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The Five Myths of Effective Operations Management

Since the GFC hit in 2008 both business and government have become more focused on cost and efficiency. In our experience of service related industries (i.e. those other than primary production and manufacturing) the spotlight has been, inevitably, on the people intensive part of the operation such as back office administration, calls centres and retail networks. Operations management in these environments has become an increasing focus even where operations are sent offshore. Unfortunately, we’ve seen a number of organisations look for a “silver bullet” in this space and a couple of “solutions” have emerged that need to be debunked. So this paper sets out to debunk five myths:

  • Myth 1: It’s Just About Numbers and Targets

  • Myth 2: Visual Management Boards Drive Improved Productivity Automatically

  • Myth 3: Team Leaders Have the Time and Know How to Improve Performance

  • Myth 4: Quality Management Drives Effectiveness

  • Myth 5: Effective Resource Use Is About Forecasting and Planning

After debunking those myths we describe a framework that has enabled organisations to deliver sustained increases in operational performance consistently.

Myth 1: It’s Just About Numbers and Targets

A common solution we have seen applied in Australia seems to rely solely on productivity measurement. This approach defines the types of work performed, applies some form of time standard for each and then asks people to track what they have completed. This means productivity targets can be set and tracked. So if person A completes 10 widgets of type B and 20 of widget type C, their productivity can be calculated based on the time standards for each widget. Unfortunately we have seen many examples where the numbers aren’t quite telling the whole story and the net result is reduced effectiveness and output. In clients we have observed in funds management, banking and insurance they have implemented this kind of workload tracking. Sometimes this is highly automated, sometimes staff track and key every item they have worked on. The net result is that performance is assiduously measured, recorded and rewarded or so it appears. However, we have observed some common problems:

  • staff were regularly obtaining 200% productivity. Managers were congratulating each other “200% is fantastic isn’t it”;

  • staff were spending between 5-10% of their day updating the tracking mechanism;

  • a staff member per team in addition to the team leader responsible for “allocation” of work;

  • complex work with longer backlogs than simple tasks; and

  • lots of activity (says the tracking system) but not much output (from observation or macro calculation).

So what is really going on here? There are two common problems. The first is that staff get very good at working the measurement system rather than doing the work. In one bank staff got a credit for opening a pending case so they did that a lot, even though it achieved nothing. Hence they looked 200% productive. The second problem is that the “standard times” quickly get out of date or are averages that don’t reflect the varying levels of work. If standard times don’t match the true complexity, the staff cherry pick the work. So while we have no problem with these measurement systems in theory, they have to be very carefully managed to be effective. The best system we’ve seen was completely automated and staff were allocated work whenever they hit “get work”. The system could not be gamed in any way and there was no allocation or tracking overhead.

The ability to game the measurement process is even true in sales environments. In one of our clients the sales conversion rates were seen as crucial. A 20 minute side by side observation uncovered that on most calls staff were cancelling the existing product and reissuing, resulting in a gross overstatement of sales results. Spot rewarding of larger sales resulted in cherry picking and under servicing lower value sales was also evident. Simply, refocusing on net revenue per rostered hour and conversion rate solved these issues. Thinking through the behaviour you want and creating specific behavioural targets is key to productive targeting.

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