Personal Blogs

We all hold values and beliefs which govern how we chose to complete our day-jobs.  However, sometimes the consequences of our actions do not produce the results we had expected.  In these situations, there has been a mismatch between our intended actions and our actually results.  When mismatch occurs, we look for new ways of doing things so that this undesirable consequence doesn't repeat itself.  This type of learning behavior is defined as 'single-loop' learning.

Single-loop learning changes how we execute our daily tasks, based on prior performance outcomes.  What single-loop learning doesn't do is alter our values and beliefs governing how we should act.  Only double-loop learning will shift these governing variables.  The power of double-loop learning is that it can free individuals and organisations from accepting excuses for less-than desirable performance.  Below is a real-life example of double-loop learning in action.

    

Supermarket Merchandising; A Product Feature Problem

• Governing Values.  

1. Product features must always be full of merchandise and presentable at all times.
2. No stock is to be left unaccounted for.  (The store's inventory computers must have a record of where everything is).
3. No merchandise can be 'plugged'. (Plugging is when product in forced into a gap that belongs to another product).
4. No employee overtime is ever allowed.
5. Associates are not allowed to order product. (Computers must automatically re-order when product is sold).

• Actions

1. An Associate has only 40 mins left in his shift.  He notices a product feature is nearly empty.  There's lots of gaps on the shelving and there's not enough product left to make this feature look presentable.  He therefore decides to dismantle this feature, and replace it with a different product that he has plenty of in the back storeroom.  However, the remaining off-coming merchandise now needs to be given a new location.  Unfortunately by this point, the Associate has ran-out of work hours must clock-off.  He is not (under any circumstances) allowed to work overtime.  Consequently, he has no time to properly scan these off-coming items into the back storeroom.  He sees a space on a nearby shelf, which normally holds a different product that the store has currently sold-out of.  He therefore decides to 'plug' this off-coming product into that gap.  He knows plugging is not allowed, but he has ran out of time and see little alternative.

• Consequence

1. The Associate ran-out of time to complete his tasks correctly (according to the stores governing values).  The consequence of this, is that the Associate decided to plug merchandise into a location temporarily.  When the Store Manager later questions why this product is in the wrong locations, the Associate present a defensive justification for his actions.  Despite the Store Manager being annoyed, the Associate quotes store's policies on 'overtime' and 'unaccounted' stock, and uses this in his defense against his Store Manager.  Both the Associate and the Store Manager ends this discussion equally frustrated with each other.

• Single-loop Learning (Actually Outcome)

1. To prevent the risk of upsetting his Store Manager with the same problem in the future, this Associate learns that he should not attempt to fix the product features when he has less than 1 hour left in his shift.  The Associate realizes that had he done nothing, and left this feature looking empty and unfilled, he would have not been blamed.  The Store Manager would still have noticed the feature being empty, but would not have been able to attribute blame on the Associate directly (as everyone is responsible for maintain the product features).  The Associate would have been able to hide his strategy behind the guise of 'collective responsibility'.

• Double-loop Learning (Lost-Opportunity)

Had the Store Manager and Associate discussed the problem openly, using a double-loop learning perspective, the could have made systemic improvements to their 'governing values'.  Both the Associate and the Store Manager could have altered their 'governing values' towards over-ordering.  Going forward, the Associate could adopt a new daily routine.  Each morning he would take 5 mins to scan every feature, and order replacement merchandise well in advance.  The Store Manager would agree to confirm the additional stock orders.  Ordering new merchandise well in advance is a very quick process.  Checking daily, would ensure the Associate is never placed in the same predicament.  It will also ensure all features remain full and presentable, in a much more time-efficient manner.  

Had both the Associate and the Store Manager discussed this problem from a double-loop learning perspective, they may found other solutions that involve altering their 'governing values'.  Instead the Associate adopted his new strategies of not caring about product features (deferring blame), when he has less than 1 hour left on the clock.  

• What's the Difference?

Why is "adopting the new product-ordering routine" a 'double-loop' rather than 'single-loop' outcome?  

Surely, the Associate has just altered his 'actions' with a more effective 'single-loop' solution.

A key difference is that 'governing values' are socially accepted constructs, devised through collective agreement between multiple stakeholders and perspectives.  Ordering new product would not have been socially agreeable according to the old governing values, as the Store Manager originally did not want Associates ordering new product manually.  This 'double-loop' solution requires the entire Store adopting an systemic change in viewpoint to their ordering strategy.  The 'single-loop' solution did not alter the systemic strategy, it only alter the execution of the existing systemic strategy.