Many years ago (September, 1994) I read an Scientific American article “Software’s Chronic Crisis” with interest – my job at the time being precisely “Software Development”. The article introduced me to “CMM” (Capability Maturity Model) in software development, as an aid to improving software to avoid the inevitable ‘crisis’. “Maturity” here didn’t connote age (there are certainly very old IT organizations I’ve worked in I would hardly call ‘mature’) but rather ‘well developed’. To oversimplify, this “Maturity” model is a list of best practices ranked (in 5 somewhat arbitrary levels) in apparently order of impact. (For further reading, I suggest Googling CMM and see related information. Carnegie-Mellon is the mother ship).
Much after 1994 a further-elaborated “CMMI” model was launched (I for Integrated) bringing together software, organization, design, and … process into an integrated model. The elements were the same however – levels, practices, and so forth. When we began a Supply Chain “Center of Expertise” (COE) at HP, my team (once we had the time) also looked at what was really the maturity of our work (process), and what was the true value. I can assure you that ‘maturity’ (i.e. best practices) is quite valuable. Let me describe our maturity framework, and give you four examples of why it’s valuable.
|Level||Supply Chain COE||Supply Chain COE Features||CMM Equivalent|
The “Level 0” which is almost unmentionable is “chaotic” – imagine a situation where nobody knows how to perform a task, or everytime a business process is performed it’s performed differently… Welcome to the 9th circle.
Let’s say a “standard” Supply Chain COE improvement program for us is about 5 weeks, with everything being more or less perfect. Let’s look at the impact of process maturity on this timeframe.
Whenever the HP Supply Chain COE team began a project, the very first activity they perform is to gather up as much existing documentation as possible prior to the first workshops in process discovery. For projects and improvements in business areas where the Supply Chain COE team has already been, this process is, well, about 5 minutes – they go to the Supply Chain COE repository and pull up the current model. The extreme on the other side is where nobody actually knows how processes are getting done at all, there is no definition, or they are not ‘digitized’. The difference in time/efficiency between the two steps is significant. For a single process thread, the general timespan is 1 week to from scope to capture. With a maturity ‘1’ organization, this takes 1 day – a 5:1 compression in that phase. We saw this when peforming Supply Chain COE on certain supply-chains which had already been examined by prior teams – in the merger between HP & Compaq. The merger process models were immediately re-used and got through to analysis bypassing lots of workshops and planning to perform capture.
Takeaway: we achieve a roughly 16% efficiency gain in level-1 maturity by having processes documented, accessible from a repository.
Compares to: ISO9000
When a project heads towards analysis (after capture), there are many blocks of techniques you can use – Six-Sigma, RACI, Kaizan, an on and on. One thing most of them have in common is business performance metrics. In a “no metrics” situation for analysis, it can take sometimes weeks (or never) to get data on process performance for a single ‘thread’. It can be a complete nightmare to get benchmark data external to an organization… furthermore, if you’re comparing organization to organization internally, you begin political firestorms. In a ‘manage to metrics’ situation, and in particular where the metrics are aligned to standard formulas (like SCOR for Supply-Chain), the time taken to gather data for analysis can compress from, in our experience, from 2-3 days (average) to 1 or so. Pretty idealized, but you can go to benchmarking companies who supply SCOR metrics and get data in less than a day for your industry – if you use SCOR, and if you have comparably defined internal data. You will see a 3:1 compression. On one program, the business partner we worked with wanted to benchmark box-level ‘shrinkage’ (losses) in their warehousing operations. By convincing them to use an industry-standard value of ‘shrinkage’ as a value metric, we could immediately access industry date without performing complex surveys. Furthermore, we could compare multiple internal organizations who did in fact manage shrinkage financially.
Takeaway: we achieve a roughly 10% efficiency gain in Supply Chain COE by level-1 maturity. This gain can be more significant the larger the program – multi-thread Supply Chain COE programs across multiple business lines, or programs which use simulation (lots of parameters) work even better.
Compares to: Balanced Scorecard approaches
From Speed up Capture, Analyse, Design and Implement. There’s nothing more horrible to me than, once having defined a valuable change to a process environment, you have to roll it out to 500 different variants; or having to examining 500 variant-by-variant process structures to design a ‘to-be’ state, or capturing 500 variants to a core process. When we work with organizations who have created standard process architectures which handle most domains without fantastic high-level duplication, the entire Supply Chain COE speed-up is basically proportional to how standardized and simple they’ve become. In our example for a single business unit, by consolidating 7 variants of process in a part of their product design stream to 1, the process team drastically sped up the Supply Chain COE cycle for change to that ‘standard’ process, and the implementation planning, as well as to-be design were 7:1 times simpler. We only use framework-based Supply Chain COE, but in comparable situations I’ve seen team’s spend a month for every project to create definitions. We’re speaking of 30 days to 1.
Takeaway: having defined ‘standard’ processes, and using standard defined language in Supply Chain COEs enormously speeds up programs. You can expect in the ‘standard’ program improvements of 2:1 or better in time, or a minimum of 50% improvements (they scale the entire program!)
Compares to: Framework Supply Chain COE
Lastly, where you have defined, aligned, and standardized processes where metrics drive into a repository where you can apply continuous statistical performance analysis, you can drive another day or so from the analysis timeline. In general these types of solutions can provide you with pre-cooked cause/effect analysis, or can alert you when triggers occur and give you immediate context and cause/effect views. You’ve basically eliminated scope, capture, analyse steps altogether, and allow for direct ‘to-be’ solutions for the event, whether it’s at a tactical or even strategic level. We’ve seen this in particular with Supply-Chain Events alert systems we use. When cycle times for defined, standard processes vary outside certain bounds, teams are alerted to begin to compensate. These repositories don’t necessarily help with structural or best practice issues in processes, but definitely take a lot of time out of the Supply Chain COE improvement cycle,in our standard example.
Takeaway: metrics repositories with automatic statistical controls grants a roughly 5% incremental efficiency in the Supply Chain COE cycle.
Compares to: Continuous Six-Sigma
What’s left? Nirvana. We are experimenting with dynamically optimizing processes – using different kinds of software tools to make the process very simple. When we have data I’ll share it…!
Compares to: Supply Chain COES
So what’s the net-net? By moving to level-4 maturity, our example process team can achieve 30% or better improvement in efficiency over ‘level 0’ maturity, with the most gains occurring in level 1 and level 3. What’s that translate to? Look at how much your company spends on consulting. Many years back, pre-merger, when we looked at consulting spend company-wide, we could see perhaps $300M spend. Wouldn’t it have been nice to lop off $100M in cost? How much do you spend on Sarbanes-Oxley compliance? AMR cites the spend in 2005 to be projected at $6B dollars. Lop off $1.8B from that spend. Growing up has never been more valuable.