Agile 101

In a Lean-Agile environment, we're continuously trying to improve the product development and delivery processes, and, to that end, as William Edwards  Deming once famously quipped: "Without data, you're just another person with an opinion." So, one might imagine that most fair-minded people would be only too happy to inform their judgement by means of empirical evidence (especially since Lean-Agile provides us with so much of it!). But, in truth, when sub-par statistics, metrics or KPIs are presented to an uninitiated audience, the resulting levels of ' Cognitive Dissonance ' and ' Mental Gymnastics ' can sometimes be quite surprising! I n my experience - ignoring complete  'non-sequiturs' - t here is usually a retort which takes the form of 1 of  5 Psychological 'Coping Mechanisms': A claim of Inaccuracy A claim of Unrepresentativeness . A claim of External fault . A claim of Irrelevance . Or a claim of Sheer hopelessne...

In his recent post " Retrospectives using Flow Metrics ", Credit Suisse 's Agile Coach, Christian Hofstetter , offered some interesting insights into applying Dan-Vacanti -style Metrics to tune a "ScrumBan" Team's Processes. The article begins with the Team's Cumulative Flow Diagram (CFD) and then 'drills down' into the data, using visual techniques such as Cycle Time Scatter Plots , Percententiles , and Ageing WIP distributions . I found this stuff fascinating. (After all, I devoured Vacanti's Actionable Agile Metrics for Predictability some years ago and the book really filled in some final, few areas of understanding that I hadn't deduced for myself.) However, my conjecture here is that, from the shape of the graph, the trained eye can see not only the basis for Christian's conclusions but also the explanation as to WHY this new Team might be struggling. (If you haven't seen it already, I'd recommend to you my othe...

Is the Cumulative Flow Diagram the most unloved and misunderstood tool in the Lean-Agile Toolbox? Despite its decorative colours and inherent simplicity, I think it might be. I too often see struggling Developers and Agile Coaches not even noticing that the shape of their CFD is visibly describing to them the effects of team and organisational dysfunction. I'm left to conclude that they don't have a mental model of what "Good" might look like. So let me take you on a little tour, a simplified '3-bin' (3-state) version of the ideal CFD, and help you see what I can see... NB: In these two examples, I shall assume it to be a Stable System ; that the Backlog Items are of equally small size; that the capacity and efficiency of the team remains constant; and that there is a steady, FIFO demand for work the team performs. Defining the 3 states: "New" – Items waiting in the Product Backlog. "Active" – Items being worked upon by th...

Every Scrum Master worth their salt should be able to describe the momentous event that occurred back in 2011AD....   Scrum's "Immutable Rules" changed! In 2011, the Sprint Backlog Items transitioned from a firm " Commitment " t o a mere " Forecast ." " So what? " might be the response. Well, here's the crucial thing: The Sprint Backlog never was that important - satisfying the Sprint Goal was the objective - with the change to Forecasting went the last vestige of excuse for followers of Kanban to decry the inflexibility of Scrum's Sprints. Although, at the time, the intention was primarily to negate the perverse incentives created by Management pressure for completing all the Items in the Sprint Backlog. For Development Teams with such Managers, the only rational response was either to cut corners with the quality of the code (accruing Technical Debt during the Sprint), or to pair extreme pessimism with Par...

We've all been there. Waiting. Waiting. Looking at the queue ahead of us. Waiting. Glancing at our watches. Wondering when we're going to get our turn... In the 1960s, Dr. John Little famously provided the Mathematical proof for the following helpful formula (which later became known as Little's Law). L = ⅄W Where, in this instance: L is the average number of customers at the checkout. W is the average time the customer waits. ⅄   is the average rate of new arrivals. Now, crucially, in a stable system - where no customers are created or destroyed on their way through the checkout process(!) - the Departure rate will equal the Arrival rate. Therefore, in a stable system, we can happily equate these to queuing theory terms. Such that: L ≡ average W.I.P. (Work in Process) W ≡ average Cycle time ⅄ ≡ average Throughput E.g. if there are 5 of us queuing at the checkout, 4 people ahead of us, and the cashier is processing on average 1 person every 2 min...

The naming of this Blog isn't an Orwellian reference (or at least isn't intended to be!), rather more an introductory guide and talking shop for those new to Lean-Agile and its concepts. That said, I do find it amazing how often very established Agilists - many with decades' worth of experience - seem to come to grief all because they don't fully appreciate the basics. I'm here to help. I'm a former Software Developer, a former QA Manager, and now an experienced Scrum Master and Agile Coach. I really like to think that I can 'see things from both sides of the fence' - whether it be: Agile versus Waterfall (or 'JFDI') QA versus Programmer Developer versus Management But I promise you, once you understand the basic tenets of Lean-Agile, it's almost disappointingly simple. "Let me show you! (Even though I'm not your Big Brother!)"