluster of frequently accessed data improves response time. Data grids can take caching a step forward by supporting more complex queries and using massive parallel processing (MPP) computations.
Appliance
Massive parallel processing sits between data access and data storage. Appliance here is a pre-configured set of hardware and software including servers, memory, storage, input/output channels, operating systems, DBMS, admin software and support services.
It may have a common database for online transactions and analytical processing, which improves the interactivity and speed. Appliances can perform complex processing on massive amounts of data.
Implementing and maintaining high performance data bases on clusters is challenging and few companies have the necessary expertise to do so themselves.
Custom-silicon circuit boards enable to develop their specific solutions. It enables development on devices for specific use cases and allows for network optimization (integrating embedded logic, memory, networking and process cores). This plug and play functionality offers interesting possibilities.
Continue part 4 out of 5
Big Data Series – Part 4 Creating a suitable Technology Stack/ Solution
All of these components bring their individual technology features. Companies must wisely put together an overall solution from among those components, leveraging their complementary advantages and customizing those to their particular needs.
There are four fundamental technology stacks (with their variations) offer possible solutions:

1. Big data core only or with enhancements (with complex event processing, with in-memory database, with query engine or with complex event processing and query engine)
   • This technology is the de-facto standard for exceptional data movement, processing and interactivity.
   • Data usually enters the cluster through batch or streaming.
   • Events are not processed immediately, but in intervals. Enables parallel processing on large data sets, and thus advanced analytics.
   • Applications and services may access the core directly and deliver improved performance of large, unstructured data sets.
   • Adding CEP enhances big data core processing capabilities, real-time detection of patterns in data and trigger events. Enables real-time animated dashboards. Could add machine learning program to the CEP.
   • IMDB can further increase computing power through placing key data in RAM.
   • Query engines can further open interfaces for applications to access big data even faster.
2. In-memory data base (IMDB) cluster only or with enhancements (with Big Data Platform, with complex event processing)
   • External data is streamed in or transferred as bulk to the IMDB
   • Users and applications can directly query the IMDB, usually through SQL like structures.
   • The incoming data is first pre-processed through the BDP before it goes to the IMDB
   • In case of CEP, the CEP first ingests the data; the processing is then done in the IMDB and then returned to the application for faster interactivity.
3. Distributed Cache only or with enhancement (with Application and Big Data platform)
   • A simple caching stack sitting atop of the data source repository. The application retrieves the data. The most relevant data subset is placed in the cache.
   • Processing of the data falls to the application (may result in slower processing speeds)
   • If BDP, the BDP ingests the data from the source and does the bulk of the processing, then puts data subset in cache.
4. Appliance only or with enhancement (with Big Data platform)
   • Data streams directly into the appliances; the application talks directly to the appliance
   • If BDP, the BDP ingests and processes data. The application can directly talk to the appliance for queries.

Continue part 5 out of 5

Big Data Series – Part 5 – 12 Immediate suggestions to build a data supply chain

• Consider data as perhaps the most important asset in your organization. Become data driven. Some people call it “data religious”.
• Research about Big data & Analytics best practices. It requires continuous learning. Refer to the different approaches offered in previous blogs (Data Acceleration Part 1 and 2).
• Do an inventory of existing data. Focus on most frequently accessed and time-relevant data.
• Identify, simplify and optimize inefficient data processes. Eliminate manual, time-consuming data curation processes (such as tagging and cleaning).
• Identify currently unmet business needs and develop solutions.
• Identify and overcome data silos.
• Simplify and standardize data access through a robust data platform
• Build an effective technology stack using one of the four suggested options while leveraging some of the described six components (Data Acceleration Part 1 and 2).
• Further explore API management, traditional middleware, PaaS and other possibilities
• Analyze current internal data sources and look for still hidden sources. Explore external sources to increase quantity and quality of available data.
• Identify and improve individual data supply chain streams
• Develop a systematic roadmap for building an effective overall data supply chain

Special thanks to Accenture Technology Labs and Analytics Group, whose thought leadership, best practices and white papers have served as inspiration and knowledge source for this Big Data series.

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To share your own thoughts or other best practices about this topic, please email me directly to alexwsteinberg (@) gmail.com.

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Digital Supply Chain - Framework and best practices

Digitalization is impacting (and disrupting) almost every industry. Supply Chain Management (SCM) is a prime candidate for value creation using digital thinking and technologies.

The complexity, scope and fluidity of today’s supply chain create enormous amounts of data, information and change. SCM must integrate, manage, optimize, change activities – ideally in real time (as the market and other factors require).

SCM covers both internal and external operations, covering clients, end customers, partners, suppliers, service providers on a global scope. Government and trade bodies, NGOs and other parties increase challenges. SCM deals with a huge number of divers stakeholders creating a complex web of contacts, roles, interests, flows of goods, services and information spanning.

In this blog, I would like to share thoughts for improving  and digitalizing supply chain management. Like always, I base my views on best practices. Here, I summarize and present methodology from Capgemini Consulting, that I regularly use for benchmarking:

Traditional supply chain models have resulted in rigid organizational structures, inaccessible data and fragmented relationships with partners. We often find a combination of numerous key deficits: Lack of transparency, agility, end-to-end process integration; sub-optimal use of locations and labor cost differences, bundling of tasks; overly complex IT landscapes.

Often several hundred applications supporting supply chain processes, lead to lengthy implementation cycles and overly high maintenance costs. Disparate IT systems bring in inconsistency and redundancy in data.

Digital supply chains are based on a digital operating model that implements digital capabilities along the organizational layers of governance, processes, data & performance management and IT. Such model enables business process automation, organizational flexibility and digital management of corporate assets.

Business Process Automation bears a value driver potential of on average 20 percent of the cost base. It integrates business processes, collaborates with customers and suppliers, has event driven process scenarios and embeds analytics/ optimization. It is about straight through processing, complete execution of end-to-end processes without the need for re-keying or manual intervention. All necessary data is available to employees to complete the transactions. Management of physical flows is enabled by a closely knit web of checkpoints that are tracked and monitored.

Organizational flexibility bears a value driver potential of on average 50 percent of the cost base. It accelerates business process innovations, manages a mix of global and local processes, flexibly handles In & Outsourcing and rapidly implements new business models. It gives greater freedom to choose the appropriate degree of centralization needed to support specialization or minimize process costs. Centralizing specific functions can generate higher value through better quality and productivity. Central master data management helps avoid double entries and inconsistencies; while supply chain planning activities benefit from a bigger pool of optimization objects.

Digital Management of Corporate Assets bears a value driver potential of on average more than 5 percent of the cost base. It generates new business insights, operates a scalable data model (processes, product lines, customers) and integrates views (financial and operational KPIs, internal and market data). As information becomes available at the micro level it allows companies to treat a single customer order as a profit center or a single process as a cost center. Aggregation of all these transaction results in much more accurate performance measurement of a specific customer, industry segment or location.

Capgemini offers a systematic Framework with five layers for Digital Transformation of Supply Chain Management:

On the top, layer 1, Digital Supply Chain strategy integrates digital initiatives into the overall supply chain strategy in order to generate and measure long term value. The identification of business benefits requires top management expertise and inputs regarding currently perceived pain points and industry best practices. Typical outcomes of an analysis of pain points are often broken processes, local instead of global optimization, low visibility, etc.

Supply Chain Operating and Governance Model, layer 2, helps realize the full potential of being a global company. It examines internal alignment of roles, procedures, service level agreements and transfer pricing schemes.

Integrated Supply Chain Performance Measurement, layer 3, uses Web 2.0 technologies to trace every order or transaction. Tagging technologies and virtualized data centers make information available. Combining this operational data with financial information, with external data from market and benchmarking efforts improves decision making.

Integrated Supply Chain Performance Management, layer 4, integrates the different supply chain functions such as product development, procurement, production, maintenance, and logistics across locations in order to minimize waste and non-value added activities.

Supply Chain Technology Architecture and Infrastructure, layer 5, provides the design logic for business processes and IT infrastructure, integrates and standardizes requirements of the organizations operating model. The challenge is to select and implement digital technologies and integrated platforms that employ reusable and exchangeable components with minimal investment in time and effort. Examples are RFID, wireless tracking devices, warehouse labor and vehicle management systems, voice-directed picking devices, etc.

Since 2005, I have been working with British Telecom’s Supply Chain Excellence practice. Auditing, evaluating and improving BT’s partners and suppliers I have witnessed the enormous opportunities to generate value for companies: Produce better products & services, respond to clients faster and more flexibly, develop effective eco-systems of partners, suppliers and customers…

Leading improvement efforts across all business functions and value chains, I could align structures & roles with strategies, optimized systems, processes, policies and procedures. Digitalization gives us now the tools and technical capabilities to take supply chain management to the next level – a global, truly holistic, agile, effective and cost-efficient, living and continuously improving ecosystem.

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To share your own thoughts or other best practices about this topic, please email me directly to alexwsteinberg (@) gmail.com.

Alternatively, you also may connect with me and become part of my professional network of Business, Digital, Technology & Sustainability experts at

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Google+ at  https://plus.google.com/u/0/+AlexWSteinberg/posts