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Tag: Batch Size

Continuous Delivery and the Theory Of Constraints

How should you actually implement Continuous Delivery?

Adopting Continuous Delivery takes time. You have a long list of technology and organisational changes to consider. You have to work within the unique circumstances of your organisation. You’re constantly surrounded by strange problems, half-baked theories, off the shelf solutions that just don’t work, and people telling you Amazon is nothing to worry about

How do you identify and remove the major impediments in your build, testing, and operational activities? How do you avoid spending weeks, months, or years on far-reaching changes that ultimately have no impact on your time to market?


  • Continuous Delivery means applying technology and organisational changes to the unique circumstances of an organisation
  • If a Continuous Delivery programme does not focus on the activities with the most rework and/or queue times, there is a high probability of sub-optimal outcomes
  • The Theory Of Constraints is a management paradigm for improving organisational throughput, while simultaneously decreasing both inventory and operating expense.
  • The Theory Of Constraints can be applied to Continuous Delivery, as the build, testing, and operational activities in a technology value stream should be homogeneous
  • The Five Focussing Steps can be used to identify constrained activities, and then introduce the necessary technology and organisational changes to reduce rework and/or queue times


Technology can bring benefits if, and only if, it diminishes a limitation – Dr. Eli Goldratt

An organisation will have one or more technology value streams. Each one will represent a sequence of activities that converts business ideas into value-adding software for customers. The first part of a technology value stream will be design and development activities, which are inherently non-deterministic and highly variable. The second part will be build, testing, and operational activities, which should be as deterministic and invariable as possible. When an organisation lacks the necessary stability and throughput in its technology value streams to satisfy market demand, it is in a state of Discontinuous Delivery.

Continuous Delivery is a set of holistic principles and practices to improve the stability and throughput of IT delivery. It needs a substantial investment in technology changes:

  • Version Control: put code, configuration, infrastructure definitions, migration scripts, etc. in version control to preserve change history
  • Environments: automate self-service, on-demand provisioning of short-lived test environments and incremental release patterns such as Canary Deployments
  • Development: use Test Driven Development with Pair-Programming to build quality into a codebase, and use Trunk Based Development with Continuous Integration to ensure it is always releasable
  • Architecture: establish an Evolutionary Architecture to encourage loosely-coupled, independently deployable services
  • Testing: automate parallelisable acceptance tests with dynamic test data, use Smoke Testing to validate deployments, and use Exploratory Testing to uncover new information
  • Operability: aggregate logs and metrics to create a centralised telemetry platform for visualising normal conditions, and alerting on abnormal conditions

It also requires a significant amount of organisational changes:

  • Batch Size: reduce features per release candidate to decrease variability, feedback delays, risk, overheads, inefficiencies, and lead time via Little’s Law
  • Management: devolve decision making to the employees closest to the work to empower better choices in design, testing etc.
  • Culture: grow a performance-oriented culture of cooperation and collaboration to increase information flow between teams
  • Responsibilities: change responsibilities for testing and on-call production support to encourage a shared sense of accountability
  • Risk: use continuous code reviews via Pair-Programming and traceability of production changes to replace Risk Management Theatre with adaptive risk assessment
  • Skills: invest in employee training to ease the introduction of new practices and tools e.g. infrastructure automation
  • Structures: convert siloed functional teams into cross-functional teams, and align team and software architecture with Conway’s Law to enable faster delivery

These changes are challenging in isolation, but it is their application to the unique circumstances and constraints of an organisation that makes Continuous Delivery so difficult. Adopting Continuous Delivery means implementing widespread changes in the highly uncertain conditions of a complex, adaptive system, in which behaviours are emergent and interactions are unpredictable.

A Continuous Delivery programme should aim to reduce rework and queue times in a technology value stream, as they are the most common sources of waste. Rework is any activity that must be repeated due to a failure, such as a tester being given a defective release candidate. Queue time is time spent queuing for an activity, such as a deployment waiting for a tester to become available. There are many potential causes of rework and queue time, including the following:

Cause Description
Snowflake environments Manual environment provisioning
Brittle deployments Unreliable code, config, or infra changes
Environment contention Slow access to test data, services, or environments
End-To-End Testing Reams of slow, non-deterministic tests
Rigid architecture Application deployments coupled together
Excessive toil Manual, repetitive build/testing/operations tasks

Over the years, the advice on how to implement Continuous Delivery has evolved. The Continuous Delivery book suggested using the Plan-Do-Check-Act Cycle to experiment with changes, and implement a maturity model. Lean Enterprise recommended the Improvement Kata be used to create a Continuous Improvement framework, with Plan-Do-Check-Act used to structure different experiments. This can be very effective, but there needs to be a way to focus on the activities where a reduction in rework and/or queue times will have an immediate, positive impact. Otherwise, there is a high probability of sub-optimal outcomes, stakeholder dissatisfaction, and Discontinuous Delivery remaining the norm.

Given sufficient Continuous Delivery expertise, heuristics can be used to locate those activities in a technology tech value stream. An alternative method is needed when those heuristics are unavailable, or insufficient to overcome resistance to change.

Example – Network Activation

Think of an 18 month Continuous Delivery programme for a multi-team, multi-application Network Activation platform. The delivery teams already use Pair-Programming, Test-Driven Development, Trunk Based Development, and Continuous Integration. The on-prem technology value stream has 2 pre-production activities – Functional Test and Integration Test – and a production deployment involves 10 semi-automated and manual tasks. Problems include snowflake environments, brittle deployments, and End-To-End Testing. There is no data on deployments, beyond an average lead time to production of 34 days. The product owner wants it to be 14 days.

Over 18 months there is a concerted effort to create a consistent deployment pipeline, with the Cost of Delay used to prioritise pipeline features. Aggregate Artifacts and Artifact Containers are used to automate deployments, infrastructure provisioning errors are eliminated, and pipeline dashboards are built. Verify Branch By Abstraction is used to incrementally decouple applications, by replacing object serialisation with RESTful APIs. API Examples and Consumer Driven Contracts are used to verify application interactions. There are also a series of organisational changes to policies, processes, and teams, to reduce queue time for Functional Test and Integration Test.

These changes produce many benefits, including a 95% build time improvement from 1 hour to 3 minutes, and a 43% improvement in test deployment time from 3.5 hours to 2.5 hours. However, there is no impact whatsoever on the average lead time of 34 days. On that basis, the Continuous Delivery programme is unsuccessful.

The Theory Of Constraints

In The Goal, Dr. Eli Goldratt sets out the Theory Of Constraints. The Theory Of Constraints is a management paradigm for improving organisational throughput, by optimising a small number of constraints in a homogeneous workflow. It states the goal of an organisation is to make money, by increasing throughput while simultaneously decreasing both inventory and operating expense.

A constraint is any resource with capacity equal to or less than market demand, and it will limit the throughput of the organisation. A constraint is internal when market demand is greater than throughput, and external when throughput is greater than market demand. An hour lost at a constraint is an hour lost for the whole organisation.

A non-constraint is any resource with capacity greater than market demand. The level of utilisation of a non-constraint is determined by a constraint elsewhere. An increase in non-constraint capacity is a waste of time and money, as it can only increase queued work before a constraint and cannot reduce work starvation after a constraint. An hour lost at a non-constraint is an illusion.

The central premise of the Theory Of Constraints is to iteratively increase the capacity of a constraint, until the flow of work items can be balanced according to market demand. The Five Focussing Steps are used to achieve this:

  1. Identify the constraint(s): calculate market demand for a product, and which activities have capacity less than or equal to demand
  2. Exploit the constraint(s): increase constraint utilisation by eliminating idle time, and time wasted on lower priority and/or defective work items
  3. Subordinate everything else to the constraint(s): protect a constraint from excessive inventory or work starvation by maintaining a buffer of materials, and regulating the arrival of new work items into the system based on constraint capacity
  4. Elevate the constraint(s): offload constraint work items to a non-constraint by investing in additional equipment, people, and/or allocation of work items to third parties
  5. If a constraint has been broken, go back to step 1, but do not allow inertia to cause a constraint

Continuous Delivery and the Theory Of Constraints

The Theory Of Constraints can be applied to Continuous Delivery, as the build, testing, and operations activities in a technology value stream should be homogeneous. There will be one, or at most a few constrained activities, caused by rework and/or queue times. The Five Focussing Steps can be used to identify and optimise the constraint(s), until the flow of release candidates is balanced from mainline commit to production and Continuous Delivery is achieved. At that point, an external constraint will emerge upstream in product development or downstream in sales. If market demand increases later on, a new internal constraint might surface within the technology value stream.

Identify the constraint

A constraint is identified by establishing the market demand for a product, and then calculating how much time each activity contributes to meeting that demand. Overall market demand should be specified by the product owner as a target Lead Time lt, and measured from mainline commit to production launch.

lt = X minutes/hours/days

The ability of an activity a n to meet lt should be expressed as its Activity Time at n. It can be measured as the median between its finish time aft n and the finish time of the prior activity aft n – 1, for all occurrences of a n in a time period t. This measurement ensures queue time and process time are both accounted for. The measurement unit should be minutes, hours, or days based on lt. It might be necessary to measure variability in Activity Time as well, if one or more activities exhibit an undesirable amount of variability.

at n = median ( ( aft n - aft n - 1 ) for a n in t )

If an activity has at n greater than lt, it is a constraint. If a high percentage of release candidates consistently fail to pass the activity on the first attempt, it is unstable and rework must be reduced. Alternatively, if release candidates are regularly delayed before starting the activity, it is too slow to begin and queue time must be reduced.

If no activity has at n greater than lt, there is no internal constraint. This means lt is not ambitious enough, or there is an external constraint outside the technology value stream.

Applying the Theory Of Constraints to the Network Activation platform with its target lead time of 14 days reveals a constraint. Visualising each activity as queue time and process time shows Production queue time was often greater than 14 days. This was caused by a well-intentioned release management policy of deploying release candidates into production when ready, and then waiting for a launch date pre-agreed with an upstream billing provider months earlier. Launch dates were delayed when testing finished late, but never brought forward when testing finished early. 18 months of hard work reduced variability, Functional Test queue time, and Integration Test queue time, but those improvements merely led to an increase in Production queue time until launch dates occurred.

Optimise the constraint

A constraint is optimised by experimenting with the full range of technology and organisational changes recommended by Continuous Delivery. There needs to be a concerted effort to reduce the average and variation in Activity Time for the constrained activity, until the target Lead Time can be satisfied.

Exploiting a constrained activity means ensuring it is always working, and always doing valuable work. This aligns with Continuous Delivery emphasising the automation of repetitive tasks to free up people for higher-order problems. Automated infrastructure provisioning, automated unit and acceptance testing, and moving to an Evolutionary Architecture are all examples of technology changes to reduce time spent at a constrained activity. The most effective organisational change is to reduce batch sizes, as a smaller batch will have shorter process times and queue times.

Subordinating unconstrained activities means limiting the flow of mainline commits, builds, and deployments to match the constrained activity. This is best accomplished with Work In Process (WIP) Limits, which encourage people to collaborate on a few work items at any point in time. Stop The Line teaches people to prioritise a releasable codebase over developing more features, and eXtreme Programming practices such as Test-Driven Development and Pair-Programming can also foster a shared team cadence.

Elevating a constrained activity means investing in people and tools to increase its capacity. This might be achieved with technology changes, such as a move to short-lived test environments via fully automated infrastructure provisioning. It might also involve organisational changes such as paying third party suppliers for more test data, hiring more engineers, or running training courses to improve Staff Liquidity.

For instance, an assortment of changes should be tried if End-To-End Testing activity is a constraint. Time wasted should be eliminated by ensuring testers are uninterruptible, making more testers available, and running testing slots 24 hours a day. Defective release candidates should be minimised by moving all other testing activities in front of End-To-End Testing, adding automated contract tests, and rejecting release candidates with one or more test failures. Over time the end-to-end tests should be incrementally replaced with automated acceptance tests, monitoring dashboards, and automated anomaly detection.

Example: MediaTech

Consider a 7 month Continuous Delivery programme for a multi-team, multi-application MediaTech platform. The delivery teams use Trunk Based Development with some Pair Programming, but there is little Test-Driven Development. The on-prem technology value stream has 2 pre-production activities – Functional Test and Release Test – and a production deployment involves 60 semi-automated and manual tasks. Problems include snowflake environments, brittle deployments, environment contention, a rigid architecture, End-To-End Testing, and excessive toil. There is no data on deployments, beyond an average interval of 3 weeks. The product owner has set a target lead time of 1 week, at an interval of 1 week.

The range of problems at MediaTech makes Continuous Delivery very challenging. There is a proposal to create a fully automated deployment pipeline, with infrastructure provisioning, deployments, and telemetry. Other suggested options include the promotion of Test-Driven Development, implementing zero downtime deployments, and immutable release candidates. However, there are concerns about the time needed to make such changes.

After 2 months, deployment data is scraped from chat channels, and applying the Five Focussing Steps shows sweeping changes are not immediately required. The constraint on a target lead time of 1 week is Release Test process time, due to perennial environment instability caused by configuration and test data issues. Interestingly, there is no obvious constraint on a target lead time of 2 weeks. As a result, the product owner agrees to a target lead time of 2 weeks at an interval of 2 weeks, as an intermediate milestone.

In the Theory Of Constraints, a lack of skilled people can constitute a constraint. When the above data is shared with the MediaTech operations team, they reveal an unseen constraint on the 14 day target lead time. There is a single release planner, who endures a heavy workload for each production deployment on top of their day to day work. Everyone agrees the Continuous Delivery programme needs to focus on automating the release planning tasks, and improving the existing automated tests.

Over the next few months, the release planning workload is reduced and the automated tests are stabilised. Callout rota emails by the release planner are replaced by a shared rota, co-owned by the delivery teams. A release note process overseen by the release planner involving 3 different teams is replaced by a fully automated release note tool. Furthermore, end-to-end tests in Integration Test and Release Test are prioritised by their non-determinism, and gradually rewritten as build-time acceptance tests. The target lead time of 2 weeks at an interval of 2 weeks is successfully achieved, and after several production deployments the product owner decides the new throughput is sufficient. The Continuous Delivery programme is considered a success, and without a fully automated deployment pipeline.

Further Reading

  1. Beyond The Goal by Dr. Eli Goldratt
  2. Measuring Continuous Delivery by the author
  3. Resilience as a Continuous Delivery Enabler by the author


Thanks to Thierry de Pauw for his feedback on this article.

More releases with less risk

Continuous Delivery reduces defect probability and cost

Continuous Delivery often challenges conventional wisdom within the IT industry, and by advocating the rapid release of value-add to reduce risk it contradicts the traditional belief that a low release cadence is an effective risk reduction strategy. How can releasing software more frequently reduce both defect probability and defect cost?

The probability of a defect is the likelihood of a change within a changeset unexpectedly impeding value-add and imposing an opportunity cost. Given the defect probability of a changeset is proportional to its size we can calculate the defect probability of a change as follows:

Fix More With Less - Defect Probability

n = number of changesets
probability = (1 / 2n) * 100 [percentage]

The above formula indicates that decreasing changeset size by increasing the number of changesets will reduce defect probability, and this is confirmed by Don Reinertsen’s assertion that “many smaller experiments produce less variation than one big one“. For example, if a change is released in 1 changeset there is a 1 in 2 chance or 50% probability of failure. If it was instead released in 3 changesets there would be a 1 in 8 chance or 12.5% probability of failure.

The cost of a defect is the product of cost per unit time and duration, where cost per unit time represents economic impact and duration represents lifetime.

cost = cost per unit time [currency] * duration [unit time]

A defect has an inception date at its outset, a discovery date when diagnosed, and a resolution date when fixed. The interactions between these dates and cost per unit time enable a division of defect cost into sunk cost and opportunity cost. The sunk cost of a defect represents the economic damage already incurred at the point of discovery, while opportunity cost represents the economic damage still to be incurred.

Fix More With Less - Defect Cost

sunk cost duration = discovery date – inception date [unit time]
sunk cost = cost per unit time * sunk cost duration [currency]

opportunity cost duration = resolution date – discovery date [unit time]
opportunity cost = cost per unit time * opportunity cost duration [currency]

cost = sunk cost + opportunity cost [currency]

As cost per unit time is controlled by market conditions it is far easier to reduce opportunity cost duration by shortening lead times. This can be accomplished via batch size reduction, as Mary and Tom Poppendieck have observed that “time through the system is directly proportional to the amount of work-in-process” due to Little’s Law:

lead time = work in progress [units] / completion rate [units per time period]

Little’s Law is universal for all stable systems in which these variables are consistent long-term averages, and it is mathematical proof that reducing batch size will reduce lead time. For example, if a jug contains 4 litres of water and pours 2 litres per second then it will empty in 2 seconds. If instead the jug contained 2 litres of water and still poured 2 litres per second it would empty in 1 second.

Releasing smaller changesets more frequently into production can also reduce sunk cost duration, as small batches accelerate feedback. A smaller batch size will decrease the lead time and complexity associated with each changeset, creating faster feedback loops that will reduce the time required to discover a defect.

Consider an organisation with an average changeset size of 24 changes and an average lead time of 12 days. How can we reduce the defect probability of the next production release R1?

Fix More With Less - Defect Probability Smaller Changeset

n = 1
probability = (1 / 21) * 100 = 50%

Based on the binomial probabilities involved we recommend to the organisation that it reduce defect probability by applying batch size reduction to R1 and splitting its changeset into 2 smaller releases R1 and R2. This would decrease defect probability from 50% to 25%.

Fix More With Less - Defect Probability Larger Changeset

n = 2
probability = (1 / 22) * 100 = 25%

Unfortunately the organisation ignores our advice to release smaller changesets, and the release of R1 at a later date introduces a defect D1 that remains undiscovered for 6 days. D1 impedes a sufficient amount of value-add that a cost per unit time of £20,000 per day is estimated, which means a sunk cost of £120,000 has already been incurred and an opportunity cost of £240,000 is forecast. The organisation immediately triages D1 for a fix, but how can we reduce its opportunity cost?

Fix More With Less - Defect Cost Large

cost per unit time = £20,000
sunk cost = 6 days * £20,000 = £120,000
opportunity cost = 12 days * £20,000 = £240,000
overall cost = sunk cost + opportunity cost = £360,000

Given the organisation currently has an average batch size of 24 changes per changeset and a 12 day average lead time, Little’s Law computes an average completion rate of 2 changes per day and informs us that a reduced batch size of 12 changes per changeset would produce a 6 day lead time.

completion rate = work in process / lead time
completion rate = 24 changes per changeset / 12 days = 2 changes per day

lead time = work in process / completion rate
lead time = 12 changes per changeset / 2 changes per day = 6 days

Based on Little’s Law we again recommend to the organisation a halved batch size of 12 changes per changeset, and this time our advice is accepted. A fix for D1 is included in the next changeset released into production in 6 days, which produces an opportunity cost saving of £120,000.

Fix More With Less - Defect Cost Smaller Opportunity Cost

cost per unit time = £20,000
sunk cost = 6 days * £20,000 = £120,000
opportunity cost = 6 days * £20,000 = £120,000
overall cost = sunk cost + opportunity cost = £240,000

As well as decreasing the total cost of D1 by 33%, the new lead time of 6 days increases the rate of feedback for future production defects. When a subsequent release introduces defect D2 at a lower cost per unit time of £10,000 per day the reduced size and complexity of the offending changeset means D2 is discovered in only 3 days.

Fix More With Less - Defect Cost Smaller Sunk Cost

cost per unit time = £10,000
sunk cost = 3 days * £10,000 = £30,000
opportunity cost = 6 days * £10,000 = £60,000
overall cost = sunk cost + opportunity cost = £90,000

When we triage D2 we discover its cost per unit time has decreased to £1,000 per day, meaning its sunk cost is a poor indicator of opportunity cost and its Cost of Delay is lower than expected. Based upon the new 6 day lead time we recommend to the organisation that it defer a D2 fix for at least one release in order to implement pending value-add of greater value than the £12,000 opportunity cost of D2.

Fix More With Less - Defect Cost Even Smaller Opportunity Cost

cost per unit time = 3 days * £10,000, 12 days * £1,000
sunk cost = 3 days * £10,000 = £30,000
opportunity cost = 12 days * £1,000 = £12,000
overall cost = sunk cost + opportunity cost = £42,000

The assumption within many IT organisations that risk is directly proportional to rate of change is flawed, as it assumes a constant large batch size. Risk is actually proportional to size of change, and a low release cadence of large changesets is not as effective a risk reduction strategy as a high release cadence of small changesets. Continuous Delivery enables the release of smaller changesets to rapidly release value-add as well as reducing both the probability and cost of defects.

No Projects

Projects kill flow and teams. Focus on products, not projects

Since the Dawn of Computer Time, enormous sums of money and embarrassing amounts of time have been squandered upon software projects that have delivered little or no return on investment, with projects floundering between segregated Business and IT divisions squabbling over overestimated value-add and underestimated delivery dates. Given Grant Rule’s assertion that “studies too numerous to mention show that software projects are challenged or fail“, why are software projects so prone to failure and why do they persist?

To answer these questions, we must understand what constitutes a software project and why its delivery model is incongruent with product development. If we start with the PRINCE 2 project definition of “a temporary organization that is needed to produce a unique and predefined outcome or result at a pre-specified time using predetermined resources“, we can offer a concise definition as follows:

A project is a fixed amount of time and money assigned to deliver value-add

The key characteristic of a software project appears to be its fixed end date, which as a delivery model has been repeatedly debunked by IT practitioners such as Allan Kelly denouncing “endless, pointless discussions about when it will be done… successful software doesn’t have a pre-specified end date” and Marc Lankhorst arguing that “over 80% of IT spending in large organisations is on maintenance“. However, the fixed end date of a software project is invariably a consequence of its requirement for a collection of value-adding features to be simultaneously delivered, suggesting an augmented definition of:

A project is a fixed amount of time and money assigned to deliver a large batch of value-add

Once we view software projects as large batches of value-add, we can apply The Principles Of Product Development Flow by Don Reinertsen and better understand why so many projects fail:

  1. Increased cycle time – a project might not be deliverable on a particular date unless either demand is throttled or capacity is increased, e.g. artifically reduce user demand or increase staffing levels
  2. Increased variability – a project might be delayed due to unpredictable blockages in the value stream, e.g. testing of features B and C blocked while testing of feature A takes longer than expected
  3. Increased feedback delays – a project might incur significant costs due to slow feedback on bad design decisions and/or defects increasing rework, e.g. failures in feature C not detected until features A and B have passed testing
  4. Increased risk – a project might have an increased probability and cost of failure due to increased requirements/technology change, increased variation, and increased feedback delays
  5. Increased overheads  – a project might endure development inefficiencies due to increased requirements/technology change, e.g. feature C development time increased by need to understand complexity of features A and B
  6. Increased inefficiencies – a project might encounter increased transaction costs due to increased requirements/technology change e.g. feature A slow to release as features B and C also required for release
  7. Increased irresponsibility – a project might suffer from diluted responsibilities, e.g. staff member has responsibility for delivery of feature A but is unincentivised to participate in delivery of features B or C

Don also provides a compelling explanation as to why the project delivery model remains prevalent, by explaining how large batches can become institutionalised as they “appear to have scale economies that increase efficiency [and] appear to reduce variability“. Software projects might indeed appear to be efficient due to perceived value stream inefficiencies and the counter-intuitiveness of batch size reduction, but from a product development standpoint it is an inefficient, ineffective delivery model that impedes value, quality, and flow.

There is a compelling alternative to the project delivery model – product development flow, in which we apply economic theory to Lean product development practices in order to flow product designs through our organisation. Product development flow emphasises the benefits of batch size reduction and encourages a one piece continuous flow delivery model, in order to reduce costs and improve return on investment.

Discarding the project delivery model in favour of product development flow requires an entirely different mindset, as epitomised by Grant urging us to “accommodate the ideas of flow production and lean systems thinking” and Allan affirming that “BAU isn’t a dirty word… enhancing products is Business As Usual, we should be proud of that“. On that basis the No Projects movement was conceived by Joshua Arnold to promote the valuation of products over projects, and anointed as:

Projects kill flow and teams. Focus on products, not projects

Release more with less

Continuous Delivery enables batch size reduction

Continuous Delivery aims to overcome the large delivery costs traditionally associated with releasing software, and in The Principles of Product Development Flow Don Reinertsen describes delivery cost as a function of transaction cost and holding cost. While transaction costs are incurred by releasing a product increment, holding costs are incurred by not releasing a product increment and are proportional to batch size – the quantity of in flight value-adding features, and the unit of work within a value stream.

Economic Batch Size [Reinertsen]

The above graph shows that a reduction in transaction cost alone will not dramatically impact delivery cost without a corresponding reduction in batch size, and this mirrors our assertion that automation alone cannot improve cycle time. However, Don also states that “the primary controllable factor that enables small batches is low transaction cost per batch“, and by implementing Continuous Delivery we can minimise transaction costs and subsequently release smaller change sets more frequently, obtaining the following benefits:

  1. Improved cycle time – smaller change sets reduce queued work (e.g. pending deployments), and due to Little’s Law cycle time is decreased without constraining demand (e.g. fewer deployments) or increasing capacity (e.g. more deployment staff)
  2. Improved flow – smaller change sets reduce the probability of unpredictable, costly value stream blockages (e.g. multiple deployments awaiting signoff)
  3. Improved feedback – smaller change sets shrink customer feedback loops, enabling product development to be guided by Validated Learning (e.g. measure revenue impact of new user interface)
  4. Improved risk – smaller change sets reduce the quantity of modified code in each release, decreasing both defect probability (i.e. less code to misbehave) and defect cost (i.e. less complex code to debug and fix)
  5. Improved overheads – smaller change sets reduce transaction costs by encouraging optimisations, with more frequent releases necessitating faster tooling (e.g. multi-core processors for Continuous Integration) and streamlined processes (e.g. enterprise-grade test automation)
  6. Improved efficiency – smaller change sets reduce waste by narrowing defect feedback loops, and decreasing the probability of defective code harming value-adding features (e.g. user interface change dependent upon defective API call)
  7. Improved ownership – smaller change sets reduce the diluted sense of responsibility in large releases, increasing emotional investment by limiting change owners (e.g. single developer responsible for change set, feedback in days not weeks)

Despite the business-facing value proposition of Continuous Delivery, there may be no incentive from the business team to increase release cadence. However, the benefits of releasing smaller change sets more frequently – improved feedback, risk, overheards, efficiency, and ownership – are also operationally advantageous, and this should be viewed as an opportunity to educate those unaware of the power of batch size reduction. Such a scenario is similar to the growth of Continuous Integration a decade ago, when the operational benefits of frequently integrating smaller code changes overcame the lack of business incentive to increase the rate of source code integration.

Business requirements = minimum release cadence
Operational requirements = maximum release cadence

A persistent problem with increasing release cadence is Eric Ries’ assertion that “the benefits of small batches are counter-intuitive“, and in organisations long accustomed to a high delivery cost it seems only natural to artificially constrain demand or increase capacity to smooth the value stream. For example, our organisation has a 28 day cycle time of which 7 days are earmarked for release testing. In this situation, decreasing cadence to a 36 day cycle time appears less costly than increasing cadence to a 14 day cycle time, as release testing will ostensibly decrease to 19% of our cycle time rather than increase to 50%. However, this ignores both the holding cost of constraining demand and the long-unimplemented optimisations we would be compelled to introduce to achieve a lower release cadence (e.g. increased level of test automation).

Improving cycle time is not just about using Continuous Delivery to reduce transaction costs – we must also be courageous, and release more with less.

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