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While on the face of it this advice seems to make sense, there is more than half a century of research to tell us that it will make the problem worse, not better. The “bull-whip” effect, first documented by Jay Forrester in 1958, is a well-known phenomenon in supply chains that every student of operations management learns about as they progress in their studies. What happens is that when a customer near the end of a supply chain (like a ward sister) places an order under conditions where s/he is uncertain whether the order will be delivered on time and in full they instinctively add a bit of “safety stock” just in case. And a little more when they have to place orders long in advance.

The bulk pharmacy receiving orders from many wards then experiences an upward spike in demand and also increases the order it places on the regional distribution centre.

The bull-whip travels up the supply chain and is amplified through every successive order until the factory producing the medication faces order patterns that fluctuate wildly between highly exaggerated demand followed by slumps in demand.

It becomes very difficult to manage production and distribution of supplies under these circumstances. When people in the supply chain work under pressure, the consequences are poor delivery performance, high cost because of under-utilisation of resources when demand slacks, warehouses full of products that customers do not need, and, in the case of public sector hospitals, medication that expires because it was not ordered in response to actual demand.

The Minister of Health cannot be expected to be an operations management expert. He was educated and trained as a clinician. Like many other professionals in government he excelled and was promoted into a managerial and eventually a political position without much to prepare him for the operational requirements of the job.

Over the last decade similar scenarios have played out in South Africa where well-intentioned managers do exactly the wrong thing to address a problem in their area of responsibility. For example, long waiting times in customer-facing facilities such as hospitals and courts is an endemic problem. The instinctive response by the public is to arrive earlier, as early as four o’clock in the morning, in an attempt to be first in the queue. But only one person can be first in the queue, the rest end up overwhelming the available staff and have to wait their turn. Why then do public sector managers tell the public to come earlier in the morning rather than later in the day when they are not as busy? (Cape Argus, 27 June 2012, p. 4).

This creates a demand peak early in the morning that puts the facility staff under great pressure. Then they work like Trojans to process the people only to find that by lunch-time that they don’t have any more customers. Naturally they slump back in their chairs mentally exhausted by the pressure they were working under, and then find themselves accused of being lazy and having a bad attitude. To be sure, there are staff who exploit this situation, but by far the majority of customer-serving public sector staff are people who are trying to do their best under very difficult working conditions.

The solution to this problem is to reduce peak demand by communicating to citizens that if they come later in the day they will wait less. Much like Eskom is trying to reduce the evening demand peak for electricity.

Management have let them down, because it is management’s responsibility provide the operational systems that enable staff to do their work effectively. But can we hold public sector managers accountable for this situation when they have not been educated in the basics of operations management?

There are tried-and-tested techniques available to ministers to help them do their job better. For example, just-in-time supply systems, pull scheduling, demand and capacity management and queuing theory to mention a few that can be applied to improve service delivery. Operations management is under-utilised in the public sector as a means to eliminate waste and improve flow of patients and customers, supplies and information through the service delivery processes. It is high time that this deficiency is recognised and steps taken to incorporate operations management practices into public sector service delivery improvement.

I selected the quotations in this article to highlight that although it is possible to measure many things, it is not necessary to measure everything. But how does one narrow and focus on key metrics? My proposal is to use at least 3 metrics that provide you with a summary assessment of how your team/department is performing from a systems thinking perspective.

The open systems model (Katz and Kahn version shown in the figure below) gives us a general framework that can help the assessment of how an organisation is performing in relation to its goals. What I like about this depiction of an open system is that it makes it possible to see that the output of the system should be seen within the context of the external environment. In the case of defining metrics, this means our metric should focus on our performance in relation to the promise made to the external environment. So our first and key metric should be how we are performing in relation to the service level agreement (SLA) and therefore the customers’ experience of the organisation.

If we are not a monopoly, how we perform in relation to our customers’ expectation will impact the demand for our services. Hence the next key metric should be the customer demand. This metric provides us with two insights. The first is whether our key assumptions about what customers expect from us were correct or incomplete. If we are meeting our SLA and yet the demand is not growing it means there may be other things in the environment which we don’t understand (assuming the economic cycle has not changed). The next insight from this metric is whether or not we have sufficient capacity to meet the demand without compromising the SLA. Plans can be made to safeguard the SLA while meeting the growing or variable demand.

The third metric should come from the operations of the team/department. This is probably where the question of where to focus comes in, since there are many variables that can be measured. An understanding of systems theory is very helpful here because it is from this that we recognise that the performance of a system is determined by the performance of the system’s constraint. The terms ‘bottleneck’ and ‘constraint’ are sometimes used interchangeably but it is very important to know that they mean different things, and understanding the difference is fundamental to managing systems (a topic for another day perhaps).

So our third metric should inform us about the performance and capacity of the system’s constraint. In particular, we want to look at the relationship between the customer demand and the constraint’s capacity. If the constraint is unable to manage the demand we need actions to close this gap. In addition, if the performance of the constraint is dependent on multiple conditions, we would need to increase our metrics to include those conditions. This is because anything that compromises the constraint, compromises the performance of the system. Therefore, at minimum, our dashboard will have three key metrics: Performance against SLA, Customer demand and Performance of the constraint. The need for additional metrics will be determined by constraint’s relationship with other components of the system.

In this age when people are talking about big data, and dashboards are becoming the hype, it may be easy to get lost in the details. I hope this article will provide some food for thought for those who are feeling overwhelmed by the demands of measurements everywhere. It’s important that we remember that organisations are systems and how we use metrics should reflect this understanding. As a manager, what are you measuring and how are you finding data collection?