The Cost of 15 Minutes

Have you ever thought about the value of 15 minutes in your crazy busy day?   This infographic quantifies the real magnitude of losing 15 minutes per day around common non-value added daily tasks in most DCs. Sound interesting? Contact voice@mountainleverage.com and let’s chat about about how we can save some time in your DC operations this year.

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The Cost of 15 Minutes
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What Causes Picking Errors?

by DOUG HOPPER, Fulfillment Process Expert

 

Recent increases in E-comm and B2C ordering patterns have put a spotlight on the impact that picking errors now present to operations managers.  At a minimum, the B2C shipper incurs a customer return with all the associated costs.  But all too frequently the single item mis-ship results in the loss of the next  sale.  Consumers typically do not return to shop at the seller who delivers the wrong item- or the right item in the wrong size, color or quantity.  In fact, they tell their friends and the damage is accurately measured with a multiplier nearly every time.  Even in the B2B environment, these errors typically carry a deduction against future invoices along with a correlating reduction in customer satisfaction, opening the door for competitive offerings – even in long-time established accounts.

The picking error that occurs in the distribution center is most often the result of using a sub-optimized process for order fulfillment.  The interaction between a picker and paper pick tickets, paper labels, RF handhelds or wearable devices, all have inherent distractions that take the picker’s attention away from the task at hand.  Errors occur when a picker is at the pick face with similar or like items stored side by side and must look away from the item/location in order to enter or read information from an RF device or paper ticket.

Voice picking technology removes this inherent distraction faced by all selectors in DCs by utilizing a conversation with the system of record (WMS, ERP, Inventory System) to keep the picker focused on picking the correct item and quantity every time.

Here are a few examples of improvements achieved with our voice-directed picking application:

  • One company achieved a reduction of 50% in returns after implementing voice, resulting in nearly $1.3 million of savings in the first year.  
  • At one manufacturer, their shipping errors dropped in the first month from an average of 500 per week to only 3.
  • One company achieved 99.99% order accuracy, noting only 1 error per 7,000 cases  and reduced overall QA staff by 75%.

Want to know more? Reach out and we can discuss if voice can help solve some of your challenges at voice@mountainleverage.com.

Doug Hopper

Doug is an in-house Fulfilment Process Expert at Mountain Leverage with over 20 years of experience running supply chain operations throughout the USA for Nestle Company.  He brings valuable insight and experience to today’s distribution challenges.

 

What Causes Picking Errors?
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Why Industrial Voice Crushes Siri, Google, and Alexa

By DAN PAUL, VP of Customer Success

 

Love it or hate it, speech recognition technology is now a part of our daily life.  With U.S. revenue growth projections of 16% year over year, expect to find yourself talking to more and more machines over the course of your day.  Most people have had experience with automated phone systems and/or the myriad of personal electronic devices which have speech as the main user input (Alexa, Siri, Google, etc.).  Unfortunately, the user experience with these consumer-grade options leaves much to be desired.  I didn’t quite get that” messages and confounding translations (“10 percent” = “temper sent”??) can leave users ready to throw their devices out the window.

Anyone using the latest industrial grade voice recognition technologies will tell you that there exists a marked difference between consumer-grade speech recognition software and today’s industrial grade offerings.  We have identified the top four reasons for this improved performance.

  1. Accuracy
    1. As you might imagine, speech recognition is hard.  While great strides are being made in natural language processing (NLP), consumer systems still rely on something known as ‘Speaker Independent” recognition.  This means that the users of the system never train the system on how they talk and moreover, most of  these systems do not improve over time as they speak more and more into it.  With the wide array of accents, grammar, and speech patterns it takes massive computing power and a massive data set of examples to determine what someone is saying – and the mistakes are many with this variability.
    2. High-end Industrial Voice Systems use ‘Speaker Dependent’ recognition, which means that each individual user trains the system with their own voice.  This may sound like a large time investment, but most voice systems need only 50-70 phrases to handle typical operational workflows and training takes less than half an hour.  User-specific training means that person’s unique vocal patterns, accents, and language are used when determining what command or response the user is attempting to give.
  2. Hardware/Microphone
    1. Consumer grade speech recognition typically relies on picking up sound from a distance or from over a phone.  In both cases capturing good quality audio is challenging because of the varying volume levels, likelihood and presence of background noise, and varying quality levels of the device itself.
    2. The best industrial speech systems rely on rugged headsets which bring the microphone close to the source of the desired sound: the mouth.  In addition, microphone arrays cancel out background noise before it even reaches the speech processor.  By normalizing these components of sound (volume/gain, quality) you begin the race with a huge headstart.
  3. Performance
    1. Most consumer grade speech systems require connectivity to the internet to function.  Assuming that you have a strong and continuous connection, the response time for these systems can be pretty good due to the super computers crunching this sound data in the background.  But any break in connectivity brings your efforts to a halt.
    2. In contrast, many industrial systems perform speech recognition right on the device worn by the user.  This means that you are able to use the system even on a deserted island.  Also,the system is designed to allow only a limited array of phrases at any given time, ensuring that the recognition of these phrases is instantaneous – every time.
  4. Adaptive Recognition
    1. As previously stated, the data that is being collected and compiled for consumer grade recognition is enormous and growing.  This helps raise overall recognition scores, but does little for the individual experience.
    2. In contrast, industrial systems can adapt to your changing speech patterns.  As the day wears on, your voice changes somewhat. Hay fever or a common cold can drastically change your speech.  By taking constant samples of recognition scores and adjusting the underlying speech template for users, the system improves over time – even with changes in the user’s speech.

Speaker dependant voice-directed workflows in industrial settings,  now in use for over two decades, have become the de-facto standard in distribution center technology.  Mountain Leverage has been delivering voice solutions for over a decade to a fiercely loyal customer base who enjoy improvements in accuracy, productivity, training time, safety, employee satisfaction, and more.  We understand how speech recognition works and use it as a tool to deliver amazing results across many industries.  

Want to know more? Reach out and we can discuss if voice can help solve some of your challenges at voice@mountainleverage.com.

Dan Paul
Dan is the VP of Customer Success at Mountain Leverage. With over a dozen years of experience delivering voice solutions, Dan has a passion for helping companies discover and unlock their operational excellence.

Why Industrial Voice Crushes Siri, Google, and Alexa
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See Voice Picking from First Person POV

by QUINN EDGELL, Communications Lead

Many studies report that voice is a worker-preferred pick technology. Companies report quicker onboarding, decreased turnover, and a more satisfied workforce. But why?  Enhanced ergonomics?  A feeling of empowerment and accomplishment?

In February, we paid a visit to our customer Bluestar in Kentucky to find out why.  But we didn’t just ask questions.  One of their shift supervisors, Jordan, let us strap a camera to him so that we could experience warehouse voice picking firsthand.

In the industry’s first and only “First Person POV Picking” video, you can experience everything exactly as a selector does. Watch Jordan:

  • sign in
  • pair equipment
  • download assignments
  • travel from pick to pick
  • confirm location & quantity

Ready to watch? Click the link below for the full video.

See Voice Picking from First Person POV
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BlueStar, Inc.

Located in Hebron, KY, BlueStar Inc. is a distributor of point of sale and barcoding hardware/software. When their old picking system presented efficiency challenges, BlueStar upgraded to voice for their pick to tote operation. Watch as the management team discusses the implementation, working with the Mountain Leverage team, and overall results.

 

BlueStar, Inc.
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