Travel time optimisation is one of the most crucial steps to keeping operational costs under control while expanding their scope with the same volume of resources. It usually starts with applying a route optimisation solution with the goal to find the perfect work optimisation formula. Route planning for complex schedules with hundred of resources assigned to different entities is a very tricky game. That’s why a serious thought is required in the process of optimising complex work models and keep costs and inefficiencies under control in the mean time.

So how can we save time, money and efforts by optimising the total travelling time between field jobs?

Route optimisation 

When we talk about optimising the travelling time between jobs, we mean applying a route optimisation solution for finding the most cost-efficient route – not only from point A to point B but also to point C, D, E, etc. And if needed, we change the order jobs to fit more completed tasks in a day.

Google maps can indeed do (and has done) route optimisation for almost every person with access to the internet. But when we mean complex jobs like field inspections and manage hundreds of teams, we should start looking to an end-to-end work optimisation systems. And that goes far beyond the functions of google maps.

Related: Where resource allocation optimisation and remote inspections intersect?

The hidden benefits of route optimisation

We already said why it is crucial for companies to use route optimisation – to reduce efforts, costs and time. But there are also hidden benefits that are nonetheless important. To name just a few of them:

  • Improving field service for the end customer: increasing the number of daily completed jobs is going to shorten the response time. Therefore it will bring more satisfied customers. 
  • Improving employee productivity: reducing the travelling time of inspectors, technicians or other entities to improve their focus and motivation.

We already said that it takes more than google maps to optimise complex operations with many variables. If we have to plan 10 different field inspection today to 10 locations, we will have to open a file and start filling in the details and then assign the tasks to relevant inspectors.

Manually designing a conflict-free schedule will be a very time-consuming endeavour. But throwing all of this data into an electronic inspection management system and then leave the AI engine automatically to make an optimal schedule will take just seconds.

route otpimisation software

What science is used for efficient inspection scheduling with optimised routes?

Certain factors define how long an inspection will take – the complexity of the case, the distance between a job and an inspector, the available technical devices (if needed), the location of the warehouse (if there’s such), the skill set of the inspector, etc. Based on these factors is created a route between the 10 inspection jobs and the assigned inspectors.

This example serves as a good case for regulators and organisations that are performing field inspections to understand how route optimisation can help them. Their specific objectives often require error-free inspection scheduling that will send qualified inspectors to relevant jobs with minimised travelling time. That’s why the skill set is a crucial variable in the route optimisation formula. Based on our expertise with regulators, we refined this formula and offer it as a successful model to our customers from the regulatory sector. Read the case study now.

But when we talk about resource optimisation in other industries, the business objectives will be different. It means that new variables will be put into the equation to find the most suitable route optimisation formula. Canalix dedicated a significant amount of time to develop a suitable proof-of-concept to mirror a real-world scenario. That’s the way we help our new customers find a working route optimisation formula that serves their business objectives. Request a product tour here.

How route optimisation is implemented into field operations?

Canalix offers a resource optimisation solution that can help field service providers with route planning and safety management. Our track record includes regulators that successfully use our solution as an inspection optimisation tool. If you want to learn more about our resource optimisation system, book your free demo with us today.

“The robots will replace humans and there will be no jobs for people”. In the distant past the unknown was a source for imagining evil powers that threaten everything that humans created. Today the new technologies serve as the new unknown and we create myths about them too. The myths have always been here to help us invent villains. But with more knowledge at our hands, we also got better at debunking them.

AI technologies are making its way in all levels of governments and enterprises today. That’s why it’s more and more important for CIOs to understand the value of AI without making wrong assumptions, based on myths. Are you ready to bust a few myths about AI with us?

Let’s start with myth #1:

AI can replace human thinking?

Artificial intelligence is called artificial for a reason. It can replace human intelligence up to a point. AI can learn how to execute tasks, but if the conditions of this task change, then AI will fail. To say that AI will replace human thinking sounds more like a Terminator movie plot and less like reality.

AI can learn things independently of human touch

AI technologies do not learn on their own. They need human control. The AI needs updates, constant integration of new knowledge, etc. That’s why when choosing an AI boosted software service, CIOs must pay close to attention to the technological state of the product they will use.

Related: Guide for finding the best inspection management software

AI makes decisions independently of humans

AI is data & rules-driven technology. Rules are defined by human experts. While the AI can independently solve simple tasks, based on pre-defined rules, sometimes there is complexity that is far beyond the capacity of AI. This is where human involvement is needed. We have a perfect example for the way AI can transfer the decision-making to a human party:

Let’s imagine that a citizen is filing an appeal for review to a government agency that is governing the field inspections. While the citizen files the data of his appeal on the front-end of the Agency’s website, on the back-end an AI is trying to categorise the complexity of the case based on the gathered data.

With a well defined set of rules, the AI would be able to set a relevant complexity score. If the case is too complex (above certain score) it will be send for review by human. If the case is simple enough for automatic processing through the AI, then it will be automatically allocated to an inspector and the inspection scheduling and execution will happen in the most optimal way.

Related: Learn more about inspection management and AI complexity score

We worked so far so good, so we don’t need AI

AI is not a magic that will instantly improve the business outcomes within an organization. Whether your company needs or does not need AI, it should be a decision based on data. In other worlds, every CIO must be able to answer why his organization does or does not need AI.

Having visibility on technologies and knowing how they can or can’t help on the strategy of the organization is important. The business needs are ever evolving and the decisions that are made today may not apply for tomorrow’s technological landscape. That’s why no matter where a government agency or an enterprise may stand in terms of adopting AI, they must have their research done.

Debunking myths is easy. Staying always alert to how technologies evolve is hard. Do you want to follow what’s new with AI and inspection software? Sign up for our newsletter.

Contact us now.

The traditional vs the modern is a conflict as old as the world. It has a very visible implication in the inspection driven enforcement activity of the public sector. In the regulatory environment this conflict is formulated like: digital inspection solutions vs old traditional inspection methods.

The regulatory bodies that use traditional methods for inspection are facing an expensive and clumsy process. On the other hand the nature of inspections is always to some degree risky for the success of the whole operation. This risk can increase the cost of inspection process. For example there could be a mistake in the allocation of cases, allowing wrongfully skilled inspectors send on site or some other mistake that could impact the cost.

We’ve already looked through variety of success stories of smart cities that embraced digital transformation. They are winners on the side of the modern and adopters of the optimization through disruptive tech solutions. These solution are doing their magic on different levels at the same time. We already talked about how data automation is an essential level of operation for the inspection software. In this article we will look through AI optimization as another feature that is a game changer for the inspection driven regulatory enforcement.

Related: Read why data is the ground base for digital transformation. An example with the top five smart cities in the world for 2019.

So, how is AI changing the game?

AI optimises the resource utilisation in inspections

Artificial Intelligence can design the most optimal ways for inspections. It can learn from the existing data and eliminate mistakes like sending wrongfully skilled inspectors on site or planning routes in nonoptimal ways. According to the user data of Canalix, the AI optimization can bring up to 40% better resource utilisation.

AI enables more efficient data flow

When the amount of data is managed intelligently, the business operations are improving. Even if there are potential issues, they can be evaded before affecting the whole outcome. The bottom line is that AI can literally shrink the costs of inspection.

AI eases the decision-making process

The reducing of process time is like the elephant in the room when we talk about digital transformation. So it is with the AI driven inspection platforms. The intelligence of the system can take into account different factors at the same time and eliminate the unnecessary work. This improves the efficiency and at the end of the day decision are made faster. The benefit goes not only to one individual, who is directly involved in the inspection process, but to the whole inspection ecosystem.

A good business optimisation tool will give you the digital infrastructure to do your job better. A great optimization platform will bring data automation and AI optimisation to table. Combine it with a killer UX and you will have a disruption technology in your hands.

Do you want to try how Canalix can optimise regulatory inspections? Contact us now.

Bonus definition of AI by Salesforce:

Artificial Intelligence is the concept of having machines “think like humans” — in other words, perform tasks like reasoning, planning, learning, and understanding language.