{"id":7902,"date":"2026-08-20T10:34:34","date_gmt":"2026-08-20T08:34:34","guid":{"rendered":"https:\/\/getnave.com\/blog\/?p=7902"},"modified":"2026-08-20T10:34:34","modified_gmt":"2026-08-20T08:34:34","slug":"improve-delivery-system-one-measurable-change","status":"publish","type":"post","link":"https:\/\/getnave.com\/blog\/improve-delivery-system-one-measurable-change\/","title":{"rendered":"How to Improve Your Delivery System One Measurable Change at a Time"},"content":{"rendered":"
<\/div>\n

Weeks or months into a transformation, performance is worse than when you started.<\/p>\n

Leadership approved the transformation because they wanted better performance. Instead, delivery is slowing down. The numbers are moving in the wrong direction. And the improvements everyone was promised have not arrived yet.<\/p>\n

How long will leadership tolerate that? How long before people start questioning the transformation? Or the people leading it?<\/p>\n

This is one of the biggest problems I have with transformation plans that try to change everything at once. You introduce new structures, new roles, new processes, new tools and new ways of working. Eventually, those changes may produce better results. But first, the organization has to learn how to operate inside an entirely new system. And during that transition, performance often gets worse before it gets better.<\/p>\n

Why Performance Gets Worse Before It Gets Better<\/h2>\n

There is a useful way to think about this called the J-curve. When you introduce a significant change, performance doesn’t immediately go up. It often goes down first.<\/p>\n

People are learning. Old ways of working have been disrupted. New ones have not become natural yet. Teams are figuring out how responsibilities have changed and how decisions should now be made. Only after that adjustment does performance begin to recover and, hopefully, move beyond where it started.<\/p>\n

\"The<\/a>
\nThe J-curve effect. Performance falls below the current status quo before the new system settles, then climbs to a new one above it. Safety is how far performance is allowed to fall before the transformation is in trouble. Tolerance is how long the organization will wait for the recovery. A big-bang transformation survives only if the dip stays inside both, and almost nobody measures either one before they start. Learn more at getnave.com<\/a>.<\/em><\/p>\n

The larger the transformation, the more change you introduce at once and the harder it becomes to understand what is actually producing the results you’re seeing.<\/p>\n

This is why I’ve never been a big believer in transformation plans that change everything at once. I’d rather start with what you already do today. Find one thing that is getting in the way of better performance. Change it. Measure what happens. Then decide what to change next.<\/p>\n

The 1% Strategy That Transformed British Cycling<\/h2>\n

One of my favorite examples of this way of thinking comes from a completely different world. British Cycling.<\/p>\n

For almost 100 years, British cyclists had won just one Olympic gold medal. Then Dave Brailsford took over and introduced an approach that became known as the aggregation of marginal gains. The idea was simple: instead of searching for one enormous improvement, find many small opportunities to get slightly better.<\/p>\n

They looked at the obvious things, such as equipment and training. But they kept going. They experimented with the seats on the bikes. They looked at what riders slept on and transported mattresses between hotels so athletes could sleep consistently. They painted the inside of the team truck white so dust and dirt that could affect the bikes would be easier to see.<\/p>\n

\"The<\/a>
\nThe aggregation of marginal gains. The rising line is improving 1% every day for a year; the falling line is declining 1% every day. The improvement curve climbs from day one, and it never drops below its starting point. Learn more at getnave.com<\/a>.<\/em><\/p>\n

None of those changes transformed British Cycling on its own. That was the point. They weren’t betting the outcome on one enormous transformation. They were continuously finding opportunities to improve the system they already had.<\/p>\n

The Three Questions Every Improvement Initiative Should Answer<\/h2>\n

I think there is a very important lesson in that for organizations trying to improve their delivery systems.<\/p>\n

Start with what you do now. Then identify one problem worth solving and decide what evidence would tell you that the change worked. Before you introduce an improvement, answer three questions.<\/p>\n

What problem are we trying to solve?<\/p>\n

What are we going to change?<\/p>\n

What would we expect to see in our data if that change actually worked?<\/p>\n

That last question matters enormously. Without it, you can introduce initiative after initiative and still have no evidence that any of them improved performance.<\/p>\n

Imagine your teams are struggling with long delivery times because too much work is being started at once. You decide to introduce work in progress limits. Before you do, establish the baseline. What do your delivery times look like today? How much work is typically in progress? How predictable is the system?<\/p>\n

Then introduce the policy and watch what happens over time. Did delivery times improve? Did work in progress decrease? Did predictability improve?<\/p>\n

Now you have evidence. You can keep the change because the system improved, adjust it because the result wasn’t strong enough, or remove it because it didn’t produce the outcome you expected.<\/p>\n

More importantly, when leadership asks whether the transformation is working, you don’t have to ask them to believe that eventually everything will pay off. You can show them.<\/p>\n

This is where flow metrics become incredibly useful. They give you a way to establish your current performance and then measure whether the changes you’re introducing are actually moving the system in the right direction.<\/p>\n

If you’re trying to prove whether your improvement initiatives are actually working, this is exactly what Nave helps you do. You can establish your baseline, introduce a change and track how your delivery performance evolves over time.<\/p>\n

<\/div>\n

Why I Prefer Evolutionary Change<\/h2>\n

There is nothing particularly exciting about making one small change. It doesn’t look like transformation. There is no big launch. No moment where the entire organization suddenly starts working differently.<\/p>\n

But that’s exactly what I like about it. You don’t need to throw away everything your teams already know how to do. You don’t need to wait months to find out whether the transformation worked. And you don’t have to explain a huge performance drop while everyone waits for the promised improvement on the other side.<\/p>\n

Instead, every change has to earn its place.<\/p>\n

Tomorrow, look at your delivery system and identify one problem you want to improve. Don’t redesign the entire system around it. Choose one change. Decide what evidence would tell you that it worked. Establish your baseline. Then introduce the change and watch what happens.<\/p>\n

If the numbers move in the right direction, keep going. If they don’t, you’ve learned something before betting the rest of your transformation on it.<\/p>\n

That is how I believe sustainable transformation happens: one measured improvement at a time.<\/p>\n

See what is changing in your delivery system at getnave.com<\/a>.<\/p>\n

Thanks for tuning in! I’ll see you next week, same time and place for more managerial goodness. Bye for now!<\/p>\n

<\/div>\n
<\/div>","protected":false},"excerpt":{"rendered":"

Weeks or months into a transformation, performance is worse than when you started. Leadership approved the transformation because they wanted better performance. Instead, delivery is slowing down. The numbers are moving in the wrong direction. And the improvements everyone was promised have not arrived yet. How long will leadership tolerate that? How long before people […]<\/p>\n","protected":false},"author":1,"featured_media":7903,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"yasr_overall_rating":0,"yasr_post_is_review":"","yasr_auto_insert_disabled":"","yasr_review_type":"","footnotes":""},"categories":[7],"tags":[1932,1817,1905,1933,1815,1930,1931,1800,1877,1929],"class_list":["post-7902","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-project-management","tag-agile-transformation","tag-change-management","tag-continuous-improvement","tag-data-driven-decision-making","tag-delivery-management","tag-delivery-performance","tag-evolutionary-change","tag-flow-metrics","tag-kanban","tag-organizational-transformation"],"acf":[],"yoast_head":"\nHow to Improve Your Delivery System One Measurable Change at a Time<\/title>\n<meta name=\"description\" content=\"Learn how to improve 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