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

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


Resource optimization and business scheduling software

CASE STUDY: How a regulatory agency cut costs with resource scheduling software?
          • reducing the scheduling time with up to 75%
          • increasing efficiency of operations with 40%
          • fully eliminating errors in the resource allocation process.
Download the case study.


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 from point A to point B and 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 managing hundreds of teams, we should start looking to end-to-end work optimisation systems. And that goes far beyond the functions of google maps.

RelatedWhere do 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 enhance 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 ten different field inspections today to 10 locations, we will have to open a file, fill in the details, and 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 leaving the AI engine automatically to make an optimal schedule will take 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 inspector’s skill set, etc. Based on these factors, is created a route between the ten inspection jobs and the assigned inspectors.

This example serves as a good case for regulators and organisations performing field inspections to understand how route optimisation can help them. Their specific objectives often require error-free inspection scheduling to send qualified inspectors to relevant jobs with minimised travelling time. The skillset is a crucial variable in the route optimisation formula. Based on our expertise with regulators, we refined this formula and offered 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. New variables must enter the equation to find the most suitable route optimisation formula. Canalix dedicated a significant amount of time developing a suitable proof-of-concept to mirror a real-world scenario. That’s how we help our new customers find a working route optimisation formula that serves their business objectives. Request a product tour here.

How is route optimisation implemented into field operations?

Canalix offers a resource scheduling software to 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. Book your free demo with us today if you want to learn more about our resource optimisation system.

“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 an obvious implication for the public sector’s inspection-driven enforcement activity. This conflict is formulated in the regulatory environment like digital inspection solutions vs old traditional inspection methods.

The regulatory bodies that use traditional inspection methods face an expensive and clumsy process. The nature of inspections is always, to some degree, risky for the success of the whole operation. This risk can increase the cost of the inspection process. For example, there could be a mistake in allocating cases, allowing wrongfully skilled inspectors to be sent on-site or another error that could impact the inspection cost.

We’ve already looked through various success stories of regulators that transformed their inspections with different levels of inspection automation. They are winners on the side of the modern and successful regulatory tech adopters. Inspection automation technologies are doing their magic on different levels simultaneously. We already talked about how data automation is a basic level of operation for the inspection software. In this article, we will look through automated AI inspection optimisation as another feature that is a game-changer for inspection driven regulatory enforcement.

Related: How real-time data can improve the inspection performance?


Case study: Reduce operational costs with inspection scheduling software

Read it to understand:

    • The advantages of modular digital transformation
    • The vital architectural practices and technologies that enable modular transformation
    • How a regulatory agency in the UK is benefitting from a modular approach with Canalix.


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 nonoptimal ways. According to the user data of Canalix, AI optimisation 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 improve. Even if there are potential issues, they can be evaded before affecting the outcome. The bottom line is that AI can shrink the costs of inspection.

AI eases the decision-making process

The reduction 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 system’s intelligence can take into account different factors simultaneously and eliminate unnecessary work. This improves efficiency, and decisions are made faster at the end of the day. The benefit goes to one individual who is directly involved in the inspection process and the whole inspection ecosystem.

A good business optimisation tool will give regulators the digital infrastructure to do their job better. An excellent inspection platform will bring data automation and AI optimisation. Combine it with a great UX, and a regulator will have a disruptive technology in their 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, performing tasks like reasoning, planning, learning, and understanding language.

Request a demo or ask us more about adopting an automated AI inspection software through the form below: