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Home / Magazine Articles / Being data driven – Lean manufacturing vs. Systems thinking

Being data driven – Lean manufacturing vs. Systems thinking

Many housing providers’ IT and business teams are now talking about ‘systems thinking’ in terms of their data-driven processes, often as a complement to ‘lean manufacturing’ practices. I have had the privilege of working with both approaches in service and manufacturing environments and the purpose of this article is to detail my findings of these and some of my own experiences.

My career started as a customer service advisor in an area housing office. As I progressed, I found that I had a natural affinity with delivering sustainable business improvement. While working at two housing providers I was fortunate to be trained in and delivered projects using systems thinking techniques, and I was particularly impressed by the level of focus on the customer, demand analysis and control charts.

By applying demand analysis techniques to call centre calls, it was straightforward to identify where improvements could be made, and by using control charts when looking at performance, we could statistically identify if a process was ‘in control’ or ‘out of control’ (do we need to intervene?). Then by looking down from a ‘helicopter’ perspective and identifying strategic partnerships where customers are shared, waste could be reduced across collaborative enterprises.

Now, having spent most of my career in housing, I then thought that I needed to broaden my horizons. With a view to develop myself, I started working in a manufacturing environment.

It was a bit of a culture shock…

Actually being able to see people working (i.e. physically making things) was different. The base level of lean knowledge in the company was significantly higher than in housing. I shouldn’t have been surprised; lean came to manufacturing in the 1980s but it has never really stopped getting better.

For example, a handful of people in a housing organisation might know about statistical process control (control charts) but it seemed that almost everyone did in manufacturing. Moreover, there were statistical techniques (Anova, T-Test) I learned during my psychology degree masquerading as ‘between-operator reliability’ and ‘attribute agreement analysis’. This was data-driven business improvement at a level that I hadn’t seen before.

I also found the approach to visual management and 5S (workplace organisation) remarkable. Every area had a very large visual management board, there were daily stand-ups updating the board with the relevant team and it actually enabled decisions to be made that were timely and effective. It was impressive for both the discipline applied and its simplicity.

I know there are a number of housing providers going the 5S and visual management routes, but before you go too far, get yourself into a factory and see how it works in practice at the highest level.

Having analysed both of these methods, a hybrid of both approaches makes the most sense. 5S (tidying up and gridding everywhere) doesn’t add the same value to a service organisation as it does to a manufacturing one because the former is limited to electronic files and databases.

Manufacturers don’t use their databases anywhere near as well as service companies do. What was interesting was that the manufacturers I saw were very inward looking in terms of their internal efficiencies. This meant that systems thinking approaches such as demand analysis and strategic partnerships were not as developed.

The common themes are using data to inform improvements and developing people to be able to sustain changes (so organisations don’t need to rely on endless days of consultancy).

At Data Futurists, we have been lucky enough to find consultants from both systems thinking and lean manufacturing and we have tried to fuse their expertise together into what we call data-driven business improvement.

Neil Topping is a director of Datafuturists.

See More On:

  • Vendor: Datafuturists
  • Topic: General News
  • Publication Date: 056 - March 2017
  • Type: Contributed Articles

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