Profit meters – These risky profit hills for each key performance indicator (KPI) and controlled variable (CV) are the profit meter, showing where you stand relative to the hilltop and red peak of perfection if you got your inputs right. Just point to your current position on the hill3.
If the brown hill is correct, any input data error will mislead to a different best mean and a corresponding loss down the brown hill. This proves incorrect data always leads to measurable financial losses. This method quantifies that loss. Honesty is indeed the best policy.
All you have to do is find the right hill and stay on top. This is how to manage risk. It optimizes setpoint setting and quantifies the financial value of improved dynamic performance, reduced variance. This also quantifies the mantra: It is better to play it on the safe side. The hilltop obeys the law of diminishing returns.
This one-independent-variable-at-a time optimization theory guarantees a unique solution, so anyone who properly deploys it will perform better on average than those with equivalent talent and skill who do not.
The greatest gain from reduced variance comes at the hilltop where the curvature is greatest; the gain diminishes as the mean is further removed from the top and the clifftent function approaches linear, so deviations cancel. This is why control engineers should focus on aligning setpoints to economics first for easy profit, before working on reducing variance, even where gains are greatest. To do this, they must know the clifftent for each CV.
There is an optimum percentage of the 30 ºC limit violations and an optimum margin limit – hilltop mean, nσ. (The optimum nσ is never 6σ; it depends on the tradeoff situation.)
Finally, the conventional process control benefit claim procedure is incorrect on three counts. First, it assumes the starting mean is best; this is wrong. Strike one. Then it assumes reduction in variance about that mean makes no money; this is wrong. Strike two. Finally, it assumes that with better control, the new mean is moved to the new best value; this is wrong. Strike three. The conventional procedure is arbitrary and excludes the very real variance reduction benefit that has remained intangible and hidden4-6 for so long.
How the Hydrocarbon Processing Industry Operates
Operating chemical manufacturing businesses is a pillar of chemical engineering, after product and plant development. The way the hydrocarbon processing industry (HPI) operates its plants can be described in a straightforward way.
Purpose – What is the purpose of operating an oil refinery or the HPI? Maximum production? Optimum yields? On spec products with no quality giveaway? Minimum energy? Minimum inventory? Minimum maintenance? Maximum reliability? Perfect safety? Zero emissions? Obey the law? Customer satisfaction? Job security? Job satisfaction? Shareholder value? Can all these goals be unified?
A useful objective for optimizing risky HPI operating tradeoffs is maximizing expected value profit over appropriate periods4-15. The universal HPI performance measure is expected value profit rate, not maximum production, not minimum energy, not minimum inventory, not zero mishaps.
Situation – How do you operate an oil refinery2, 6, 13, 14 or petrochemical7 plant? HPI operation consists of setting important operating conditions such as production rate, product quality, efficiency and inventory. "Important" means the settings are economically significant. These targets, limits and specs are directly related to control system setpoints and constraints for flows, temperatures, pressures and levels. The settings should be set right, and the plant should be held at them. If plant constraints are set too conservatively, the allowable operating domain is too restricted, and profitability suffers. If constraints are set too liberally, the allowable domain exceeds feasibility, and ugly things happen.
HPI operations are filled with tradeoffs, risky tradeoffs and penalty pitfall cliffs. As in all decisions of life, HPI operations require optimizing risky tradeoffs6. The only thing operators can influence is the mean and variance of CVs and KPIs. Even major equipment changes only provide new feasible setpoints. Every significant CV/KPI has a risky tradeoff associated with it. Every CV/KPI has a profit meter3, 6 associated with it.
Profit tradeoffs are shaped like a tent, invariably concave downward, with a peak at the proper limit. Many CV profit functions have a discontinuity near the peak, a cliff. Exceeding limits can have serious penalties. HPI operations are full of cliffs2, 6. Do not fall off big ones very often. But maximum profit results from tight control near the cliff edge.
Questions – How do you optimize tradeoffs? Are risks involved? How do you account for risk? Is safety involved? Do economics matter? Does the maintenance state of the equipment matter? Is there any established method for doing it right? Any best practice? Any universal decision method? Or is it up to organization, empiricism, engineering judgment, culture and management experience? Is an IT provider a profit or cost center? If the financial value of IT services, products and solutions cannot be quantified, they should be a cost center, based on the faith theory,8 experience or judgment. If they can quantify their financial value, they can become a profit center.
IT Problems – Is there a standard decision process for gathering pertinent information and setting setpoints? Does the value of data, information, measurement and control depend upon what you do with it? If so, is there a standard method for doing something with it? Deciding what to do to set setpoints? If not, then it will not be possible to predict the value of data, information, measurement, control or unclear decisions.