User-Friendly RASCI Accountability Matrices

Right now, you’re probably thinking that’s a statement of opposites. Something dreamed up by a consultant to impress, or just to fill a blog page. But wait. What if I taught you to create order in procedural chaos in five minutes flat? ?Would you be interested then?

The first step is to create a story line ?

Let’s imagine five friends decide to row a boat across a river to an island. Mary is in charge and responsible for steering in the right direction. John on the other hand is going to do the rowing, while Sue who once watched a rowing competition will be on hand to give advice. James will sit up front so he can tell Mary when they have arrived. Finally Kevin is going to have a snooze but wants James to wake him up just before they reach the island.

That’s kind of hard to follow, isn’t it ?

Let’s see if we can make some sense of it with a basic RASCI diagram ?

Responsibility Matrix: Rowing to the Island
Activity	Responsible	Accountable	Supportive	Consulted	Informed
Person	John	Mary	Sue	James	Kevin
Role	Oarsman	Captain	Consultant	Navigator	Sleeper

Now let’s add a simple timeline ?

Responsibility Matrix: Rowing to the Island
?	Sue	John	Mary	James	Kevin
Gives Direction	?	?	A	?	?
Rows the Boat	?	R	?	?	?
Provides Advice	S	?	?	?	?
Announces Arrival	?	?	A	C	?
Surfaces From Sleep	?	?	?	C	I
Ties Boat to Tree	?	?	A	?	?

Things are more complicated in reality ?

Quite correct. Although if I had jumped in at the detail end I might have lost you. Here?s a more serious example.

There?s absolutely no necessity for you so examine the diagram in any detail, other to note the method is even more valuable in large, corporate environments. This one is actually a RACI diagram because there are no supportive roles (which is the way the system was originally configured).

Other varieties you may come across include PACSI (perform, accountable, control, suggest, inform), and RACI-VS that adds verifier and signatory to the original mix. There are several more you can look at Wikipedia if you like.

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Uncover hidden opportunities with energy data analytics

What springs to mind when you hear the words energy data analytics? To me, I feel like energy data analytics is not my thing. Energy data analytics, however, is of great importance to any organisation or business that wants to run more efficiently, reduce costs, and increase productivity. Energy efficiency is one of the best ways to accomplish these goals.

Energy efficiency is not about investment in expensive equipment and internal reorganization. Enormous energy saving opportunities is hidden in already existing energy data. Given that nowadays, energy data can be recorded from almost any device, a lot of data is captured regularly and therefore a lot of data is readily available.

Organisations can use this data to convert their buildings’ operations from being a cost centre to a revenue centre through reduction of energy-related spending which has a significant impact on the profitability of many businesses. All this is possible through analysis and interpretation of data to predict future events with greater accuracy. Energy data analytics therefore is about using very detailed data for further analysis, and is as a consequence, a crucial aspect of any data-driven energy management plan.

The application of Data and IT could drive significant cost savings in company-owned buildings and vehicle fleets. Virtual energy audits can be performed by combining energy meter data with other basic data about a building e.g. location, to analyse and identify potential energy savings opportunities. Investment in energy dashboards can further enable companies to have an ongoing look at where energy is being consumed in their buildings, and thus predict ways to reduce usage, not to mention that energy data analytics unlock savings opportunities and help companies to understand their everyday practices and operating requirements in a much more comprehensive manner.

Using energy data analytics can enable an organisation to: determine discrepancies between baseline and actual energy data; benchmark and compare previous performance with actual energy usage. Energy data analytics also help businesses and organisations determine whether or not their Building Management System (BMS) is operating efficiently and hitting the targeted energy usage goals. They can then use this data to investigate areas for improvement or energy efficient upgrades. When energy data analytics are closely monitored, companies tend to operate more efficiently and with better control over relevant BMS data.

FUJIFILM Cracks the Energy Code

FUJIFILM was in trouble at its Dayton, Tennessee plant in 2008 where it produced a variety of speciality chemicals for industrial use. Compressed-air breakdowns were having knock-on effects. The company decided it was time to measure what was happening and solve the problem. It hoped to improve reliability, cut down maintenance, and eliminate relying on nitrogen for back-up (unless the materials were flammable).

The company tentatively identified three root causes. These were (a) insufficient system knowledge within maintenance, (b) weak spare part supply chain, and (c) generic imbalances including overstated demand and underutilised supply. The maintenance manager asked the U.S. Department of Energy to assist with a comprehensive audit of the compressed air system.

The team began on the demand side by attaching flow meters to each of several compressors for five days. They noticed that – while the equipment was set to deliver 120 psi actual delivery was 75% of this or less. They found that demand was cyclical depending on the production phase. Most importantly, they determined that only one compressor would be necessary once they eliminated the leaks in the system and upgraded short-term storage capacity.

The project team formulated a three-stage plan. Their first step would be to increase storage capacity to accommodate peak demand; the second would be to fix the leaks, and the third to source a larger compressor and associated gear from a sister plant the parent company was phasing out. Viewed overall, this provided four specific goals.

Improve reliability with greater redundancy
Bring down system maintenance costs
Cut down plant energy consumption
Eliminate nitrogen as a fall-back resource

They reconfigured the equipment in terms of lowest practical maintenance cost, and moved the redundant compressors to stations where they could easily couple as back-ups. Then they implemented an online leak detection and repair program. Finally, they set the replacement compressor to 98 psi, after they determined this delivered the optimum balance between productivity and operating cost.

Since 2008, FUJIFILM has saved 1.2 million kilowatt hours of energy while virtually eliminating compressor system breakdowns. The single compressor is operating at relatively low pressure with attendant benefits to other equipment. It is worth noting that the key to the door was measuring compressed air flow at various points in the system.

ecoVaro specialises in analysing data like this on any energy type.?

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