Measuring Supply Chain Effectiveness - Too many metrics?

The supply chain leaders are faced with increased pace of change – demand uncertainty, competitor actions, new product introduction, technology change, supply uncertainty and regulatory changes; change is a constant in even seemingly mature and stable businesses. Supply chain practitioners have to respond to this through active / agile management. Constant monitoring of the supply chain is necessary to make the plan happen. What metrics should enterprises monitor?
The problem in today's enterprises is too many metrics rather than too few metrics. But even with a plethora of metrics, the enterprises are usually slow in sensing events and responding to those events. Debra Hofman had written in her AMR Research Report titled The Hierarchy of Supply Chain Metrics: Diagnosing Your Supply Chain Health (Subscription required) that measuring performance effectively remains a challenge because of an abundance of possibilities and the lack of enabler metrics. The enabler metrics provide actionable information that help you answer why there is a shortfall as indicated by an operational metric.
The best way to think through the metrics are the following

1. Understand the interdependencies between metrics
2. Think of three levels of metrics - metrics that measure overall health, metrics that help in initial diagnostics & supply chain effectiveness metrics for detailed root cause analysis
3. Think of both metrics on transactions and metrics on actual versus plan
4. Think of "actual versus plan" metrics for frequent tracking rather than as an end of period analysis tool
5. Think of metrics like a fishbone diagram or an Ishikawa diagram

Some of the metrics, besides financial metrics like revenue, revenue growth etc., that you can use for analyzing the over all health of the supply chain are perfect order (customer requested ship date performance), forecast accuracy and supply chain costs. For initial diagnosis of the problem, promise date performance, cash to cash cycle metrics like inventory turns, days of sales outstanding, days of purchase outstanding can be used. For root cause analysis, more detailed metrics like supplier on-timer performance, supplier quality, variability in production lead time, plan attainment in production and logistics etc. need to be used.
Let us take an example: Let us say that the customer requested ship date performance has decreased lately and has gone below the plan for that channel for that product. At the overall supply chain health level you know that there is a problem. But the problem could be because the demand has gone up, order lead time from customer has gone down, or because the supply has suffered. These possibilities can be analyzed by metrics and they are initial diagnostic tools. Let us say that the demand has not gone up significantly and that the order lead time has not gone down significantly. Then we need to drill into the supply side metrics to find the root cause. The metrics on the supply side that need to be checked could be any of the following -

1. Was the inventory target set right? If it was not set right, was the supply variability underestimated, was the supply lead time underestimated?
2. Was the planned replenishment adequate to keep the inventory at the inventory target?
3. Was the planned replenishment executed or was there a short fall? If the replenishment was not executed to plan, was it a capacity problem or a material problem? If it is a capacity problem, was it a run-time or machine uptime variability issue or was it a quality and rework issue?

If this reminds you of a fish bone diagram, that is exactly what this is; a fish bone diagram is a good way to think through metrics.
What do you think?
Karthik Mani