Archive for the ‘process change program’ Category

How to create flow.

24 March, 2017

Flow is the new buzz. Each of us knows exactly what flow is. And how it feels. It feels great! You probably have even experienced it yourself. Great!

But flow in relation with work?  That is something else. It needs planning,  and changing work habits.

But we can create flow in our work environment. We are able to let work items or information flow through our work processes. Just look at the car manufactures. Their assembly lines are a perfect example of flow. But it took them many years and hard work.

The good news is that we can also implement flow in creative work places. Whether we create fancy apps or provide services. In HR departments, DevOps teams, back office, marketing teams, they all are able to create flow in their delivery process

Below I explain how you can start to implement flow.

Visualize your system to identify your bottleneck

    1. Identify Bottlenecks/constraints.
      • Draw a value stream map (VSM) to  visualize your system.
      • The VSM creates a common language to engage your stakeholders in your endeavor.
      • The value stream shows you the constraints. Probably you already knew the constrain (or had a good guess). But it is like handling risks, once they have been documented, they become somehow more real. This is also true for constraints.
        • Also good to realize is that you handle constraints with care. Even with more care then porcelain. It involves people. and the tricky thing is that people who are part of the constraint, became the constraint due to the system. Not of what they did or did not do!
  1. Eliminate constraints (not the people!)
    1. Exploit the constraint.
      • Let the constraint only work on the flow-impeding work.
      • Reduce the amount of incoming work using WIP limits to the level that the constraint is able to chew.
    2. Increase the constraint capacity.
    3. Find the next constraint.
  2. Ability to manage stakeholders that impact the constraint.
    1. The demand of work is defined outside your responsibility level. So in order to balance the demand with the available capacity, takes some diplomacy and persistence.
    2. Further downstream stakeholders are in need of your work items. They will claim your capacity is not fully used.
    3. Invite your stakeholders to some flow experience
      • Involving your stakeholders in a short Flow simulation workshop, combined with theoretical explanation on queuing theory may just do the trick to at least allow you to do an experiment with manage the work through WIP limts and other queuing theory techniques. (The Okaloa Flowlab simulations offer a range of different simulations that address all aspects of flow).

Some additional advice.

Start with flow simulations with some key stakeholders. They first need to understand the basic concepts of flow, queuing theory and WIP limits. This also let them experience the impact of WIP limits on the speed and reliability of their delivery requests.

On team level  create a stable input of work. Ensure that the teams grow into a stable delivery cadence. Use the team simulations to allow the team to experiment and experience flow in their own environment. You can use the actual work environment of a sprint to experiment with reducing WIP and allow the team to  become more stable and predictable in their  deliveries.

Next step is to let multiple teams that work to create flow of their combined teams. Run the cross team simulation as a start to have teams and product management experience and experiment addressing aligning capacity differences to a smooth delivery cadence.

Upstream, product management  (program or value stream level) implements a Kanban system that adheres to the same principles of flow as on team level. But here is one additional focus point.

Internal product management use WIP limits to effectively deliver a constant flow of work requests for the downstream teams.

From the perspective of the downstream teams product management takes care they have at all times sufficient work requests (options) available. This ensures that product management is never overstressed in producing sufficient work for the downstream delivery teams (quality, instable workloads up- and downstream). For the downstream  teams this means they have at all times sufficient work on their incoming backlog.  They are able to maintain a stable cadence.

For more information on  simulations  look at the available workshops in Europe on Okaloa. On May 19th we offer a public workshop  on the Okaloa Flowlab simulations in which we  specifically address implementing Flow or Kanban systems in SAFe environments and other scaling approaches.

 

Cynefin en Kanban == niet planbaar met optimaal resultaat ==

30 January, 2013

Meer en meer worden Cynefin en Kanban met elkaar in verband gebracht. En dat is niet zo gek. Immers, beiden zijn een manier om naar complexiteit te kijken. In deze blog ga ik in op de gecombineerde kracht van beide. Gecombineerd geven zij een organisatie een niet-planbaar-optimaal resultaat.

De kern van Kanban is het inzicht krijgen in de capaciteit van waarde leveren aan de klant. De hele keten wordt hierbij betrokken. Door de deel-performantie van de verschillende stappen op elkaar af te stemmen, ontstaat een optimale doorstroming op organisatie niveau.

Dit inzicht en manier van werken vormt vervolgens het uitgangspunt voor continue verbeteringen en aanpassingen. Het (gevisualiseerde) inzicht bij alle betrokkenen geeft het continue verbeterproces een natuurlijk karakter.

Cynefin is een model dat 5 probleem domeinen definieert: obvious, gecompliceerd, complex, chaotisch en disorder. Ik verwijs naar http://en.wikipedia.org/wiki/Cynefin voor een meer gedetailleerde uitleg.

Proces verbeterprogramma’s of de implementatie van Lean/Kanban in een organisatie zijn beide typische voorbeelden van complexe problemen. Complex betekent dat er geen directe relatie bestaat tussen oorzaak-gevolg. Het resultaat is niet planbaar en is alleen terugkijkend verklaarbaar. Dit in tegenstelling tot een probleem in een gesloten systeem (gecompliceerd en simpel). Daar zijn oorzaak en gevolg door analyse of categorisering te voorspellen.

De Cynefin aanpak voor complexe problemen is door gebruik van de gecombineerde kennis van experts, inzicht te krijgen in de mogelijke oplosrichtingen. Deze vormen de basis voor de volgende stap. Het starten van safe-fail `(niet fail-safe) probes. Door een continue en real-time feedback loop wordt de impact van elke probe zichtbaar. Succesvolle probes worden versterkt en uitgebreid. De niet succesvolle probes worden gestopt. In een zich ontvouwende werkelijkheid wordt een (niet gepland) optimaal resultaat bereikt.

De gecombineerde kracht zit in de opbouw van kennis over de capaciteit van de organisatie en het begrip van de mate van complexiteit van de taken (zowel in bouw als management). Het leiderschapsteam kan op basis van deze kennis en begrip de organisatie stimuleren tot continu leren en verbeteren. Het vormt de basis voor de strategie en de continue aanscherping van deze strategie.

How SenseMaking creates learning organizations by effective change programs.

8 June, 2012

The natural approach for effective change programs is full involvement of and guidance by all involved key-players. Combined with a natural learning approach this leads to effective continuous change programs and a learning organization.

What is different related to old-fashioned centrally and top-down managed change or improvement programs is the emerge of the outcome during execution of the change program. Based on the learning path, new nearby objectives are defined.

Sensemaking creates learning organizations in just 6 steps.

1    Entry criteria
Use the 6 steps only if your problem belongs to the complex domain. Obvious and complicated problems are bests solved by using standard solving techniques. See the Cynefin model for background information on the different problem domains.

2    SenseMaker design
SenseMaker® facilitates continuous, small and large scale, fine-tuned listening to all stakeholders (clients, employees, external (non-)experts). The design of SenseMaker is essential for a successful change or improvement program. A correct design ensures that the right information is distilled. An incorrect design provides not the information that the next steps need in order to be effective. It leads to frustrated stakeholders as their voice, thoughts and ideas are not heard or implemented.

3    Initial workshop
The initial workshop both validates the SenseMaker design and generates a vision of possible solutions. These possible solutions provide the first steps towards learning. Typically a Cognitive Edge method like Future Backwards is used to facilitate the workshop.

4    Experiment definition
The outcome of the initial workshop is used to define a first set of low-key-low -profile experiments. The costs and duration of each experiment is kept to a minimum. None of the experiments is expected to provide an ultimate solution to the problem.

Each experiment is described in the form of actions and indicators for feedback-signals that indicate positive or negative change. By generating additional context information, management is able to understand early (weak) signals. In this way initial actions that show promise are extended while negative indicators trigger recovery actions and choose of alternatives approaches.

5    Learning by experimentation
The initial experiments provide learning opportunities. Learning is generated both from the experiments participants and by the feedback loop continuous and real-time generated by all stakeholders using SenseMaker.

6    Emerging change
Outcome of each experiment is evaluated both by means of it’s direct outcome or delivery and by means of the feedback mechanism. Those experiments that both deliver on outcome and receives good feedback are re-evaluated for strengthening and to be continued. Those experiments that fail to deliver are stopped. Notice that learning may trigger new experiments.

Step 5 and 6 is a continuous loop. New problems or emerging insights will most likely trigger new experiments. A learning organization is created.

The above figure below shows a graphical view on experimentation in a learning organization. In the emerging process of learning, more and more practical knowledge is generated. This enables more stable experiments. By reducing the risks of failing experiments, the size (€) and duration (T).