Evaluating terminal performance
POOR PERFORMANCE AND STELLAR PERFORMANCE CAN BE EQUALLY HARD TO RECOGNIZE
CLARK VARNER, NORTH HIGHLAND, HOUSTON
UPSTREAM IS ALWAYS at the mercy of the price of oil. Refining profitability is wildly varied based on this. But in midstream, the margin is mostly impacted by controlling costs. They don't have the built-in excuses of the market, but there are some external and internal influences affecting performance.
Benchmarks can be helpful, but rarely take into account factors specific to individual terminals, making it tough to compare apples to apples. Even within the same geography, differences in environmental requirements, labor laws, unions, and access to pipelines and waterways greatly influence a terminal's costs and revenue opportunities. Further, how a terminal is used within a trading environment greatly affects inventories, "hustle costs," and ultimately, sales prices.
The result is that poor performance is often difficult to identify and correct, as stellar performance is difficult to recognize and reward/reinforce. A terminal with better cost numbers than another that is operating under more difficult circumstances is considered a better performer even though the opposite may be true.
For example, if a company is comparing a terminal operating in the Houston area with one operating in the San Francisco Bay area, it is likely that the numbers for the Houston area terminal will be better, regardless of how efficient operations really are. Barriers such as unions and more strict environmental regulations should be accounted for in what is considered optimal performance.
The Theoretical Best Approach, applied to terminals, looks at the best possible performance that can be achieved at each individual terminal. This takes time to analyze each terminal, but much of the analysis can be re-used on terminals in similar situations. The theoretical best, for the purposes of this analysis, is what could be achieved if removable barriers are taken away (often with investment of time and money). Then the barriers are added that prevent optimal performance - things like small tank sizes, lack of automation, high cost of personnel, etc. This revised target is called Best with Barriers (see Figure 1).
For example, Terminal A could theoretically have a personnel cost of $2,400/day (assuming fully loaded cost of $100/hour and having the terminal manned 24 hours/day due to regulatory requirements). However, the need for manual inventory reading and other manual tasks that could be automated raise the headcount to 1.5 employees, or $3,600/day. With no investment in automation or reduced manual tasks, this is the "Best with Barriers" for that terminal.
This approach provides the data to make business cases for improvement (removing barriers) and for identifying barriers that weren't obvious before. If a terminal cannot consistently meet Best with Barriers levels, there is often another barrier in place preventing optimal performance. Or there may be a perceived barrier that really should not be in place (such as an outdated policy or procedure that is still being followed).
Here is an example of how personnel costs could be judged based on what they are compared to. If a standard benchmark of $3,800/day is applied, actual performance over a five-day period would look like Figure 2.
It would appear that the terminal is doing well as compared to peers in the benchmark, exceeding performance expectation four out of the five days. When the same actual data is compared to Theoretical Best, performance looks horrible (see Figure 3).
As performance at the Theoretical Best is unachievable given the barriers in place at this specific terminal, it is difficult to see what is efficient and what is not. It would appear that analysis would be needed on every day's performance to determine causes. As with comparing the actual performance to benchmarks, this approach makes it very difficult to determine where inefficiencies are hiding and what to address.
Figure 4 measures the same actual data against the Best with Barriers targets calculated specifically for this terminal. This view shows that the terminal is doing pretty well on days 1 and 5, at its best possible on day 2, and could use some attention on what happened days 3 and 4.
Creating visualizations for this data makes it fairly simple to drill down on trouble spots and take corrective action. For many companies implementing and standardizing enterprise resource planning (ERP) software over the past few years, the data is available to see levels of detail that should make it clear where to take corrective action.
By focusing on where an individual terminal should be performing, given its limitations, a conscious decision is made to live with a barrier or remove it - or, in other words, where to make investments in sustainable performance improvements. Separately, day-to-day performance is evaluated to judge current performance against a realistic expectation. Keeping these views separate provides clarity on where to focus in both.
ABOUT THE AUTHOR
Clark Varner is a vice president with North Highland Worldwide Consulting. With more than 25 years of experience in energy consulting, he has specialized in operational efficiency, downstream supply chain management, and customer relationship management. Varner holds an MBA from Rice University and a bachelor's degree in finance from the University of Texas at Austin.