How AI Helps Improve Field Service

Its seems that with the current rate of technological innovation that these is something new every single day.  Therefore, you’re always looking forward to a new technological innovation that’s going to help you make your business operations more efficient and automated.

One of the most fascinating milestones in the field of technology is the integration of Artificial Intelligence (AI) in business. In one way or the other, AI gives a glimpse of machine supremacy that allows computers to perform tasks that were initially performed by humans. 

Are machines going to completely replace people in the workplace?

Of course, not.  Technologies like AI and Machine Learning are designed and meant to support employees in doing their tasks too boost their productivity.

AI is predominantly used to eliminate jobs and tasks that humans find boring, demotivating or monotonous. In some cases AI is also used to do jobs that are considered dangerous for humans to preform.

Previously the most common implementations for AI were all about gaming, entertainment, and advanced science,  now it’s spreading into a number of industries including the field service industry.

FieldElite – Field Service Software , can help you optimise the day-to-day operations of your business.

AI in field service management will enhance you business capabilities with:

  • Information Sharing
  • Real Time Updates
  • Automated Workflows
  • Digital Form Data Collection
  • Data Analysis

Improved Customer Service

For Service Based companies, customer retention is vital. Primarily because It can be 5-25 times more costly to acquire a new customer than it is to retain an existing ones.

Therefore customer retention should be a primary focus.? The good news is that by making use of AI you can implement services It can be 5-25 times more costly to acquire a new customer than it is to retain an existing one.

Staying on top of and ensuring you satisfactorily address and meet you customer demands and expectations can be a daunting task.? It can also be an expensive one,? especially for small field service based businesses like :

  • Heating & Plumbing Engineers
  • Electrical Contractors
  • Fire Safety Inspectors
  • HVAC Engineers
  • Facility Management
  • Building, Construction & Trade

Implementing Artificial Intelligence and Machine Learning to automate mundane and repetitive customer administration tasks will enable your staff to be free to provide additional value added tasks for your customers. Making your customers happier.

?Think about the active Chatbots. You can always get complaints directly from customers and address them right away.??

If at any point the customer is unhappy with your services, they can always raise the issue via the Chatbots. Since the bots contain necessary customer information, you can always get back to them and fix the issue at hand.?

With AI in field service, you can solve problems before they arise, or what is otherwise known as predictive maintenance,? In that way, you’ll have better customer relations because you’ll be able to address your customer concerns before they even become aware of them.

Improved Productivity

Scheduling tasks and managing the workforce isn’t a walk in the park. It goes beyond assigning tasks to your team members in the field and giving them deadlines to meet. Whether it’s a small firm or a big organisation, it’s quite difficult to organise the workforce.?

However, adopting Artificial Intelligence can iron out the difficulties most field organisations face in scheduling and managing tasks. Some years back, most firms relied on human intelligence to dispatch jobs to the right people based on given conditions. This was quite difficult, especially that it wasn’t always successful. But thanks to AI. With field service apps like FieldElite scheduling tasks and managing workforce is only a few clicks away.?

What’s more? There?s no room for error. Therefore, you’ll always match the right people for the job. Again, your team will always get tasks on time. That means, the job completion rate will go up, and hence the workforce becomes more productive.?

Predictive Maintenance

Usually, most business operations are based on ?solve the problem as it occurs?, which is just OK. However, it’s not always safe to wait until a problem occurs so that you solve it. Prevention is better than cure, and that’s why Artificial Intelligence comes handy in Field Service.

Using FieldElite Workforce Management Software , you don’t have to wait until something breaks.? Utilizing AI in field service enables you to proactively address field service needs and prevent unforeseen failures and interruptions.?

The ability to predict field service needs through field service apps like FieldElite enables you to make more accurate forecasts. In this way, resource planning is made easier, and as such, you’ll have smoothly running workflows. Again, by taking care of unforeseen circumstances in advance, you’re flexible enough to take care of the unexpected. And that means the overall productivity of your business will go up.

Job Management

Most field service jobs involve multiple stages that can take several days to complete. In addition to this, more often than not, you have to coordinate lots of equipment and contractors at the same time. All these can’t be achieved solely by human efforts. For more successful outcomes, it’s important to incorporate Artificial Intelligence in your field service operations.?

FieldElite is the field service solution that can help you manage sophisticated tasks. The app is packed with field service management tools that enable you to assign complicated tasks and keep track of your field techs. For long-cycle jobs, FieldElite app enables you to follow up on the activities going on the field to ensure they’re completed.?

With AI, there?s no room for error even when the jobs become more sophisticated.

Data Analysis

?

Field service industry involves lots of data. Some years back, organisations depended on human intelligence to analyse big data. Well, things still worked out, but as a human is to err, the outcome wasn’t always perfect. However, with Artificial Intelligence data analysis, 100% accuracy in data analysis is achievable. Field service solutions like FieldElite provide sophisticated data analytic tools that enable you to crack massive data and offer accurate solutions.?

FieldElite data analytics capabilities give you an insight into what’s not working and what needs to be improved. In that way, you can always address matters arising and take care of the loopholes.?

It’s time to go paperless with field management software like FieldElite if you?d like to make your business more profitable. Apart from improving the productivity of your workforce, incorporating AI in your business increases profitability. If you’re still doing your usual field rounds with a clipboard, it’s time to simplify your task with FieldElite app.?

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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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Saving Energy Step 2 ? More Practical Ideas

In my previous blog, we wrote about implementing a management system. This boils down to sharing a common vision up and down and across the organisation, measuring progress, and pinning accountability on individuals. This time, we would like to talk about simple things that organisations can do to shrink their carbon footprints. But first let’s talk about the things that hold us back.

When we take on new clients we sometimes find that they are baffled by what I call energy industry-speak. We blame this partly on government. We understand they need clear definitions in their regulations. It’s just a pity they don’t use ordinary English when they put their ideas across in public forums.

Consultants sometimes seem to take advantage of these terms, when they roll words like audit, assessment, diagnostic, examination, survey and review across their pages. Dare we suggest they are trying to confuse with jargon? We created ecoVaro to demystify the energy business. Our goal is to convert data into formats business people understand. As promised, here are five easy things your staff could do without even going off on training.

  1. Right-size equipment? outsource peak production in busy periods, rather than wasting energy on a system that is running at half capacity mostly.
  2. Re-Install equipment to OEM specifications ? individual pieces of equipment need accurate interfacing with larger systems, to ensure that every ounce of energy delivers on its promise.
  3. Maintain to specification ? make sure machine tools are within limits, and that equipment is well-lubricated, optimally adjusted and running smoothly.
  4. Adjust HVAC to demand ? Engineers design heating and ventilation systems to cope with maximum requirements, and not all are set up to adapt to quieter periods. Try turning off a few units and see what happens.
  5. Recover Heat ? Heat around machines is energy wasted. Find creative ways to recycle it. If you can’t, then insulate the equipment from the rest of the work space, and spend less money cooling the place down.

Well that wasn’t rocket science, was it? There are many more things that we can do to streamline energy use, and coax our profits up. This is as true in a factory as in the office and at home. The power we use is largely non-renewable. Small savings help, and banknotes pile up quickly.

What Kanban can do for Call Centre Response Times

When a Toyota industrial engineer named Taiichi Ohno was investigating ways to optimise production material stocks in 1953, it struck him that supermarkets already had the key. Their customers purchased food and groceries on a just-in-time basis, because they trusted continuity of supply. This enabled stores to predict demand, and ensure their suppliers kept the shelves full.

The Kanban system that Taiichi Ohno implemented included a labelling system. His Kanban tickets recorded details of the factory order, the delivery destination, and the process intended for the materials. Since then, Ohno?s system has helped in many other applications, especially where customer demand may be unpredictable.

Optimising Workflow in Call Centres
Optimising workflow in call centres involves aiming to have an agent pick up an incoming call within a few rings and deal with it effectively. Were this to be the case we would truly have a just-in-time business, in which operators arrived and left their stations according to customer demand. For this to be possible, we would need to standardise performance across the call centre team. Moving optimistically in that direction we would should do these three things:

  • Make our call centre operation nimble
  • Reduce the average time to handle calls
  • Decide an average time to answer callers

When we have done that, we are in a position to apply these norms to fluctuating call frequencies, and introduce ?kanbanned? call centre operators.

Making Call Centre Operations Nimble
The best place to start is to ask the operators and support staff what they think. Back in the 1960?s Robert Townsend of Avis Cars famously said, ?ask the people ? they know where the wheels are squeaking? and that is as true as ever.

  1. Begin by asking technical support about downtime frequencies, duration, and causes. Given the cost of labour and frustrated callers, we should have the fastest and most reliable telecoms and computer equipment we can find.
  1. Then invest in training and retraining operators, and making sure the pop-up screens are valuable, valid, and useful. They cannot do their job without this information, and it must be at least as tech-savvy as their average callers are.
  1. Finally, spruce up the call centre with more than a lick of paint to awaken a sense of enthusiasm and pride. Find time for occasional team builds and fun during breaks. Tele-operators have a difficult job. Make theirs fun!

Reducing Average Time to Handle Calls
Average length of contact is probably our most important metric. We should beware of shortening this at the cost of quality of interaction. To calculate it, use this formula:

Total Work Time + Total Hold Time + Total Post Call Time

Divided By

Total Calls Handled in that Period

Share recordings of great calls that highlight how your best operators work. Encourage role-play during training sessions so people learn by doing. Publish your average call-handling time statistics. Encourage individual operators to track how they are doing against these numbers. Make sure your customer information is up to date. While they must confirm core data, limit this so your operators can get down to their job sooner.

Decide a Target Time to Answer Calls
You should know what is possible in a matter of a few weeks. Do not attempt to go too tight on this one. It is better to build in say 10% slack that you can always trim in future. Once you have decided this, you can implement your Kanban system.

Introducing Kanban in Your Call Centre Operation
Monitor your rate of incoming calls through your contact centre, and adjust your operator-demand metric on an ongoing basis. Use this to calculate your over / under demand factor. Every operator should know the value on this Kanban ticket. It will tell them whether to speed up a little, or slow down a bit so they deliver the effort the call rate demands. It will also advise the supervisor when to call up reserves.

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