To understand the future of processes, you must dig a little deeper than many people do.
Process thinking goes back well over a 100 years, to the origin of modern iron and automobile production. The raw materials and finished goods of such manufacturing and production processes are literally spatial – 3-dimensional. What can you do to significantly improve productivity in a 3-dimensional world? The answer these days is simple: You build robots. Robotization has literally changed the world during the past 30-40 years.
Rather than manufacturing and production processes, however, the world is now increasingly focused on white-collar and digital processes. What 3-dimensional presence do the raw materials and finished goods of these processes have?
Well, exactly none. The raw materials and finished goods of these processes aren’t physical and simply have no spatial presence whatsoever (except maybe for paper artifacts). Robots (at least physical ones) aren’t an option. That fact of life makes a huge difference in how you have to think about automation for such processes.
Instead, the raw materials and finished goods of such processes are all about your operational business knowledge – your intellectual capital – and your capacity to express and apply it. That capability, in turn, depends directly on your business terminology and business language. For white-collar processes you have no choice – the world is semantic. So you must deal with the subject matter semantically.
That takes us in a very different direction than most professionals currently foresee. For one thing it takes us toward natural language and away from diagrams-for-everything. That’s a huge shift in mindset. Imagine having a business conversation with your smart phone about gaps and ambiguities in business policies and in the meanings of the vocabulary you use to talk about subject matter knowledge. Don’t think that’s possible? Have you watched your kids talking to their smart phones lately?
Sooner or later businesses will realize that operational business knowledge differentiates their product/services and enables their ever-more-automated processes to function. Capturing, managing and re-using that intellectual capital puts a premium on structured business vocabulary (concept models) and on business rules expressed in structured natural language. Those business rules are the only way you have to ensure quality from white-collar and digital processes.
Read more on this topic:
Are Processes and BPM Relevant in the Digital Economy? http://www.brsolutions.com/2015/10/19/are-processes-and-bpm-relevant-in-the-digital-economy/
Measuring Quality and Defects in the Knowledge Economy: http://www.brsolutions.com/2015/10/27/measuring-quality-and-defects-in-the-knowledge-economy/
Quality and Tolerances in the Knowledge Economy: http://www.brsolutions.com/2015/10/29/quality-and-tolerances-in-the-knowledge-economy/
I was recently asked the question above. It’s a good one. It arose in response to another post, “What is the best way to simplify business process models?”. See http://goo.gl/gWnO0 for a very brief, very dramatic case study.Here’s my answer. Business people (and business analysts) should see only the core set of transforms (business tasks) that respond to some business event, and that lead transform-by-transform to appropriate business end-results through conditional flows. Many process modellers also try to include the criteria that resolve those conditional flows, which in a true business process model, are always business rules. The result is embedding the business rules procedurally within the model, rather than expressing them declaratively (and separately) in their natural form. The more scenarios the model covers, the more hopelessly complex it becomes. The issue has nothing to do with conventions per say, or with the media of presentation – electronic, paper or otherwise. I’m talking about the core stuff of the model itself. Just transforms, transforms, transforms!
The best way to simplify business process models is to take the business rules out and address them separately. Example: For a large pharmaceutical client, we reduced a complex 28pp model to just under 4 pages by doing that – nearly an order-of-magnitude reduction in size (and probably even more in terms of complexity). Everyone loved the results. There was nothing unusual or atypical about the result – we’ve seen it happen many, many times.By the way: If you know of similar cases, I’d love to hear about them!
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