What is Value Stream Management (VSM)? Learn how Planview's VSM helps to achieve better business outcomes

Project and portfolio management discipline in Planview Tasktop’s VSM guide

Superpower:

Mid to long-term strategic planning and execution tracking

DISCIPLINE AND NOTABLE VENDORS:

What Do You Use It For?

Define and manage strategic goals and business and product visions
Break down strategic goals into accomplishable deliverables and assign them
Attribute and track business value per deliverable. Measure market results of the delivery (often through OKRs)
Plan and coordinate execution across multiple programs, solutions and portfolios
Allocate resources and plan capacity
Report on and oversee execution across all teams, programs, and solution groups within a portfolio

how does VSM help the discipline improve?

Analyzes data coming from PPM tools to identify how to improve the underlying people, process and technology that influence how quickly business value flows from concept to reality
Measures Flow Time and Flow Velocity across value streams to improve predictability, reveal trending and inform planning
Identifies bottlenecks and waste in overall end-to-end processes and in each product value stream individually
Highlights where to invest to shorten time-to-market and unlock capacity
Reveals necessary adjustments to Flow Distribution and Flow Load to support portfolios’ long-term health and business goals

Example

A financial services company has a strategic initiative to implement new tax legislation by January 2022.

here’s how they’d use their VSM tool:

The Portfolio Leaders and PMO (if exists) will turn to their VSM tool to understand the systemic bottlenecks impacting multiple product value streams working on this initiative. This data tells them where they need to make investments to eliminate constraints through staffing, technology or process changes. They’ll use the data to make the business case to executive leadership that these investments will ensure successful delivery.
Next, with their Product Managers, they’ll consult the Flow Metrics for each product value stream contributing to the initiative. They’ll look at Flow Time and Flow Velocity to predict how long it will take each one to deliver the new tax legislation features. They’ll identify which product value streams have flow issues that may pose a risk to on-time delivery.
Product Managers will use their VSM tool to set intentional targets for Flow Distribution, prioritizing capacity for new features through December 2021, but then allocating considerable capacity to the accrued tech debt and post-launch defects starting in January.
Over the course of the initiative, the Portfolio and Product Leaders will monitor trends in Flow Metrics to identify issues that may impede the ability to deliver the features, for example too much unplanned work, too much concurrent work, long wait times in under-resourced or under-automated areas, or delays due to dependencies. They’ll use this data to proactively address and mitigate the issues to ensure the success of the initiative.

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Example

A health insurance provider has gone through an Agile transformation and is now taking its first steps in the shift from project to product.

One of the Engineering Manager’s biggest pain points is how funding starts and stops annually. She feels this creates alternating cycles of team starvation and overload. She also knows — but cannot quite quantify — that a lot of completed work gets voided once funding is lost. The spent resources never actually deliver any value to the business, and basically hurt profitability.

The Engineering Manager wants to communicate all these points to the CFO to get buy-in to pilot a continuous funding model.

here’s how they’d use their VSM tool:

The Engineering Manager consults the Flow Metrics dashboard for her product value stream. She observes that, as typical for Q1, the team is highly loaded. Flow Load (a measure of WIP) is very high — consisting of both work that’s been started but now waiting for funding renewal as well as new initiatives still waiting for their first funding approval.
Insights into Flow Load generated by the VSM tool allow her to see what is happening in more detail: 45% of Features work have no code committed against them; 55% of Features have code committed against them, of which half haven’t had a code checkin in over a month.
She shares these insights with her VP and together they begin to make the case to their CFO. They demonstrate that in the current model waste is extremely high: more than 25% of Features advanced so far in the value stream as to have code committed to them — only to have the work voided due to loss of funding. They translate that waste into dollars and can demonstrate how hundreds of thousands of dollars are being wasted each year, for this value stream alone.
She also presents, together with the Product Manager, how in many cases the loss of funding meant the original business cases were rarely delivered in their entirety.
With the CFO’s blessing they begin a pilot of a continuous funding model during which they target reducing time-to-value (Flow Time) by 50%.
In addition to tracking Flow Metrics throughout the pilot, they also track the impact on business outcomes in their VSM tool. The data allows them to show the CFO, during their regular checkpoints, how the shorter delivery timelines improved their top line. They also show how waste due to cancelled work went down from 25% to 5%.

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Example

A government agency has adopted release automation and deployment tools to get them out of spreadsheets and into a more automated release process. Now, they can view the live status of a release and get timings per application.

However, due to the complicated nature of their solutions, releases are still high-touch, heavily coordinated events that take place over weekends. Staff is on-call to perform specific manual operations and tests during the release process.

Despite an agile transformation and implementation of some DevOps processes, business value is delayed for weeks and months while waiting for one of these heavily coordinated releases.

here’s how they’d use their VSM tool:

Starting with a small selection of products from the most critical business domains, the Product and Engineering Managers will model the end-to-end flow of epics, stories and defects through their product value streams.
Once Flow Time, Flow Velocity and Flow Efficiency are baselined, they are able to measure wait times.
The findings are clear: business value delivery is held up after code changes are complete. Stories that take 24 days on average to deliver, wait an additional 50 days for release. And stories related to product quality improvements wait up to ten times longer on the backlog than new business capabilities.
The business case for finally granting each product its own autonomous road to production is clearly made to executives. As a next step, leadership prioritizes investment in three focus areas to improve the release process.
The first area is self-service around database changes, infrastructure, security and performance testing to remove dependencies on a central, constrained function.
The second area is implementation of feature flagging to allow each product to release new capabilities at their own pace, while supporting retest when the dependent functionality is available.
The third area is improved architecture, implementing micro-services to disarm dependencies between product components and layers.

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Example

For the IT staff at one of the largest providers of specialized hospitals and rehabilitation facilities in the U.S., supporting rapid growth with a relatively small core team had become a daily challenge. One key product value stream was under particular pressure, as they developed and supported the systems used by tens of thousands of patient care specialists.

While the business often complained feature delivery was too slow, the developers were either drowning in unplanned support work or blocked while waiting on input or approval from the business. They sought to understand which investments in people, process and technology would help them go faster.

here’s how they’d use their VSM tool:

The Product and Engineering Managers consulted their Flow Metrics to better understand where their work was slowing down.
Flow Distribution, which measures the de-facto investment in different types of value creation, revealed that nearly 70% of their delivery was resolutions to bugs and support tickets. Only 25% of their capacity was going towards features and new business capabilities.
A review of the work items that made up the lion-share of this Defect work turned out not to be software defects, so much as a confusion and misunderstanding of the new capabilities or UX changes they had been releasing weekly. Their users could not keep up and the changes were disrupting their ability to provide patient care.
While the support tickets were resolved through conversations — and not code — they were consuming precious developer bandwidth.
They decided to run an experiment to use feature flagging to artificially slow down the release of UX changes from once a week to once a month. The monthly release was accompanied by a newsletter and training.
They immediately observed an improvement in Flow Distribution, and subsequently, month after month the distribution of defects declined while Flow Velocity for features increased.
Time spent on support tickets went from 60% to 20%. Feature Flow Velocity doubled! They were able to roll out the new technology to hundreds of facilities without disrupting patient care.
These phenomenal results were shared with other product value streams, who soon adopted the revised release schedule to similarly favorable results.

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Example

A financial services company must implement new tax legislation by January 2022. Many product value streams are affected, but the VP Engineering is concerned that one specific product will not be ready on time. She’s proposing to hire more developers to boost their velocity and guarantee their delivery.

here’s how they’d use their VSM tool:

The value stream leaders — product owner, engineering lead, scrum master — consult their Flow Metrics and 6-month trends for Flow Time, Flow Velocity and Flow Efficiency. They use this information to predict how long it will take them to deliver. They have to agree with their VP: it doesn’t look good.
To get an idea what can be done to shorten those Flow Times and increase velocity, they look at the data underpinning their current 10% Flow Efficiency and understand their bottlenecks.
The VSM data reveals that work slows down and piles up in two value stream stages: 1) Stories in the “Draft” status, a stage owned by the product owner. 2) Stories in “Ready for QA” status, a stage owned by the testers. Neither of these issues can be solved by hiring more developers.
After further investigation and verification with their staff, the leads take the findings to their VP and present their recommendations. In order to deliver the business capabilities needed for the new tax legislation, they recommend hiring an additional product analyst as well as prioritizing efforts and resources to automate a significant part of their testing suite.
The VP approves these recommendations. She assigns an additional product analyst and approves an investment in test automation. The work to improve the test suite is tracked as Debt work, with an explicit understanding that in the next six weeks Feature distribution will go down at the expense of Debt work.
The impact of these changes on flow are tracked on the metric timelines, to monitor whether Flow Times are indeed improving with these changes. Within 9 weeks, the product observes a 15% improvement in Flow Time and a significant uptick in the product’s velocity.

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Example

An information security company has a SaaS product in its growth stage. With more customers using their product, they’ve started experiencing more frequent outages.

They’re trying to get better at managing these incidents quickly and efficiently without causing too much disruption to work in progress.

here’s how they’d use their VSM tool:

In their VSM tool, they have a Flow Metrics dashboard that measures their value stream’s flow and business results side-by-side, so they can understand the interplay between them.
They notice something interesting: Every time they have a production incident, Flow Velocity (throughput) declines by an average of 5 Feature stories. It seems the impact of each incident is so high such that it significantly reduces how much new value they can deliver to their customers.
Next, they use their VSM tool’s bottleneck analysis to understand how to resolve incidents faster. They notice that when an incident requires infrastructure changes, resolution time doubles from 45 minutes to 90 minutes. With a 99.5% availability commitment, that really only allows for two incidents per month before they start having to give customers credits.
They decide to make two improvements. To address the impact on Flow Velocity, they make more of an effort to have the incident handled by just one engineer, to reduce context-switching for the rest of the team. Second, they work with their infrastructure colleagues to set up self-service for things like increasing memory.
After running these two experiments they see great results. The Flow Velocity impact of an incident has been reduced from 5 flow items to just 1, and the Flow Time for incidents has improved by 50%.

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Example

A wholesale retailer with a successful membership club has an internal product that manages membership and rewards. The product has matured and stabilized over several years and has supported tremendous business growth. At the same time, its codebase has become complex, which has exacerbated developer attrition and slowed down delivery across the board.

The Product Manager is wondering whether it might be time for application re-platforming, but is looking for evidence that indeed the time is right.

here’s how they’d use their VSM tool:

The Product Manager consults her VSM dashboard, looking at data for the last 18 months. She notes that overall Flow Velocity has gradually yet steadily decreased over this time period. At the same time, Defect distribution has been climbing while Debt distribution has continued to decline.
She also notes that new hires and interns are taking around six months to fully onboard due to the complex codebase, such that their impact on Flow Time and Flow Velocity is delayed considerably.
Now that the product is mostly mature, the number of critical feature requests has also gone down, indicating the time may be right for the application re-platforming effort.
The happiness of her team, which she tracks side-by-side with her Flow Metrics has also been declining in the latest eNPS surveys.
The Product Manager asks her principal engineers to investigate options for re-platforming. They identify an industry-standard platform that would require less customization and allow them to use third party components with ease.
With a new option in hand, the Product Manager presents her case to VP R&D and the CIO. She asks for their support for the proposed plan: Over the next six months, her value stream would pay down a lot of technical debt through re-platforming, at the expense of delaying some new features. She explains this move will have several benefits: Faster delivery of features and defects in the future, faster onboarding of new team members, higher productivity with existing staff, reduced attrition amongst senior engineers.
With her leadership’s support, the value stream begins the application re-platforming effort.
Once the effort is complete, the impact on the business is significant: Onboarding new hires now takes only two months (instead of six). The next set of major features are delivered quickly and with ease. And employee happiness scores are much higher in the following eNPS survey.

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Example

A pharmaceutical company has a mature and successful internal inventory management product. It is used by the company’s lines of business and is critical to everyday business operations.

Over time, with the addition of more code and tests, the product’s build has gotten larger and more complex. At the same time, the build software itself has also gotten old and out-of-date.

Currently the build is slow and flakey, with regression tests frequently failing. Troubleshooting takes forever and the team has become increasingly frustrated. They are more overloaded than ever but getting less done.

here’s how they’d use their VSM tool:

The Engineering Manager consults the value stream’s Flow Metrics dashboard to gather data to confirm everyones’ feeling that the build has become a painful bottleneck. She focuses on the impact the build has had on Severity 1 incidents.
The metrics reveal that while an incident can be resolved very quickly in code — building, testing and ultimately releasing the code adds an average of 3.5 days to Flow Time. Flow Time for incident resolution has been getting longer consistently over the last 6 months.
Side-by-side with the Flow Metrics, she can see the impact on business results. In months where there were two or more incidents, order volumes and customer satisfaction have both declined.
She also observes that the team’s Flow Load (WIP) has been rising over this time period, because engineers are filling in the “dead time” between builds by taking on new work. As a result, team happiness is low as reflected in recent Employee NPS scores, which many employees attribute to their frustration with the build and the overhead it creates.
The Engineering Manager and DevOps Engineer design a plan to rework the build and present their evidence-based proposal to the Product Manager. They explain that it’s time to pay down the debt accrued in the build, even though it means delaying some planned features by approximately four weeks.
The team spends four weeks reworking the build. They track this work as Debt so they can see its weight in their Flow Distribution. Finally, they get to where they want to be, a fast build that takes less than an hour to release.
Next, they observe the impact on their Flow Metrics over the next few weeks. They see that Flow Velocity (throughput) has increased and Flow Time for incidents has gotten shorter now that they have less interruptions. The Flow Load has gone down too, indicating the team is finishing work before starting something new.
And the business is thrilled. Faster resolution times have resulted in higher customer satisfaction and order volumes are ticking up.

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Example

An online trading company has a strategic initiative to implement new tax legislation by January 2022. A key outcome they will be tracking is the response time for key user transactions — how quickly it can perform the State and Federal verifications based on the new tax code — to ensure a good customer experience.

here’s how they’d use their VSM tool:

Product Managers and Product Owners will monitor their Flow Metrics as the new capabilities are gradually (through quarterly releases) introduced to the product.
Their Flow Distribution reveals a pattern: following each release, the value stream is dominated by Defect work for 2-3 weeks (leaving no time for feature development), putting their ability to deliver by January at risk.
Further investigation into the defects reveal they are primarily related to transaction response times, raised by New Relic. Finding these issues so many weeks after the code change is causing rework to take longer.
The Product Managers decide monitoring must ‘shift left’ to discover these issues earlier. They decide to begin using New Relic in test environments during their weekly regression testing.
In addition, to ensure rapid feedback to developers, they set up an integration between New Relic and their Enterprise Agile Planning tool to directly create defects on the product teams’ backlog, so there is no delay in addressing these issues.
The leads now monitor the impact of this change on their Flow Metrics and Flow Distribution. They discover they’ve eliminated 80% of the post-release defects and increase their Flow Velocity by 10%.

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Example

An HRM (Human Resources Management) company sells payroll software for the gig economy. In their highly competitive market, despite user experience being a critical factor in revenue growth, collaboration with the UX design team is often chaotic and can be the source of longer flow times and rework. For example, UX designers will design fantastic experiences that get leadership very excited, but are not technically feasible. As a result, multiple cycles to revise the design (and undo false expectations) are required before implementation can begin.

Because the UX work is not managed in the Enterprise Agile Planning tool, rather manually, it’s hard for the product managers to measure and quantify the impact of the revision cycles on Flow Time, but they sense they are significant.

here’s how they’d use their VSM tool:

They start off by baselining their value stream metrics, including Flow Time — the time elapsed from acceptance/commitment to delivery. Since the UX is not tracked in a tool, naturally UX cycle time will not be included here. The clock will start when the feature is accepted by the value stream.
Next, they conduct an experiment to quantify UX design’s impact on Flow Time: Start managing UX design tasks in the Enterprise Agile Planning tool. As part of this experiment and in order to minimize disruption to the design team for now, the product owner creates and updates the UX design work items on behalf of the designers.
Now they measure the impact of UX design on Flow Time, by starting the clock when the UX design work begins. After a trial period, the product managers are able to show that UX design adds six weeks to Flow Time on average.
In order to shorten Flow Times, the product managers propose an experiment: Embed a UX designer in the product value stream, so they take part in grooming and standup meetings and gain a better understanding of the scope, timelines and technical considerations. The hypothesis is that their subsequent designs are more feasible to implement and require less revision cycles.
Observe the impact of this experiment on Flow Time. If Flow Time is consistently shorter, the experiment is a success. The learnings can be adopted and trialed by other products experiencing similar challenges.
Finally, after proving how important it is to measure the UX design work and how much it improved collaboration and communication when their work was made visible, the case can be made that all UX designers will now manage their work in the Enterprise Agile Planning tool.

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Example

A financial services company has a strategic initiative to implement new tax legislation by January 2022, which will touch a lot of backend code. The regression test automation suite for their core backend services notoriously takes 26 hours to run, so it’s only ever run on the weekend. Someone from QA has to babysit it and frequently intervene to keep it running.

Paying down the debt in this test suite has never been prioritized over revenue-generating feature work until now. However, the VP is becoming increasingly worried: if an emergency update or urgent change is warranted, it couldn’t be done in less than 26 hours. She’s asked the team to make a recommendation on how they can remove this impediment.

here’s how they’d use their VSM tool:

The product, engineering and testing leads for this value stream consult their current Flow Metrics to baseline the current situation and observe the ways the testing constraint is impacting their flow.
They can see that Flow Load (a measure of work in progress), notches up every Monday due to a lot of defects logged over the weekend. The six-month trend shows that load is steadily increasing over time, with defect work overtaking feature work. Developers are having to context-switch more and more, pausing feature work as they tend to defects from week-old code changes.
When analyzing the bottlenecks impacting Flow Time, they see that stories and defects spend an average of 5 days in the ‘Release Certification’ state, as work waits for regression testing.
They also consult their business results which are measured side-by-side with their flow. They are concerned by the particularly low employee NPS score, brought down by the QA members tasked with weekend test-babysitting and engineers frustrated by the constant context-switching.
They work together to design an experiment to reduce the 26 hours down to the maximum time required for a single test to complete, e.g. 15 minutes. This will make it possible to run tests more frequently and get feedback to the engineers faster.
They fire up multiple environments so tests can be run in parallel instead of serially. They record their hypothesis about improving Flow Metrics as well as eNPS while maintaining the same Escaped Defect Ratio.
Based on the success of their experiment, they present their VP with their long term request for technology that can spin up multiple testing environments on-demand. They include data to back up their request: 1) Reduced Flow Time by 2 business days by shortening release certification from 26 hours to 15 minutes. 2) Improved employee happiness by 20% by reducing context switching.
The VP improves the investment in test environments, which is tracked as Debt in the value stream’s flow metrics.

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Example

A telecom provider has an internal system that provides services for all client communications sent by business applications.

The IT organization has recently begun the shift from project to product, and this system is now being treated as an internal product. A product owner and application lead have been assigned, and they’ve begun using a VSM tool to help them measure and improve flow through their complete value stream.

here’s how they’d use their VSM tool:

As the long-term custodians of this internal product, they want to make all the work related to this product visible and measurable against business outcomes. They especially want to be able to prioritize long-neglected Risk work (related to security and compliance) and technical, process and skills Debt.
They attempt to model all the product’s work as part of their value stream: Features, Defects, Risks and Debts. They realize that they’re really struggling to account for security-related work.
Currently, security scans are run by a central security team and the leads meet with a Security Manager once a month to review the list of discovered vulnerabilities, prioritize what will go into the backlog, and manually create a story for it in their Enterprise Agile Planning tool. As a result, a lot of vulnerabilities never get prioritized or addressed in a timely manner.
As a remedy, the leads set up an integration between their application security testing tools and Enterprise Agile Planning tool, which will auto-create a new “Risk” issue type on the backlog for every vulnerability found. The Risk work will be prioritized as part of sprint planning, shortening the time to patch a vulnerability and providing a more accurate picture of the true demand and workload on their teams.
Now, the leads start to get visibility into Risk work. They make sure to allocate at least 10% of their Flow Distribution going forward — and even more initially — in order to work off the accumulated issues.

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Example

An automotive manufacturer has a strategic initiative to introduce a new infotainment system to to their electric vehicle models. The front-end product value stream is worried about timelines, because in the past they’ve felt that testing has been their bottleneck, causing them to release with lower quality than desired. They are wondering whether they should beef up the QA team in anticipation of this new initiative, but budgets are tight.

here’s how they’d use their VSM tool:

The value stream’s product owner and engineering lead look at their Flow Metrics and 6-month trends for Flow Time, Flow Velocity and Flow Efficiency.
To understand what typically slows them down, they examine the data underpinning their current 30% Flow Efficiency. The data reveals a bottleneck in the “Ready for QA” status, indicating QA cannot pull in work at the rate development is completing the work. The bottleneck is indeed manifesting just outside the gates of QA.
To understand whether additional testers would relieve the constraint, they examine their Feature flow and find that on average, each Feature has 10 bugs found in pre-production. QA is getting overwhelmed by basic acceptance criteria issues and the subsequent overhead to retest.
This data points to a code quality issue that wouldn’t be solved by hiring additional testers; rather it’s the requirements definition and acceptance criteria themselves that need to be improved. That can be done with the existing business analyst team they already have, without hiring additional testers.
The value stream constructs an experiment: They will improve the requirements definition and acceptance criteria precision — with peer review — for one month and measure whether this actually reduces the load on QA. They observe whether the work piling up in the Ready for QA status goes down, whether the bug to Feature ratio decreases. If work can flow through QA at a more predictable and even pace, Flow Time should improve too.
When indeed they confirm the improvement in Flow Time, Flow Velocity and Flow Efficiency, they make the change to the requirements management process permanent. If needed they will hire an additional business analyst, but not an additional tester.

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Example

A multi-national company provides systems and components to the automotive industry. It was recently awarded a contract to supply the electric vehicle charger to one of the world’s leading auto manufacturers and — as a result — became subject to new, more stringent regulation. Regulation requires that requirements can be traced to the code that implements them and the tests that verify the functionality.

here’s how they’d use their VSM tool:

During the rush to kick off the new project, the decision is to do the traceability manually. Team members manually cross-reference requirements to stories, tests and defects tracked in other tools. When their client updates a requirement, the team must manually update the requirement and all its related work items in all the tools. When they change the status of a work item, the team must update their client by email.
All the while, the company is measuring flow for the impacted product value streams. Flow Efficiency is quite low, indicating the high-coordination costs and waste in the system.
The Compliance Officer and VP Engineering set out to operationalize a tool for automated traceability within 30 days with a goal of improving efficiency and shortening Flow Times. They identify a vendor and proceed to set up internal integrations between the Requirements Management tool, Enterprise Agile Planning Tool and Test Management Tool that can synchronize data in near real-time and insert hyperlinks between related artifacts automatically. They also set up integrations with their OEM client, such that any update in requirements is reflected in their tools immediately.
Thanks to automated cross-tool traceability, the company has benefited from 98% faster handovers from Requirements and Systems Engineers to Software Developers and Software Testers. These gains are reflected in shorter Flow Times, higher Flow Velocity and much better Flow Efficiency. They’ve also succeeded in minimizing human errors and reducing potential SLA breaches.
When it’s time for an audit, it is very easy for the auditor to trace a requirement through its implementation and testing artifacts.

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VALUE STREAM MANAGEMENT

A New Way to Deliver Value

Every Discipline Has a Super Power

1. Ideate

2. create

3. Release

4. Operate

Enterprise Agile Planning

Service Desk

Product Management

Build & Packaging

Logging, Monitoring & Alerting

Quality Assurance – Testing

User Experience Design

Requirements Management

Quality Assurance – Security

Compliance & Risk Management

Incident Management

Release & Deploy Automation

Application Development

Project & Portfolio Management (PPM)

VALUE STREAM MANAGEMENT

A New Way to Deliver Value

1. Ideate

4. Operate

3. Release

2. Create

Measure

Customer

Optimize

Every Discipline Has a Super Power

1. Ideate

2. create

3. Release

4. Operate

Enterprise Agile Planning

Service Desk

Product Management

Build & Packaging

Logging, Monitoring & Alerting

Quality Assurance – Testing

User Experience Design

Requirements Management

Quality Assurance – Security

Compliance & Risk Management

Incident Management

Release & Deploy Automation

Application Development

Project & Portfolio Management (PPM)

1. Ideate

Project & Portfolio Management (PPM)

DISCIPLINE AND NOTABLE VENDORS:

What Do You Use It For?

how does VSM help the discipline improve?

Example

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Recommended Reading

2. Create

Application Development

DISCIPLINE AND NOTABLE VENDORS:

What Do You Use It For?

how does VSM help the discipline improve?

Example

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Recommended Reading

3. Release

Release & Deploy Automation

DISCIPLINE AND NOTABLE VENDORS:

What Do You Use It For?

how does VSM help the discipline improve?

Example

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Recommended Reading

4. Operate

Service Desk

DISCIPLINE AND NOTABLE VENDORS:

What Do You Use It For?

how does VSM help the discipline improve?

Example

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Recommended Reading

1. Ideate

Enterprise Agile Planning

DISCIPLINE AND NOTABLE VENDORS:

What Do You Use It For?

how does VSM help the discipline improve?

Example

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Recommended Reading

4. Operate

Incident Management

DISCIPLINE AND NOTABLE VENDORS:

What Do You Use It For?

how does VSM help the discipline improve?

Example

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Recommended Reading

1. Ideate

Product Management

DISCIPLINE AND NOTABLE VENDORS:

What Do You Use It For?