Step by step solution to develop and implement Product Life Cycle Management (PLM)

Global product development and support capabilities have become a key differentiator of corporate financial performance. Innovation and product develop is becoming now accepted as a core business discipline.

This article discusses the challenges and offers a step-by-step process to implement best practice Product Life Cycle Management (PLM).

Understanding PLM

Product Lifecycle Management (PLM) is a strategic corporate asset, a cross-functional, enterprise discipline that augments innovation, drives revenue growth and operational efficiencies.

PLM includes product strategy, portfolio and product management. It covers all activities from idea generation; requirements gathering; product design, engineering, validation & compliance, costing quality, direct material sourcing, manufacturing, after-market services and product retirement.

Various capabilities support PLM including: product structure & reuse; part & intellectual property management; engineering changes; state-gate approvals; software configurations; quality tests & defects; product costs; development project status; design and scientific tools; analytics. The focus is on effectiveness, efficiency and innovation.

Complexity results in wasted effort & resources

Large companies make substantial investments within the PLM process, but half of the spending is wasted on products & output that do not meet market needs or timing. Lack of central coordination, prioritization and integration of processes, systems, data and people results in a substantial number of non-value adding activities & tasks and effectiveness & efficiency drags.

Working with various PLM vendors across capability areas further increases complexity.

Understand client needs and innovate

-          Use understanding of consumer behavior and customer needs as starting point in order to enhance product portfolio and reduce complexity

-          Assess the potential innovation opportunities constantly. Synthesize customer insights, emerging technologies and leveraging own core competencies.

-          Focus on the features that the client really wants/ is willing or able to pay for

-          Practice frugal innovation principles for developing, but also developed markets

-          Re-focus innovation resources on challenges that matter

Design plays a key role

Design plays a pivotal role, as up to 80 percent of product’s cost, quality and client perceived value is locked during the design phase.

Shockingly, currently about half of R&D spending is wasted. The R&D project portfolio must be aligned to more understood market demand and capacity to deliver better sized in order to achieve improved time to market and greater “hit rate” of products.

There are many design types & techniques that should be integrated into an overall Design Practice:

-          Design for sourcing

-          Design for engineering

-          Design for manufacturing

-          Design for dismantling, recycling and zero waste

-          Design for use/ life span

-          Design for serviceability

o   Ease of servicing and reachability (from technician or repair person standpoint)

-          Design for the environment

-          Design for overall Sustainability (including Carbon footprint/ GHG emissions)

Various design & development activities (styling & industrial, mechanical, electrical, integrated circuit engineering, artwork & packing design; software development and technology research) needed to support a company’ portfolio of offerings, increase complexity.

Reduce product complexity

-          Understand the problem

o   Direct materials and components make up 60 to 80 percent of product costs

o   Component fragmentation and limited reuse drives up costs by 10 to 15 percent and increases component count by 30 to 70 percent.

o   It negatively impacts product cost flexibility and speed-to-market

-          Conduct fragmentation assessments; component parametric and substitutability analysis

-          Consolidate global sourcing and supplier management

-          Implement governance and component standardization metrics

-          Simplify Service BOMs

-          Implement end-to-end part standardization

Improve operational efficiency

-          Eliminate bottlenecks capacity constraints and delays.

-          Complexity and costs created in the supply chain must be justified by the revenue generation

Use standard methodologies and tools

-          Lean Six Sigma

-          Value Engineering methods (you may refer to a related article in my blog section)

-          Examples : SAP/ PLM, Oracle/ Agile, Siemens/ TeamCenter, PTC, Dassault Systems

4 key steps to develop and implement PLM

1)      Create an enterprise-wide framework to define the organization’s PLM capabilities.

a.       Define what is and is not PLM

b.      Review all processes, applications, metrics, organization and data that underpin product development process follow (from initial concept to product retirement)

c.       Examine the performance and maturity of each across all organizational entities and competencies.

d.      Connect all corners of the PLM landscape with each other

e.      Determine about 15 to 30 Level 1 capabilities and break further down into Level 2 and 3 (capabilities will increase on each lower level as covering business more in detail)

f.        Companies will be surprise of how disjointed and fragmented their overall PLM approaches are, how many gaps & redundancies exist and little, few metrics & documentation support their PLM activities.

2)      Link the PLM framework capabilities to key corporate and product priorities

a.       Use 5 to 10 business metrics to link; and also to track the effectiveness & efficiency of innovation and product development outputs.

                                                               i.      The metrics should transcend any one department or function and link causes to effect.

3)      Link new enterprise PLM framework to corporate priorities and use as ongoing PLM planning tool

a.       Deconstructing the organization’s PLM capabilities serves as powerful tool for ongoing planning activities

b.      It enables the many, disjointed constituents to have a meaningful dialog about trade-offs, PD investment decisions

c.       It helps measure impacts of projects over time against key metrics.

4)      Establish/ empower group to own, review and update the PLM framework and corporate road map.

a.       Unambiguous, unwavering and visible senior executive sponsorship is necessary to ensure that PLM becomes part of company’s innovation fabric rather than a one-time project or program.

 

Special thanks to Kevin Prendeville, Managing Director – Accenture Product Lifecycle Services, Global and North America, for some of his publications & content.


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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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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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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.

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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Keys to building your digital commerce platform right

Building effective commerce platforms are important to meet the sophisticated needs in the digital age. The undertaking is very complex as it requires work on the strategic, tactical and operational level. Looking only at technology is a frequent shortcut to failure.
Every larger company should consider working with a service provider that can ideally address all aspects of this complex undertaking.
Based on previous projects, I provide you with a check list of key areas and recommendations.

Vision, strategy and business model
Your service provider (SP) should be able to help you develop/ refine your digital vision, strategy and business model. Your SP should be able to help you build a roadmap for transforming your organization.

Management, business and technology consulting
Your SP should not only speak tech-language, but truly understand your business needs from top to bottom. Throwing technology at problems, before clarifying/ solving the business side, often only creates more problems.
The SP should provide true vertical domain knowledge, including customer behavior & 360 degree view, Omni-channel objectives, partner models, inventory, pricing and sales.

Operations
Many areas are obvious candidates for outsourcing. Your SP should be able to take you through various options such as hosted, managed services, performance based pricing models, staff outsourcing, etc.
As requested, the SP should be able to provide commerce infrastructure and operations services (advanced automated site testing, performance management, security testing on cloud platforms)

Systems
Often upgrades to ERP and order management systems are necessary. The SP needs to be able to integrate disparate internal client systems (legacy, home grown ecommerce systems, etc.) and advise on design and architecture.

Technology
Ability to implement the technology flawlessly has become a basic requirement. The SP need to implement a solid commerce technology infrastructure able to meet the expected demands in the future. It involves upgrading and optimizing existing sites and integrating diverse technology programs to enable a comprehensive digital experience. As needed ecommerce, call-center, in-store and other areas need to be fully brought together; applications, mobility wisely integrated.
Big Data & Analytics increasingly drive the marketing of the digital age. XaaS and Cloud enable faster, better and more efficient business and operations. The SP needs to understand these areas and incorporate those effectively into the overall solution.
The SP should offer accelerators and own of the shelf software and solutions to minimize time/ cost to market and maximize value of the new commerce applications. Further the SP must be able to provide solid post-implementation support.

Organizational alignment and Program Management
Markets, Customers and Competition are rapidly evolving. Realignment of the organizational structures, roles, processes, policies, procedures and metrics is unavoidable. The SP should provide guidance in organizational design, transformation & change management. Solid Program management and project execution should support all efforts.

Innovation and agility
The SP should be able to drive innovation and offer best practices from across the industry; leverage insight and solutions from its own innovation labs.
The SP should practice agile methodology and should drive agility throughout the organization.

Price/ Costs
Be aware that the price quoted is not the total cost! Some service providers may be considerable cheaper, but also come with substantial burden on the client organization in terms of increased management time and other overhead. The biggest – often unaccounted - expense – is that of missed revenue and business opportunity.

Engagement
Last but not least the SP should really support a collaborative arrangement and offer various pricing and operating models in line with the client’s needs and performance metrics.

Finding the right Service Provider
Not all Service providers (SP) obviously have breadth and depth of experience to provide the mentioned necessary service. Nevertheless, a good number of companies, also called Global Commerce Service Providers, are able to address most of those needs and provide full client solutions.

Accenture Digital, Sapient Nitro, Deloitte Digital, Razorfish Global, Cognizant and IBM GBS are among the leaders of GCS Providers. Strong contenders are Tata Consulting Services, Infosys, HCL, Wipro and others.

Finding and selecting the right Service Provider for your company is both a science and an art. This guide has given you some key pointers. For questions and thoughts, please let me know.
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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

https://www.linkedin.com/in/alexwsteinberg   or
Xing at https://www.xing.com/profile/Alex_Steinberg   or
Google+ at  https://plus.google.com/u/0/+AlexWSteinberg/posts