I wrote this a few years ago, but I’m going through a similar agile transformation right now. Although, every agile transformation is different, this still makes sense to me although it is just a draft post. I figured I’d just post it because I never search my drafts for nuggets of knowledge :).
If we are going to do Kanban we shouldn’t waste time formally planning sprints. Just like we don’t want to do huge upfront specifications because of waste caused by unknowns that invalidate specs, we don’t want to spend time planning a sprint because the work being done in the sprint can change anytime the customer wants to reprioritize.
We should have a backlog of prioritized features. The backlog is regularly prioritized (daily, weekly…) to keep features available to work. If we want to deliver a specific set of features or features in two weeks, prioritize them and the team will do those features next.
There is a limit on the number of features the team can have in progress (work in progress or WIP). Features are considered WIP until they pass UAT. Production would be a better target, but saying a feature is WIP until production is a little far fetched if you aren’t practicing “real” continuous delivery. So, for our system, production is considered passing UAT. When the team is under their WIP limit they are free to pull the next feature from highest priority features in the backlog.
This is going to most likely reduce resource utilization, but will increase throughput and improve quality. Managers may take issue at developers not being used at full capacity, but there is a reason for this madness and hopefully I can explain it.
Having features pulled into the pipeline from a prioritized backlog instead planning a sprint allows decisions on what features to be worked to be deferred until the last possible moment. This provides more agility in the flow of work in the pipeline and the product owner is able to respond quickly to optimize the product in production. Isn’t agile what we’re going for?
Pulling work with WIP limits also gives greater risk management. Since batch sizes are smaller, problems will only affect a limited amount of work in progress and risk can be mitigated as new work is introduced in the pipeline. This is especially true if we increase the number of production releases. If every change results in a production release we don’t have to worry about the branch and hotfix dance.
Focusing on a limited amount of work improves the speed at which work is done. There is no context switching and there is a single focus on moving a one or limited amount work items through the system at one time. This increases the flow of work even though there may be times when a developer is idle.
The truth is the system can only flow as fast as its slowest link, the constraint. Having one part of the system run at full capacity and overload the constraint introduces a lot of potential waste in the system. If the idle parts of the system worked to help the bottlenecked part of the system, the entire system improves. So having a full system focus is important.
On my current team, we have constraints that determine how quickly we can turn around a feature. Currently, code review and QA are constraints. QA is the largest constraint that limits faster deployment cycles, but more on that later. To optimize our constraints we could follow the five basic steps outlined in the Theory of Constraints (TOC) from the book The Goal:
- Identify the constraint(s) – in this instance it’s code review and manual testing
- Exploit the constraint to maximize productivity – focus on improvements on the constraint
- Subordinate all other steps or processes to speed up or reduce capacity of the constraint – no new work may enter as WIP until the constraint has WIP available
- Elevate the constraint – prioritize work that helps remove the constraint.
To help with the code review constraint the plan is to have developers do code reviews any time WIP stops the movement of work. With this time developers can dig in and do more thoughtful code reviews and look for ways to refactor and improve the code base. Since we are touching code, why not make recommendations to make the code better. So, we can improve what an acceptable pull request is: good syntax, style, logic, tests… everything we can think of to make the codebase more maintainable and easy to validate.
To remove the QA constraint, the plan focuses on developers creating automated tests to help lessen the work that QA has to do. The reason we don’t first focus on optimizing QA processes directly is because focusing on simply optimizing QA processes would actually increases the capacity for QA without increasing the speed at which we can flow work to production. We don’t want to increase the number of features that QA can handle because it is important to take the proper time in testing. What we want to do is remove manual regression checks for QA. Exploiting QA for us means increasing QAs effectiveness freeing up time to do actual testing instead of just following a regression script. Having developers automate regression opens us up to deliver new features to production faster because automation runs these test much faster than QA. QA can focus on what they do best, testing and not running mundane scripted checks. Trick here is how do we convince developers to write automated tests without causing a revolt.
In summary, we would have to wait for a manual regression test cycle to occur and couldn’t introduce new work because it would invalidate the regression test. With automation handling +80% of regression QA can move faster, actually test more, and we can not only increase throughput through the entire system, but the overall quality of the product is also increased.
Monitoring Delivery Pipeline
We track work through the delivery pipeline as features. A feature in this sense is any change, new function, change existing function, or to fix a defect. Features are requested on features kept in a central database. We monitor the delivery pipeline by measuring:
- Lead Time
- Quantity: Unit of Production
- Production Rate
Inventory (V) is any work that has not been delivered to the customer. This is the same as work in progress (WIP). This counts all work from the backlog to a release awaiting production deployment. Whenever there is undelivered work and we have to cancel the work for some reason, we considered it an Operational Expense. Canceled work won’t be delivered to production because of defect, incorrect specs, the customer pivoted or otherwise doesn’t want it. Cancelled work is wasted effort and in some cases can also cause expensive un-budgeted rework. In traditional cost accounting inventory is seen as an asset, but in TOC it is a potential Operational Expense if it is not eventually delivered to customer so turning inventory as fast as possible without injecting defects is a goal.
Quantity (Q) is the total number of units that have moved through our delivery pipeline. Our unit of production is a feature. When a feature is deployed to production we can increase quantity by one unit. A feature is still considered inventory until it has been delivered to the customer in production. If a customer decides they don’t want the feature or some other reason to stop the deployment of the feature, it is counted as an Operational Expense and not quantity.
Flow time (FT) is the time it takes to move a feature, one unit, from submission to the backlog to deployed to a customer in production.
Production rate (PR) is the number of units delivered during a time period. This is the same as throughput. If we we deliver 3 features to production in a month our production rate is 3 features per month.
Optimize Delivery Pipeline for Flow Time
We should strive to optimize the delivery pipeline for flow time instead of production rate or throughput. The Theory Of Constraints – Productivity Metrics in Software Development posted on lostechies.com explains this well.
Let’s say our current flow time (FT) is 1 unit (Q) in a week or a production rate (PR) of 4 Q per month. If we optimize FT to 1 Q in 3 days, we will see a jump in PR to 6.67 Q per month or a 59% increase.
If we focus on optimizing PR, we may still see improvement in FT, but it can also lead to only an increase in inventory as WIP increases. The PR optimization may increase Q that is undeliverable because of some bottleneck in our system so the Q sits as inventory, ironically in a queue. The longer a feature sits in inventory the more it costs to move it through the pipeline and address any issues found in later stages of the pipeline. So, old inventory can also cause delay down stream as the team must take time to ramp up to address issues after they have moved on to another task.
So, to make sure we are optimizing for FT we focus on reducing waste or inventory in the pipeline by reducing WIP. The delivery team keeps a single purposed focused on one unit or a limited amount of work in progress to deliver what the customer needs right now, based on priority in the backlog. Reducing inventory reduces Operation Expense. (Excuse me if I am allowing some lean thinking into this TOC explanation)
Investment (I) is the total cost invested in the pipeline. In our case we will count this as time invested. We can sum the time invested on each unit in inventory in the pipeline to see how much is invested in WIP. We could count hours in timecards to determine this, but time cards are an evil construct. If we are good about moving cards, or even automated movement of cards based on some event (branch created, PR submitted, PR approved…), we could assign the time a card sits in some state to a standard investment amount in the time it sat. I’m still pondering this, but I feel like time investment based on card movement is way better than logging time.
Operating expense (OE) is the cost of taking an idea and developing it to a deliverable. This is not to be confused with operational expense which is a loss in inventory or loss in investment. Any expense, variable or fixed, that is a cost to deliver a unit is considered OE. We will just use salaries of not only developers, but BA, QA, IT as our OE. Not sure how we will divide up our fixed salaries, maybe a function that includes time and investment. Investment would be a fraction of OE because all of a developers time is not invested in delivering features (still learning).
Throughput (T) in this sense is the amount earned per unit. Traditionally, this is that same as production rate as explained earlier, but in terms of cost, we calculate throughput by taking the amount earned on production rate, features delivered to production, minus the cost of delivering the features or the investment.
To maximize ROI and net profit (NP) we need to increase T while decreasing I and OE.
NP = (T – OE)
ROI = NP/ I
Average Cost Per Feature
Average cost per feature (ACPF) is the average amount spent in the pipeline to create a feature.
ACPF = OE/Q
There are more metrics that we can gather, monitor, and analyze; but we will keep it simple for now and learn to crawl first.
Average Lead Time Per Feature
The average time it takes to move a feature from the backlog to production. We also calculate the standard deviation to get a sense on how varying work sizes in the pipeline affects lead time.
Bonus: Estimating Becomes Easier
When we begin to monitor our pipeline with these metrics estimating becomes simpler. Instead of estimating based on time we switch to estimating based on size of feature. Since we are tracking work, we have a history to base our future size estimates on.
Issues in Transformation
Our current Q is a release, a group of features that have been grouped together for a deployment. We will build up an inventory of features over a month at times before they are delivered to production. This causes an increase in inventory. It would be better to use a feature instead of a release as our Q. When a feature is ready, deliver it. This reduces inventory and increase the speed at which we get feedback.
To change our unit, Q, to feature we have to attack our largest constraint, QA. Currently, we have to sit on features or build up inventory to get enough to justify a QA test cycle. We don’t want to force a two week regression on one feature that took a couple days to complete. So, reducing the test cycle is paramount with this approach.
- The Goal: A Process of Ongoing Improvement, by Eliyahu M. Goldratt