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.

rasci

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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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Finding the Best Structure for Your Enterprise Development Team

An enterprise development team is a small group of dedicated specialists. They may focus on a new business project such as an IoT solution. Members of microteams cooperate with ideas while functioning semi-independently. These self-managing specialists are scarce in the job market. Thus, they are a relatively expensive resource and we must optimise their role.

Organisation?Size and Enterprise Development Team Structure

Organisation structure depends on the size of the business and the industry in which it functions. An enterprise development team for a micro business may be a few freelancers burning candles at both ends. While a large corporate may have a herd of full-timers with their own building. Most IoT solutions are born out of the efforts of microteams.

In this regard, Bill Gates and Mark Zuckerberg blazed the trail with Microsoft and Facebook. They were both college students at the time, and both abandoned their business studies to follow their dreams. There is a strong case for liberating developers from top-down structures, and keeping management and initiative at arm?s length.

The Case for Separating Microteams from the?Organisation

Microsoft Corporation went on to become a massive corporate, with 114,000 employees, and its founder Bill Gates arguably one of the richest people in the world. Yet even it admits there are limitations to size. In Chapter 2 of its Visual Studio 6.0 program it says,

‘today’s component-based enterprise applications are different from traditional business applications in many ways. To build them successfully, you need not only new programming tools and architectures, but also new development and project management strategies.?

Microsoft goes on to confirm that traditional, top-down structures are inappropriate for component-based systems such as IoT solutions. We have moved on from ?monolithic, self-contained, standalone systems,? it says, ?where these worked relatively well.?

Microsoft’s model for enterprise development teams envisages individual members dedicated to one or more specific roles as follows:

  • Product Manager ? owns the vision statement and communicates progress
  • Program Manager ? owns the application specification and coordinates
  • Developer ? delivers a functional, fully-complying solution to specification
  • Quality Assurer ? verifies that the design complies with the specification
  • User Educator ? develops and publishes online and printed documentation
  • Logistics Planner ? ensures smooth rollout and deployment of the solution

Three Broad Structures for Microteams working on IoT Solutions

The organisation structure of an enterprise development team should also mirror the size of the business, and the industry in which it functions. While a large one may manage small microteams of employee specialists successfully, it will have to ring-fence them to preserve them from bureaucratic influence. A medium-size organisation may call in a ?big six? consultancy on a project basis. However, an independently sourced micro-team is the solution for a small business with say up to 100 employees.

The Case for Freelancing Individuals versus Functional Microteams

While it may be doable to source a virtual enterprise development team on a contracting portal, a fair amount of management input may be necessary before they weld into a well-oiled team. Remember, members of a micro-team must cooperate with ideas while functioning semi-independently. The spirit of cooperation takes time to incubate, and then grow.

This is the argument, briefly, for outsourcing your IoT project, and bringing in a professional, fully integrated micro-team to do the job quickly, and effectively. We can lay on whatever combination you require of project managers, program managers, developers, quality assurers, user educators, and logistic planners. We will manage the micro-team, the process, and the success of the project on your behalf while you get on running your business, which is what you do best.

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What Sub-Metering did for Nissan in Tennessee

When Nissan built its motor manufacturing plant in Smyrna 30 years ago, the 5.9 million square-foot factory employing over 8,000 people was state of art. After the 2005 hurricane season sky-rocketed energy prices, the energy team looked beyond efficient lighting at the more important aspect of utility usage in the plant itself. Let’s examine how they went about sub-metering and what it gained for them.

The Nissan energy team faced three challenges as they began their study. They had a rudimentary high-level data collection system (NEMAC) that was so primitive they had to transfer the data to spread-sheets to analyse it. To compound this, the engineering staff were focused on the priority of getting cars faster through the line. Finally, they faced the daunting task of making modifications to reticulation systems without affecting manufacturing throughput. But where to start?

The energy team chose the route of collaboration with assembly and maintenance people as they began the initial phase of tracking down existing meters and detecting gaps. They installed most additional equipment during normal service outages. Exceptions were treated as minor jobs to be done when convenient. Their next step was to connect the additional meters to their ageing NEMAC, and learn how to use it properly for the first time.

Although this was a cranky solution, it had the advantage of not calling for additional funding which would have caused delays. However operations personnel were concerned that energy-saving shutdowns between shifts and over weekends could cause false starts. ?We’ve already squeezed the lemon dry,? they seemed to say. ?What makes you think there?s more to come??

The energy team had a lucky break when they stumbled into an opportunity to prove their point early into implementation. They spotted a four-hourly power consumption spike they knew was worth examining. They traced this to an air dryer that was set to cyclical operation because it lacked a dew-point sensor. The company recovered the $1,500 this cost to fix, in an amazing 6 weeks.

Suitably encouraged and now supported by the operating and maintenance departments, the Smyrna energy team expanded their project to empower operating staff to adjust production schedules to optimise energy use, and maintenance staff to detect machines that were running without output value. The ongoing savings are significant and levels of shop floor staff motivation are higher.

Let’s leave the final word to the energy team facilitator who says, ?The only disadvantage of sub-metering is that now we can’t imagine doing without it.?

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Why Predictive Maintenance is More Profitable than Reactive Maintenance

Regular maintenance is needed to keep the equipment in your facility operating normally. All machinery has a design lifespan, and your goal is to extend this as long as possible, while maintaining optimal production levels. How you go about the maintenance matters, from routine checks to repairing the damaged component parts?all before the whole unit needs to be tossed away and a new one purchased and installed. Here, we will break down the different approaches used, and show you why more industries and businesses are turning to proactive maintenance modes as opposed to the traditional reactive approaches for their?field service operations.?

Reactive Maintenance: A wait and see game

Here, you basically wait for a problem to occur, then fix it. It’s also commonly referred to as a “Run-to-Failure” approach, where you operate the machines and systems until they break. Repairs are then carried out, restoring it to operational condition.?

At face value, it appears cost-effective, but the reality on the ground is far much different. Sure, when the equipment is new, you can expect minimal cases of maintenance. During this time, there?ll be money saved. However, as time progresses there?ll be increased wear, making reliance on a reactive maintenance approach a costly endeavour. The breakdowns are more frequent, and inconsistent as well. Unplanned expenses increase operational costs, and there will be lost productivity during the periods in which the affected machinery won’t be in operation.?

While reactive maintenance makes sense when you’re changing a faulty light bulb at home, things are more complicated when it comes to dealing with machinery in industries, or for those managing multiple residential and commercial properties. For the light bulb, it’s easier to replace it, and failure doesn’t have a ripple effect on the rest of the structures in the household. For industries, each time there is equipment failure, you end up with downtime, production can grind to a halt, and there will be increased environmental risks during equipment start-up and shutdown. If spare parts are not readily available, there will be logistical hurdles as you rush the shipping to get the component parts to the facility. Add this to overworked clients in a bit to complete the repair and to make up for lost hours and delayed customer orders.

For field service companies, more time ends up being spent. After all, there?s the need of knowing which parts needed to be attended to, where they are, and when the servicing is required. Even when you have a planned-out schedule, emergency repairs that are required will force you to immediately make changes. These ramps up the cots, affecting your operations and leading to higher bills for your client. These inconveniences have contributed to the increased reliance on?field service management platforms that leverage on data analytics and IoT to reduce the repair costs, optimise maintenance schedules, and?reduce unnecessary downtimes?for the clients.

Waiting for the machinery to break down actually shortens the lifespan of the unit, leading to more replacements being required. Since the machinery is expected to get damaged much sooner, you also need to have a large inventory of spare parts. What’s more, the damages that result will be likely to necessitate more extensive repairs that would have been needed if the machinery had not been run to failure.?

Pros of reactive maintenance

  1. Less staff required.
  2. Less time is spent on preparation.

Cons of reactive maintenance

  1. Increased downtime during machine failure.
  2. More overtime is taken up when conducting repairs.
  3. Increased expenses for purchasing and storing spare parts.?
  4. Frequent equipment replacement, driving up costs.?

This ?If it ain’t broke, don’t fix it? approach leads to hefty repair and replacement bills. A different maintenance strategy is required to minimise costs. Proactive models come into focus. Before we delve into predictive maintenance, let’s look at the preventive approach.?

Preventive Maintenance: Sticking to a timetable

Here, maintenance tasks are carried out on a planned routine?like how you change your vehicle?s engine oil after hitting a specific number of kilometres. These tasks are planned in intervals, based on specific triggers?like a period of time, or when certain thresholds are recorded by the meters. Lubrication, carrying out filter changes, and the like will result in the equipment operating more efficiently for a longer duration of time. While it doesn’t completely stop catastrophic failures from occurring, it does reduce the number of failures that occur. This translates to capital savings.??

The Middle Ground? Merits And Demerits Of Preventive Maintenance

This periodic checking is a step above the reactive maintenance, given that it increases the lifespan of the asset, and makes it more reliable. It also leads to a reduced downtime, thus positively affecting your company?s productivity. Usually, an 80/20 approach is adopted,?drawing from Pareto’s Principle. This means that by spending 80% of time and effort on planned and preventive maintenance, then reactive maintenance for those unexpected failures that pop up will only occur 20% of the time. Sure, it doesn’t always come to an exact 80/20 ratio, but it does help in directing the maintenance efforts of a company, and reducing the expenses that go into it.?

Note that there will need to be a significant investment?especially of time, in order to plan a preventive maintenance strategy, plus the preparation and delegation of tasks. However, the efforts are more cost effective than waiting for your systems and machinery to fail in order to conduct repairs. In fact, according to the US Dept. of Energy, a company can save between 12-18 % when using a preventive maintenance approach compared to reactive maintenance.

While it is better than the purely reactive approach, there are still drawbacks to this process. For instance, asset failure will still be likely to occur, and there will be the aspect of time and resource wastage when performing unneeded maintenance, especially when technicians have to travel to different sites out in the field. There is also the risk of incidental damage to machine components when the unneeded checks and repairs are being carried out, leading to extra costs being incurred.

We can now up the ante with predictive maintenance. Let’s look at what it has to offer:

Predictive Maintenance: See it before it happens

This builds on preventive maintenance, using data analytics to smooth the process, reduce wastage, and make it more cost effective. Here, the maintenance is conducted by relying on trends observed using data collected from the equipment in question, such as through vibration analysis, energy consumption, oil analysis and thermal imaging. This data is then taken through predictive algorithms that show trends and point out when the equipment will need maintenance. You get to see unhealthy trends like excessive vibration of the equipment, decreasing fuel efficiency, lubrication degradation, and their impact on your production capacities. Before the conditions breach the predetermined parameters of the equipment’s normal operating standards, the affected equipment is repaired or the damaged components replaced.??

Basically, maintenance is scheduled before operational or mechanical conditions demand it. Damage to equipment can be prevented by attending to the affected parts after observing a decrease in performance at the onset?instead of waiting for the damage to be extensive?which would have resulted in system failure. Using?data-driven?field service job management software will help you to automate your work and optimise schedules, informing you about possible future failures.

Sensors used record the condition of the equipment in real time. This information is then analysed, showing the current and future operational capabilities of the equipment. System degradation is detected quickly, and steps can be taken to rectify it before further deterioration occurs. This approach optimises operational efficiency. Firstly, it drastically reduces total equipment failure?coming close to eliminating it, extending the lifespan of the machinery and slashing replacement costs. You can have an orderly timetable for your maintenance sessions, and buy the equipment needed for the repairs. Speaking of which, this approach minimises inventory especially with regards to the spare parts, as you will be able to note the specific units needed beforehand and plan for them, instead of casting a wide net and stockpiling spare parts for repairs that may or may not be required. Repair tasks can be more accurately scheduled, minimising time wasted on unneeded maintenance.??

Preventive vs Predictive Maintenance?

How is predictive different from preventive maintenance? For starters, it bases the need for maintenance on the actual condition of the equipment, instead of a predetermined schedule. Take the oil-change on cars for instance. With the preventive model, the oil may be changed after every 5000?7500 km. Here, this change is necessitated because of the runtime. One doesn’t look at the performance capability and actual condition of the oil. It is simply changed because “it is now time to change it“. However, with the predictive maintenance approach, the car owner would ideally analyse the condition of the oil at regular intervals- looking at aspects like its lubrication properties. They would then determine if they can continue using the same oil, and extend the duration required before the next oil change, like by another 3000 kilometres. Perhaps due to the conditions in which the car had been driven, or environmental concerns, the oil may be required to be changed much sooner in order to protect the component parts with fresh new lubricant. In the long run, the car owner will make savings. The US Dept. of Energy report also shows that you get 8-12% more cost savings with the predictive approach compared to relying on preventive maintenance programs. Certainly, it is already far much more effective compared to the reactive model.?

Pros of Predictive Maintenance

  1. Increases the asset lifespan.
  2. Decreases equipment downtime.
  3. Decreases costs on spare parts and labour.
  4. Improves worker safety, which has the welcome benefit of increasing employee morale.
  5. Optimising the operation of the equipment used leads to energy savings.
  6. Increased plant reliability.

Cons of Predictive Maintenance

  1. Initial capital costs included in acquiring and setting up diagnostic equipment.
  2. Investment required in training the employees to effectively use the predictive maintenance technology adopted by the company.

The pros of this approach outweigh the cons.?Independent surveys on industrial average savings?after implementing a predictive maintenance program showed that firms eliminated asset breakdown by 70-75%, boosted production by 20-25%, and reduced maintenance costs by 25-30%. Its ROI was an average of 10 times, making it a worthy investment.

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