Why & How to Improve Process Efficiency in Your Business

Process efficiency can make or break your business.

Want proof? Just look at Ford and McDonald’s. When Henry Ford reduced the amount of time it took to assemble vehicles by more than 75%,  he revolutionized the auto industry and ensured the success of Ford Motor Company. McDonald’s later became a global leader in fast food by perfecting the efficiency of their meal assembly design and passing the cost savings on to customers. 

On the other side of the same token, inefficient processes cost NASA more than $193 million when they lost the Mars Climate Orbiter in 1999. NASA’s post-mission failure report explained that “the root cause [a conversion discrepancy between metric and standard calculations] was not caught by the processes in place.”

From large systems to small calculations, your business could be losing big to process inefficiency – and fixing it could make you a huge success.

 

What Is Process Efficiency?

 

Process efficiency is the ratio of output resulting from a process compared to the time and resources required to carry out that process. More efficient processes use fewer resources. For example, if it takes 10 employees two hours to make one pizza, the process for making pizzas is extremely inefficient.

 

How to Identify and Solve for General Process Inefficiencies 

 

Whether you’re delivering pizzas, manufacturing medical devices, or maintaining Jacuzzis, you can assess the efficiency of your business process by asking yourself a few simple questions. These questions will help you find inefficiencies and figure out how to correct them.

 

 

Is there technology that can make this process faster?

Many of our OptimoRoute customers have come to us after asking themselves this very question. Manually planning routes takes an average of eight times longer than planning routes with OptimoRoute. Our software has enabled logistics professionals and managers to increase efficiency and focus more time on strategic planning.

New apps, software, and mechanical systems that can help businesses get jobs done faster and more efficiently are popping up all the time. You don’t necessarily have to adopt new technology, though. Sometimes, you can increase efficiency by simply utilizing more of the features within the technology you already use. For example, many small business owners waste time re-creating similar emails, forms, and reports. Creating a template for documents that have repetitive components can save a lot of time.

 

Is this process using a lot of resources?

If a process is using a large volume of resources (work hours, tools, supplies), you should assess every part of that process for areas where it could be improved. Is a task taking you 30 minutes, but you could do it in 10 if you weren’t constantly being interrupted? Sometimes improving efficiency is as simple as silencing notifications on internal communications for 10 minutes.

This question can help you find solutions to more complex processes that are using multiple types of resources, too. Let’s look at our client, Hardie’s Fresh Foods, as an example. Hardie’s is a food distribution company in Texas with a fleet of 160 refrigerated trucks. Their delivery process was vital for their business but was using a lot of resources (gas, trucks, drivers, etc.).

Hardie’s Fresh Foods improved their delivery process efficiency by using route optimization software. Planning routes and scheduling with OptimoRoute reduced their mileage by 20%, driver costs by 12%, and increased their delivery capacity by 14%. That’s a huge improvement in their delivery process efficiency. Hardie’s improved their customer satisfaction, too. Their on-time delivery rate is currently at 94% and has been rising at a rate of 1.5% MoM since they started using OptimoRoute.

Is any part of this process outdated?

As technology and customer behaviors evolve, your business can fall behind if you don’t regularly assess process efficiency. This doesn’t just mean ensuring your current processes are operating at maximum efficiency. It also means staying up to date with current market trends and customer expectations.

Something you did well 10 years ago may be too slow or too inefficient to do now, or it may not provide a great customer experience anymore. Just look at Blockbuster Video. Blockbuster’s failure to adapt to a changing market led to their untimely demise in 2010 when they were toppled by Netflix’s new streaming and DVD mailing services. Keeping an eye on market trends, conducting customer surveys, and understanding your clients’ needs will help you keep your processes up to date.

Is there a better way?

This is the root question every business needs to ask if they want to make sure their processes are efficient. Striving for continuous improvement will set you up for long-term success.

Ask this question about every single process and ask it often. If the answer is yes, find a better way and implement it. Fast. Finding a better way can be as small as helping one manager do their job faster with software or as large as completely overhauling an assembly line to incorporate new technology.

 

4 Services That Help Improve Process Efficiency

Luckily, there are lots of products and services designed specifically to help businesses operate more efficiently. Here are a few services designed to improve efficiency and give businesses a competitive advantage:

1. OptimoRoute: Route planning and scheduling

What is it? OptimoRoute is route optimization software.

Who is it meant for? Businesses of all sizes that need to manage workers in the field. This includes pickup and delivery services, maintenance or service companies, and long-haul trucking companies.

What are the benefits? OptimoRoute makes the entire process of scheduling and route planning eight times faster and requires 60% fewer working hours, so managers can focus on strategy.

With OptimoRoute, you can:

·         Calculate routes for multiple drivers and hundreds of orders with multiple constraints in seconds

·         Plan routes up to five weeks at a time

·         Quickly adjust schedules and routes if orders change or a driver calls in sick

·         See where drivers and workers are along their route with live tracking

·         Keep your customers up-to-speed and make sure they’re available at the right time with real-time order tracking and ETA notifications 

2. Teampay: Managing team spend

What is it? Teampay is distributed spend management software.

Who is it meant for?  Businesses of all sizes with multiple team members who need to pay for things like gas, supplies, or professional development with company funds. 

What are the benefits? Teampay enables employees to buy the things they need to do their job without having to wait around for supervisor approval or to get reimbursed. It also saves leadership and project management personnel time by reducing the number of small tasks they need to do during budgeting.

With Teampay, you can:

·         Automate parts of the spending approval process

·         Complete essential business purchases within your usual workflow 

·         Set and customize spending limitations

·         Automatically upload purchase data to the general ledger 

·         Know exactly why purchases were made and avoid having to track down employees to ask for details

3. Eduflow: Training and onboarding

What is it? Eduflow is a platform for creating customized learning, training, and onboarding courses.

Who is it meant for?  HR and onboarding teams as well as businesses that need to train employees on internal processes.

What are the benefits? Eduflow streamlines training and educational processes. It is also a valuable onboarding tool. A 2007 study conducted by The Wynhurst Group found that “new employees who went through a structured onboarding program were 58% more likely to be with the organization after three years.”

With Eduflow, you can:

·         Create onboarding checklists

·         Simplify the learning process for new hires and save managers/onboarding teams time

·         Increase your new hire retention rate

4. Adpresso: Digital advertising

What is it? AdEspresso is digital advertising management software.

Who is it meant for?  Businesses and professionals who need to create, manage, and/or track their ads across digital platforms, such as Facebook, Google, and email campaigns.

What are the benefits? AdEspresso allows you to easily manage digital marketing across multiple platforms from one central place.

With AdEspresso, you can:

·         Clearly see which ads and platforms are performing better (or worse)

·         Save time switching from platform to platform

·         Drive more profitable marketing campaigns for your business

·         Create ads for Facebook, Instagram, and Google Ads, and split test (test multiple versions of an ad) across platforms

 

Harness the Power of Process Efficiency

Making just one process more efficient can positively impact your bottom line and give your business more freedom. Just consider the success of Ashley Furniture. The furniture giant uses 3D printers to manufacture smaller parts and prototypes in-house. Integrating cutting-edge technology on their production floor has saved them time, money, and given them more freedom to test out new prototypes.

So, ask yourself: Where can my business increase process efficiency? The answer to that one question could yield incredible results.

This article originally appeared on optimoroute.com.

Work Orders: Definition, Examples, and How to Complete Yours Faster

Maintenance, while one of the most important parts of any business, has been slow to evolve. For most companies, work orders are handled the same way they were fifty years ago.

75% of companies still use paper, spreadsheets, or nothing at all to manage maintenance tasks and work orders. In a digital business landscape, this can create a bottleneck that leads to unhappy customers and costly production downtime.

With the right software solutions, you can optimize your work order process and remove obstacles to essential maintenance which could be keeping your business from working optimally.

What Is a Work Order?

A work order is a task order sent by a customer or organization, usually for a service technician.

A work order must be reviewed and accepted by the provider for a customer to receive an ETA and cost or time estimate. 

Many industries use work orders to manage maintenance and repairs. A work order is often the result of a routine inspection where the inspector finds issues.

The work order pad of the past

Before the digital revolution, many companies handled their work orders using a snap-off (tearable) work order pad.

Something like this:

The inspector or maintenance worker would bring a pad like this one to inspections and write a work request on-site. Managers would get a copy for bookkeeping purposes.

But those days are over.

Work order management software: the modern solution

Instead of a physical pad, modern companies use work order management software to handle their maintenance workflows.

With it, your business can share private request forms with external clients or internal workers as well as easily create new work orders digitally.

You can then handle the work order online and keep track of all projects in a single dashboard, making it easy to follow up on any project.

 

Who Uses Work Orders?

Work orders are used across a lot of industries in which ongoing maintenance or small jobs are a part of the client relationship.

Field service companies

Field service companies use work orders to efficiently stay in control across a wide variety of maintenance tasks that need to be done for their clients. 

Facility management companies

Facility management companies use work orders to track expenses related to maintaining client buildings.

Technicians

Technicians, like electricians and field engineers, use work orders to track expenses and working hours for specific projects.

Manufacturers

Manufacturing companies mainly use work orders for internal service requests and to track the status of repairs and expenses.

Managed computer services

Managed computer services companies use work orders to track client issues and downtime as well as managing their computer techs and expenses.

General contractors

General contractors can use work orders to handle the expenses and workflow for every part of a building project.

Within a company, the work orders may come externally from clients or internally from technicians during an inspection.

For example, a customer of a building management company might use a work order for maintenance requests for a broken sink or AC.

In manufacturing, a plant manager might find a faulty robot arm and send in a work order to ensure the plant keeps running smoothly.

Final Thoughts

For a small business with only a few customers, filing and managing work orders might not be your biggest problem to solve.

But once you hit a certain scale, any field service company is going to struggle. 

Maximizing the number of work orders your existing team can handle will help you maintain healthy profit margins, keep customers happy, and drive more revenue.

This article originally appeared on optimoroute.com.

AI and ML: Are they one and the same?

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As children we believed in magic, imagined superpowers and a fantasy where robots would one day follow our commands, undertaking our most meager tasks and even help with our homework at the push of a button! But sadly it always seemed that these beliefs, along with the idea of self-driven aero cars and jetpacks, belonged in a future beyond our imagination or in a Hollywood Sci-fi. Would we ever get to experience the future in our lifetime?

But then it arrived! Artificial Intelligence, aka AI, made its debut in real life and became the buzz word of the 21^st century, providing us with new ideas to explore and incredible possibilities. And just as we were getting used to AI we were introduced to Futuristic Learning, Deep Learning, NLP and another term we often confuse with AI: Machine Learning (ML). Whew!

Suddenly the future is well and truly here, and it’s hard to keep up with the advancement of these technologies, what each term means and how they relate to one another – particularly when it comes to AI and ML, which are often perceived as interchangeable.

But while AL and ML fall into the same domain, they are significantly different – with each having a specific application and outcome. And as more and more businesses start to question whether these tools may benefit them, we thought it was time to get to the bottom of what makes them different.

It all begins with AI.

According to John McCarthy, one of the Godfathers of AI, “AI is the science and engineering of making intelligent machines”.  We first saw AI in practice mid last century with the Turing Test – a chess experiment designed by mathematician Alan Turing that became the first time a computer defied human intelligence by defeating a human player in the game.

When looking at how ML fits in with AI, AI is the super set while ML is its subset. The latter is more dominantly used in areas with huge data sets encompassing the ‘3 Vs’ of Big Data: Volume, Velocity and Variety. AI, on the other hand, covers not only ML but also other branches including Natural Language Processing, Deep Learning, Computer Vision and Speech Recognition. Nevertheless, both AI and ML have one common goal: to achieve intelligence on a scale that defeats natural human intelligence.

Everything that has a smart system and is taking decisions based on inputted data can be considered an AI-driven machine – be it a car, door lock or even a refrigerator. AI can consist of everything from Good Old-Fashioned AI (GOFAI) all the way up to newer and advanced technologies like Deep Learning. Whenever a machine can “intelligently” complete a set of tasks based on some algorithms without human intervention, it is termed as artificial intelligence – for example identifying a series of steps to win a game or answer a generic question set by itself. AI machines are generally classified into three groups: Narrow, General and Super:

1. Artificial Narrow Intelligence or Weak AI is every intelligent task by machines that is programmed to do a single task, such as game of chess or even Siri, Google Assistant and other NLP processing tools.

2. Artificial General Intelligence or Strong AI are machines that mimic human intelligence to its core, making decisions and performing intellectual tasks that are driven by sentiments, emotions and general awareness of the environment.

3. Artificial Super Intelligence outdoes human intelligence in abstraction, creativity and wisdom. This is what Elon Musk and similar people are fearful of for controlling the world.

This brings us to the fact that we need more computing resources to handle the corpus of data which unfortunately is limited. Therefore, we need to work through a rule-based programming – hence the shift away from AI towards ML.

The rise of the machines.

A subset of AI, ML refers to machines that learn on account of some sort of prior knowledge – hence making them smarter and more likely to give results close to human intelligence. ML systems train a machine how to learn and apply decision making when encountered with new situations and are designed to get smarter over time. What started as AI is now leading major devices to adopt ML due to its likelihood to yield better results, and with the emergence of Big Data ML has gained speed and is now utilized by some of the world’s most powerful tech companies including Google, IBM, Baidu, Microsoft and Apple.

Tom M. Mitchell, a Computer Scientist and machine learning pioneer, has defined ML as: “The study of computer algorithms that allow computer programs to automatically improve through experience.” It focuses on making a machine or computer “learn” by providing it with a set of data and some predictions. Data is the fuel for machine learning and is to ML what code is to traditional computing.

Training a ML model requires giving algorithms a chunk of Big Data and one of the many learning models in order to extract processed, meaningful information – thus automating the process. It works for specific domains where we are creating models to detect or separate items, for example one fruit from a given set of fruits. Another example of its use is in manufacturing, whereby if you give input to a ML program with a large dataset of pictures of defects, along with their description, it should have the capacity to automize the data analysis of pictures at a later point in time. The model can find similar patterns in pictures with indicators as to where the defect might be by analysing the diverse dataset.

ML can be divided into three types: Supervised, Unsupervised and Reinforcement Learning.

1. Supervised Learning finds the relationship between the predicted output and input so that we can predict outputs for newer inputs based on our previous datasets. An example would be predicting the time when customers usually buy from an online store.

2. Unsupervised Learning has no label on the output or the data, meaning you’re unclear of the output of the model – it may be a wild guess. For example, a robot that serves as a housekeeper is trained to clean dust anywhere it finds it. It finds dust under the sofa more often than in other places, and thus trains itself to clean under it confidently.

3. Reinforcement Learning takes a similar approach as its name and inputs the results as a training model back into the system to improve it. Taking the same robot housekeeper example, the robot takes dust under the sofa as its input to improve the system.

Final thoughts.

Today we see AI applied to many areas of our daily lives – but it’s not as obvious to ‘see’ ML. How often do you access Google Home, Siri or Alexa? These are AI interactions between humans and machines – but it’s what’s behind these interactions that’s really interesting! They’re powered by training models and prediction systems of ML used by Netflix, YouTube, Facebook and Amazon.

ML has certainly been seized by marketers due to the opportunities afforded from being able to understand audiences at a micro level – but it’s also a term misused more than it should be, with the assumption that every AI system is also ML. If you compare AI and ML, you can clearly arrive at the conclusion that everything that uses human intelligence as a tool to mimic intelligent behaviors by machines can be termed as AI. But for that operation to be a ML tool too, one needs to use modeling techniques and a Big Data set to apply these techniques to.   

By understanding the key differences between AI and ML and the different opportunities each provides, businesses will have a better understanding of how – if at all – these tools can be applied in their operations

This article originally appeared on Makeen Technologies.