In a previous post, we described our journey towards continuous delivery (CD). Implementing the practice provided us great benefits, but deploys were still handled manually based on a checklist, consuming precious time from our development team and always risking some human error. Time is always an important factor since means a cost of opportunity: if the task can be automated, your developers could be developing new features or fixing bugs. In the end, this translates not only to money but also to developer happiness: they deal with creative work and avoid a checklist :)

CD is based on five principles: build quality in, work in small batches, computers perform repetitive tasks, relentlessly pursue continuous improvement, and that everyone is responsible. It seemed to us that the journey was not complete if we did not enhance the deployment process, providing at least some degree of automation at it. The final goal was to implement a limited version (single site, not facing our clients) to showcase how continuous deployment could help us eliminate manual work and time invested on deployments, and help us always stay current on the latest version while minimizing downtimes. …

“Without data, you’re just another person with an opinion.” W. Edwards Deming.

This year we attended FutureMSCE event, which brings together professionals from all Slovenia and abroad regarding supply chain collaboration. It was a great congress and there many points where we envisioned ourselves delivering value by solving supply chain problems. Through this post, we would like to present some relevant ideas and provide insight into our vision and how we are solving them through our platform.

Optimization planning

One of the most important problems companies face is accurate planning: the less error is introduced in expected demand, the better we can optimize the whole process and infrastructure, reducing stocks and associated costs. This is no easy task since due to imperfect information we suffer from the bullwhip effect: small fluctuations at the retail level may cause larger fluctuations in demand at a wholesale level. Wrong demand forecasts do not only affect stocking levels but sometimes introduce direct loss if requested materials are perishable or too specific to be reused. …

At Qlector, we are committed to developing and delivering high quality software and we take into account the best engineering practices as listed in 12 factor apps.

In the following post, we describe how we introduced Continuous Delivery. By doing so, we’ve reduced waste and the costs of development, as well as increased quality. First we will briefly describe the practice and principles, and later dive into details about how we implemented it. Throughout the post we point to relevant articles which were useful to us when thinking about problems and designing a pipeline.

Definitions, principles and metrics

CI & CD2: how do we define them?


Jože Rožanec

Software Engineer interested in working at the internet industry, where agile development meets scale. Interested in the intersection of ML and big data.

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