Data-Driven Management of Teams

by Beau Peterson, Ph.D, Carthage Veterinary Service, LTD.

It has often been said that our industry is “data rich and information poor.” There is a strong argument that this sentiment is improving, however there is still much progress to be made. A wealth of data is collected on a daily basis from all parts of modern swine production systems and with the continued development of sensor technology that enables remote monitoring, the data stream will only grow. Improvements in technology and connectivity of teams has also improved dramatically in the past decade bringing with it, opportunities to communicate and drive production performance. When these advances are looked at holistically, the opportunity to drive production performance and ultimately profitability should be improving by leaps and bounds. Unfortunately, this is not always the case, and normally the culprit is not a lack of data, but a lack of data synthesis and analysis that leads to clear, actionable metrics.

Choosing Metrics that Drive Performance
A good metric is objectively measured and related to the outcome in question, a great metric is measurable, actionable, responsive to changes, and controllable by the person being measured by it. Often times in a data rich environment, it is tempting to create numerous metrics in an effort to create absolute transparency into the operation and drive performance. Metrics adhere to the law of diminishing returns though, and when abundant, can create a great deal of confusion. An effective metric deck adheres to the qualities mentioned above, but it is also prioritized and the individual metrics adhere to a logical sequence that allows the operators to connect dots and quickly get to root causes of issues. Metrics should have very few degrees of separation from the outcome that is ultimately driving profitability. As an example, wean pig output from a sow farm is an outcome that is a critical driver of profitability. PSY (pigs/sow/year) is often times used as an indicator metric of wean pig output from a sow farm. It can be argued that PSY does not fit in the category of a great metric though for multiple reasons. The first reason, is the complication and variation involved in calculating it. The second reason has to do with the fact that PSY is almost exclusively a revenue metric, it loosely considers efficiency, and does not measure cost of achieving the number of pigs per sow and therefore can create a scenario, when chased blindly, that the farm actually loses money at a high PSY. There are many metrics on a sow farm that are better indicators of future wean pig volume and cost and efficiency of creating those wean pigs that should be measured and reported on if the ultimate goal is profitable wean pig volume. Some examples of these metrics are sows bred per week vs target, quality breeds per week vs target, late term fall-out, total born, still born, and pre-weaning mortality. These metrics are top line metrics, and if there is a desire to go a layer deeper, many more can be identified, however this must be balanced with the team’s capability to understand and impact the metrics and ultimately connect the dots to better performance. In summary, metrics that drive performance are granular enough to get to actions that change outcomes, while providing transparency without confusion for the operation.

Leading vs Lagging Metrics
Our industry has historically relied on lagging metrics to measure productivity and profitability. Close-outs, PSY, wean pig cost, cost per pound of gain, mortality, etc are all lagging metrics and therefore there is nothing we can do about the performance that created the metrics, only hope to change or maintain future performance based on our learnings. Even some of the metrics used as leading indicators are themselves lagging by definition. A great example is conception rate, it is a leading indicator of how many sows will farrow, but the actions and processes that created it happened 5-6 weeks, or more prior to measurement. Leading metrics are somewhat difficult to measure and report in swine production due to the fact that it is a biological system that is continually dependent on things that happened prior to a particular event. This doesn’t mean that leading metrics should be given up on though, it just requires some more creativity. Nursery mortality is a good example of a metric that has multiple leading indicators that could not only help to predict it, but if measured and reported on appropriately, could assist with prevention. Temperature in the barn, diagnostic profiles of the sow farm to predict nursery disease, management input during the first 5 days post-weaning, and feed intake during the first week post-weaning are all great leading indicators of potential problems that could ultimately result in nursery mortality. These metrics are difficult to measure and report, however not impossible. Technology advances occur every day that make this type of information more accessible and therefore as an industry, we should be looking for creative ways to adapt it and create this transparency.

Expectations, Accountability, and Routines
One of the simplest concepts in business is that clear expectations that are discussed and measured routinely, lead to accountability. While the concept may be simple and easy to understand, executing on it is often times difficult. Distraction and the tyranny of the urgent are some of the biggest enemies of execution, however clear, simple expectations can be an incredible anchor for execution when things inevitably happen.

Routines drive execution and accountability by creating visibility and the opportunity to discuss countermeasures when performance isn’t meeting expectations. Routine execution meetings do not have to take a lot of time, but discipline is required to ensure the entire team prioritizes them and engages in them. It is critical to ensure the resources are in place to have the metrics required for the routine ready for the discussion. Effective execution routines should be anchored to the metrics as the single source of truth, and should be focused on developing plans to correct out of spec metrics. Care should be taken to ensure the report outs do not become box checking exercises, and this is the responsibility of the leader to ensure the right discussions, even if they are difficult, happen with the team frequently.

The primary goal of collecting operational data is to create actionable, objective metrics that can drive real change and monitor performance in an operation. Data that is collected but has no purpose is wasteful. Often times, a lot of creativity is required to ensure the right data is being collected to create a metric deck that has the right combination of leading and lagging metrics that will drive performance, prevent mistakes, and create learnings that allow continuous improvement. Driving the performance of teams through data also requires strong routines. Data and information that is never discussed, or even not discussed routinely, will not help create the culture of accountability and execution that is critical for success in the swine industry.

To learn more about how you can drive your team with data, contact our Records team that specializes in Data Management and Data Analytics at

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