Think Studio https://thinkstudio.in Affordable Ai Softwares Wed, 02 Sep 2020 09:19:14 +0000 en-US hourly 1 https://wordpress.org/?v=5.4.1 Powerful uses of Ai in Grocery Shops https://thinkstudio.in/powerful-uses-of-ai-in-grocery-shops/ https://thinkstudio.in/powerful-uses-of-ai-in-grocery-shops/#comments Wed, 02 Sep 2020 09:16:58 +0000 https://thinkstudio.in/?p=215553

 Amazing uses of Artificial Intelligence in e-commerce

Learn How Small Shop owners can use Ai – India 

  • Most e-commerce small companies are failing to cope with stiff competition from giants like Amazon, Flipkart etc
  • Customers are turning to online shopping , and small stores in residential areas of India and other countries are losing business to online stores.
  • Although local kirana /Grocery stores in India, usually give you groceries at cheaper prices and also they give you credit facility which is not given by online stores.
  •  Most Grocery stores are now using CCTV camera in their shops but they are using it for security purpose mostly.

 

Welcome to the world of TDI

As a home grown start up from India, this is our effort to provide amazing and innovative ai solutions to small business owners , we understand that small business need cheaper and more robust solutions , also most small shop owners like grocery stores, kirana stores , electronic items, phone sellers , fashionable dress sellers etc all have very less knowledge about artificial intelligence. We are happy to share with you some amazing products that we can custom make for you at throw away price.

Our Pricing Model: We focus on cost reduction, but that’s not enough, when we say low cost we are talking about some emi per month , we usually give solutions at low upfront cost and also provide easy monthly payment options to small business people.

Convert your CCTV into Sales Agent!!

Ai in grocery store

Convert CCTV into Sales Agent

TDI founder, Abhishek Vidyarthi has observed buying behavior of consumers right from his college days, a NDIM Delhi pass out from batch 2000 -2002 says

Consumers must be pushed to buy when you sense they are waiting and watching a certain product, in old times it was done by good salesmen, today, we can do same work by using CCTV!

Grocery stores : Smart Shops

At the core of this product is a powerful Ai algorithm that captures the video stream from your existing CCTV and then magic happens, the algorithm carefully identifies all customers in the shop and keeps monitoring their behavior , data such as following are key parameters which are used to make intelligent software;

1- For how much time an average consumer visits the store?

2- Which section ( biscuits, loose grains like daal,chawal, papad, perfumes, snacks, soaps etc ) is most seen by consumers,

3- What are the busy hours in shop when many consumers come all of a sudden

Not all can be revealed in a post, but this is very basic data that we capture along with some secret datapoints. All of this data when fed into our powerful machine learning algorithm spits out the results which can increase your sales month on month here is how it help,

1- Our Software after some initial training automatically alerts you about potential buyer standing at some section in your shop, a cheap LCD monitor is installed at some location in shop where the potential consumers gets highlighted and your sales staff goes there and sales improves. You may argue that this work is already being done by your sales staff, wrong.

Ai detects patterns in shopping behavior , not only that it saves the customers buying patterns at back end and keeps on making better predictions about what this particular consumer might buy. The product is most suitable for small shops with low budget, you can buy online service from us , we tailor made projects for you.

Fashion Stores

Sri Lanka based company Avirate Fashion

Imagine a fashion store with magic mirror like below , don’t you think simply by having this in your shop you will gain lot of word of mouth, how much it costs ?

Avirate Fashion

Magic Mirror buy from TDI 

  • Amazing experience
  • Generates word of mouth
  • Your shop gets more buyer, more sales\
  • Price – Unbelievable

 

Magic Mirror : Lets users see how they would look in a certain outfit, simply by standing in front of this magic mirror. TDI uses advanced technology where even a person who is not with the shopper right now can shop easily.All you need is one pic in standing pose of any person, who wants to buy cloth from your store and you can generate many pics for all merchandise you have on your shop.

Technical Details of Magic Mirror are given below for you:

A Simple 32″ LCD monitor is mounted on wall , at front it has 3 small camera to take live pic of consumer.

Powered by Raspberrypi SBC or Jetson Nano to provide ultra fast image processing , this set up uses advanced algorithms to convert simple consumer pic into new pic where he is wearing the new dress, Its important to note that “people buy when they feel they are looking GOOD in a certain outfit” Putting on cloth many times in trial rooms is not liked by many women due to safety concerns.

Tackle fake reviews

Any experienced online retailer will be able tell you of at least one painful story about receiving fake reviews for their brand.

Consumers are flooded with an abundance of advertising every day, which can become overwhelming and this will often delay their decision making. This is where word of mouth has become invaluable.

If a customer’s friend has purchased your product and had a positive experience, then the customer will end up buying the product too.

In fact, according Dimensional Research’s recent study, a staggering 90% of respondents who recalled reading online reviews claimed that positive online reviews influenced buying decisions.

More importantly, 86% said that buying decisions were influenced by negative online reviews.

What if these reviews are fake? AI can be used to manage this problem, and here’s how:

In terms of creating fake reviews, it is well known as ‘astroturfing’ and it’s widespread across many sites and services including Amazon.

By definition, astroturfing is the practice of creating or disseminating a false or deceptive review that a reasonable customer would believe to be a trusted and neutral, third-party testimonial.

Customer reviews have become the cornerstone of trust in the online shopping world. Where users cannot physically see what the products are like before they buy them, the ratings and reviews of users who have supposedly bought them before can make or break a product.

Some eCommerce retailers are using artificial intelligence to fight astroturfing by putting more emphasis on verified and helpful reviews.

Amazon uses AI to combat fake product reviews and inflation of their popular star ratings.

Built in-house, its AI machine-learning system ensures that the prominence and weight of verified customer purchase reviews are boosted.

There is also preference to those reviews that are marked as helpful by other users as well as the newer and more up-to-date critiques on its site. The business is continuously reviewing several review characteristics such as ratings to detect fake reviews. They are critical to the company as they help to build customer trust in Amazon.

Combat counterfeit products

As with fake reviews (# list above), attributes such as product, brand and category are also useful to spot counterfeit products.

When browsing through large online marketplaces, it can be difficult for the everyday consumer to identify a counterfeit product from a third-party seller. When the consumer buys a product that looks legitimate but performs poorly, it can leave a sour taste and negatively impact the consumer’s perception of the brand.

So how can eCommerce retailers tackle counterfeit products?

Chicago start-up 3PM Marketplace Solutions adds a layer of protection for brands by adopting machine learning algorithms that spot counterfeits and help businesses understand how consumers are discovering their products.

The tech company then draws on data from multiple online marketplaces and analyses it to determine which products are in fact counterfeit. A fascinating and effective way of using artificial intelligence to tackle the painful problem of counterfeit products.

Rob Dunkel, founder of the tech start-up, recently stated that factors such as the posting rate of an account, what kind of items it sells and even potentially fake reviews on listed items, are all used to point to a counterfeiter. Clients can then submit claims with a marketplace such as eBay or Amazon, to have the shady counterfeit products removed.

 

 

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Intel CIO Says AI Playing a Growing Role in Chipmaker’s Operations https://thinkstudio.in/intel-cio-says-ai-playing-a-growing-role-in-chipmakers-operations/ https://thinkstudio.in/intel-cio-says-ai-playing-a-growing-role-in-chipmakers-operations/#respond Wed, 02 Sep 2020 06:15:30 +0000 https://thinkstudio.in/?p=215521
Intel Corp. Chief Information Officer Archana Deskus says artificial intelligence has become more critical to the semiconductor maker during the coronavirus pandemic.

AI is helping Intel generate insights and increase the speed at which products are tested, which has been crucial as technology initiatives were accelerated in recent months, Ms. Deskus said. The Santa Clara, Calif.-based company has also benefited from investments in data analytics and machine learning algorithms over the past decade as it contends with the pandemic’s effects on its business, she said.

“We’ve spent a decade building up a sturdy data foundation which is the basis for AI efforts, and when the pandemic hit, we were able to move quickly,” Ms. Deskus said.

Intel is also using AI algorithms to identify problems with employees working remotely and for optimizing inventory throughout its supply chain, as the pandemic caused manufacturing disruptions in China, Ms. Deskus said.

By the end of 2024, 75% of organizations experimenting with AI today are expected to shift from testing AI algorithms to fully deploying them in business operations, according to a June report from technology research firm Gartner Inc. That is partly because AI provides companies with critical predictions that can drive revenue growth and reduce cost and risk, according to Gartner.

By the end of 2024, 75% of organizations experimenting with AI today are expected to shift from testing AI algorithms to fully deploying them in business operations, according to a June report from technology research firm Gartner Inc. That is partly because AI provides companies with critical predictions that can drive revenue growth and reduce cost and risk, according to Gartner.

tel’s IT teams developed an AI-based hardware validation program composed of more than 50 machine learning algorithms, an effort that began about five years ago. The machine learning algorithms can automatically detect hardware bugs quicker than humans, because they are able to sift through terabytes of data to find anomalies, Ms. Deskus said.

“We can do this in a much more sophisticated way than we could previously,” she said. Previously, the work of generating tests and selecting which tests to execute was performed manually.

The algorithms are currently testing all of the functionalities of Intel’s future chips. Intel reported stronger earnings in its most recent quarter but it has signaled a delay in its development of superfast chips.

Earlier this year, Ms. Deskus oversaw the deployment of virtual private networks to support approximately 100,000 employees who began working remotely in early March. VPNs allow employees to work on their computers securely from home. Intel’s technology team used machine-learning algorithms developed in-house as well as tools built by a network monitoring company to proactively identify issues with remote-work setups, she said.

The algorithms were used in part to detect problems with Wi-Fi connectivity and internet traffic routing. Intel helped employees optimize routing configurations and bandwidth use.

“We tried to help them get as similar of an experience as if they were in the office,” Ms. Deskus said.

Intel used AI algorithms to help simulate various supply chain and logistics scenarios. For example, when supply chain disruptions for Intel products occurred at factories in China during the height of the pandemic, the company used machine-learning models to help identify alternative supply chain routes.

AI algorithms were also used to identify the root cause of quality issues on the manufacturing line in real-time to correct those problems right away. Previously, Intel relied on data analysis without machine learning to identify the root cause of problems after manufacturing had been completed.

Before the pandemic, automation wasn’t a major part of Intel’s manufacturing strategy, Ms. Deskus said. “The reliance on the [automation] technology and the pivot is happening much faster than if we didn’t go through the crisis,” she added.

Write to Sara Castellanos at sara.castellanos@wsj.com

Courtsey: Wall Street Journal September 2020

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Marketing https://thinkstudio.in/marketing-sidcul-haridwar/ https://thinkstudio.in/marketing-sidcul-haridwar/#respond Wed, 15 Jul 2020 13:42:31 +0000 https://thinkstudio.in/?p=212237 Marketing Team Offices: Our Teams In  India and Canada /USA Mr Sanjeev Sharma (Marketing Head) With more than 20 years of marketing and sales experience , he is working to increase presence of our firm in SIIDCUL (Haridwar, Roorkee, Kashipur and Dehradun) , people appreciate how he never over promises and gives you both sides of […]

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Marketing Team Offices:

Our Teams In  India and Canada /USA

Mr Sanjeev Sharma (Marketing Head) With more than 20 years of marketing and sales experience , he is working to increase presence of our firm in SIIDCUL (Haridwar, Roorkee, Kashipur and Dehradun) , people appreciate how he never over promises and gives you both sides of story.

Address: HSE Enterprises, Shopping Centre ,  Shivalik Nagar, BHEL Haridwar UK, India

Phone Number: +91-6399935149

Mr. Abhishek Vidyarthi ( Founder TDI )  Can be reached at tdi@thinkstudio.in

Address: U 501, Haridwar Greens , Navoday chowk, Haridwar UK, India

Ms. Sasha Tanoushka (USA and Canada)

Sasha looks at our sales and marketing in USA and Canada, she has been entrusted to develop our market share in areas like app development and SEO functionalities. She can be reached at +1(250-507-0960)

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developers https://thinkstudio.in/developers/ https://thinkstudio.in/developers/#respond Wed, 15 Jul 2020 13:15:09 +0000 https://thinkstudio.in/?p=212231   World Class Developers Our core developers are certified Stanford professionals in Machine Learning and Artificial Intelligence. 1- Mayank Dhiman – (4 years Experience , Stanford Certified) B.Tech from THDC Engineering College, throughout topper in his career decided to join TDI in year 2017. Today he is a respected contributor to all projects and is […]

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World Class Developers

Our core developers are certified Stanford professionals in Machine Learning and Artificial Intelligence.

1- Mayank Dhiman – (4 years Experience , Stanford Certified)

B.Tech from THDC Engineering College, throughout topper in his career decided to join TDI in year 2017. Today he is a respected contributor to all projects and is active on many coding platforms, like Kaggle , Quora, stackexchange. He has on his own worked on many advanced Algorithms which include GAN ( Generative Adversarial Networks) . He is fluid coder in Python, Java , SQl and also is comfortable in making android apps.

2- Adrian  Munguia – (20 Years Experience)

I am a recognized artificial intelligence (AI) expert with 20 years of experience creating solutions and business success in Health Care, Manufacturing, Automation, Defense, Marketing, and more.

SWE, DL, ML, CV, AI, OOA/OOP, Agile.

WHAT I DO:

▶️ I help people build AI-intensive products.
▶️ I help companies solve difficult problems with the right algorithms and AI approaches.
▶️ I help companies hire, train, and structure in-house AI teams.
▶️ I advise governments looking to embrace AI and assist decision-making, provide knowledge, difficult to obtain know-how, support from a world-class team, and access to many key resources from organizations around the world!

WHO I WORK WITH:

With anyone looking to learn, teach, adopt, or implement AI successfully in:

▶️ Healthcare
▶️ Retail & E-commerce
▶️ Fintech
▶️ Logistics and Transportation
▶️ Manufacturing
▶️ Defense
▶️ Law Enforcement
▶️ Entertainment, Gaming or Marketing

WHY IT WORKS:

▶️ Working with me will get you the most efficient, effective, transparent, and cost-effective AI services comprised of R&D and product-oriented engineering, AI models that meet client specs and professionally engineered and deployable software.

WHAT MAKES ME DIFFERENT:

▶️ I am the only AI resource around that has 20 years of industry success as part of innovative startups and world-famous Fortune 500 companies.

▶️ Contributed important CV inventions leading to US patents: 9177225, 9336459, 9317778.

▶️ Skilled problem-solver with an accurate sense of the algorithms that need to be improved, and the CV and ML approaches to be modified to meet contractual rates and obtain customer sign-off.

3- Abhishek Vidyarthi ( Stanford Certified Ai and ML , 2018)  

His strength is less of coding and more of understanding the behavior side of Technology, curious about the way people consume technology makes him leagues ahead in understanding clients key issues, be it about rolling out new product, or business process change. Abhishek always tries to match User interface with end consumer . He focuses on cost reduction and guides entire operations to deliver world class projects to clients both in B2B segment and B2C segment.

You can trust us on quality and Price.

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Overall Equipment Effectiveness (OEE) https://thinkstudio.in/overall-equipment-effectiveness-oee/ https://thinkstudio.in/overall-equipment-effectiveness-oee/#respond Sat, 02 May 2020 12:21:30 +0000 https://thinkstudio.in/?p=212107 OEE scores provide a very valuable insight – an accurate picture of how effectively your manufacturing process is running. And, it makes it easy to track improvements in that process over time.

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The simplest way to calculate Overall Equipment Effectiveness (OEE) is as the ratio of Fully Productive Time to Planned Production Time. Fully Productive Time is just another way of saying manufacturing only Good Parts as fast as possible (Ideal Cycle Time) with no Stop Time. Hence the calculation is:

OEE = (Good Count × Ideal Cycle Time) / Planned Production Time

Here, Fully Productive Time is actual production time after all losses are subtracted, Planned Production Time is the total time that equipment is expected to produce, Good Parts meet the quality standards, Ideal Cycle Time is the minimum time to produce one part, and Stop Time also called Down Time is all the production time when the production was halted because of planned or unplanned stops.

Although this is an entirely valid calculation of OEE, it does not provide information about the three loss-related factorsAvailabilityPerformance, and Quality. For that – we use the preferred calculation.

PREFERRED CALCULATION

The preferred OEE calculation is based on the three OEE FactorsAvailabilityPerformance, and Quality.

Calculate Overall Equipment Effectiveness by multiplying Availability, Performance, and Quality
OEE is calculated by multiplying the three OEE factors: Availability, Performance, and Quality.

Availability

Availability takes into account all events that stop planned production long enough where it makes sense to track a reason for being down (typically several minutes).

Availability is calculated as the ratio of Run Time to Planned Production Time:

Availability = Run Time / Planned Production Time

Run Time is simply Planned Production Time less Stop Time, where Stop Time is defined as all time where the manufacturing process was intended to be running but was not due to Unplanned Stops (e.g., Breakdowns) or Planned Stops (e.g., Changeovers).

Run Time = Planned Production Time − Stop Time

Performance

Performance takes into account anything that causes the manufacturing process to run at less than the maximum possible speed when it is running (including both Slow Cycles and Small Stops).

Performance is the ratio of Net Run Time to Run Time. It is calculated as:

Performance = (Ideal Cycle Time × Total Count) / Run Time

Ideal Cycle Time is the fastest cycle time that your process can achieve in optimal circumstances. Therefore, when it is multiplied by Total Count the result is Net Run Time (the fastest possible time to manufacture the parts).

Since rate is the reciprocal of time, Performance can also be calculated as:

Performance = (Total Count / Run Time) / Ideal Run Rate

Performance should never be greater than 100%. If it is, that usually indicates that Ideal Cycle Time is set incorrectly (it is too high).

Quality

Quality takes into account manufactured parts that do not meet quality standards, including parts that need rework. Remember, OEE Quality is similar to First Pass Yield, in that it defines Good Parts as parts that successfully pass through the manufacturing process the first time without needing any rework.

Quality is calculated as:

Quality = Good Count / Total Count

This is the same as taking the ratio of Fully Productive Time (only Good Parts manufactured as fast as possible with no Stop Time) to Net Run Time (all parts manufactured as fast as possible with no stop time).

OEE

OEE takes into account all losses, resulting in a measure of truly productive manufacturing time. It is calculated as:

OEE = Availability × Performance × Quality

If the equations for AvailabilityPerformance, and Quality are substituted in the above and reduced to their simplest terms the result is:

OEE = (Good Count × Ideal Cycle Time) / Planned Production Time

This is the “simplest” OEE calculation described earlier. And, as described earlier, multiplying Good Count by Ideal Cycle Time results in Fully Productive Time (manufacturing only Good Parts, as fast as possible, with no Stop Time).

Why the Preferred OEE Calculation?

OEE scores provide a very valuable insight – an accurate picture of how effectively your manufacturing process is running. And, it makes it easy to track improvements in that process over time.

What your OEE score doesn’t provide is any insights as to the underlying causes of lost productivity. This is the role of AvailabilityPerformance, and Quality.

In the preferred calculation you get the best of both worlds. A single number that captures how well you are doing (OEE) and three numbers that capture the fundamental nature of your losses (Availability, Performance, and Quality).

Here is an interesting example. Look at the following OEE data for two sequential weeks.

OEE Factor Week 1 Week 2
OEE 85.1% 85.7%
Availability 90.0% 95.0%
Performance 95.0% 95.0%
Quality 99.5% 95.0%

OEE is improving. Great job! Or is it? Dig a little deeper and the picture is less clear. Most companies would not want to increase Availability by 5.0% at the expense of decreasing Quality by 4.5%.

CALCULATION EXAMPLE

Now let’s work through a complete example using the preferred OEE calculation. Here is data recorded for the first shift:

Item Data
Shift Length 8 hours (480 minutes)
Breaks (2) 15 minute and (1) 30 minute
Downtime 47 minutes
Ideal Cycle Time 1.0 seconds
Total Count 19,271 widgets
Reject Count 423 widgets

Planned Production Time

As described in the OEE Factors page, the OEE calculation begins with Planned Production Time. So first, exclude any Shift Time where there is no intention of running production (typically Breaks).

Formula: Shift Length − Breaks

Example: 480 minutes − 60 minutes = 420 minutes

Run Time

The next step is to calculate the amount of time that production was actually running (was not stopped). Remember that Stop Time should include both Unplanned Stops (e.g., Breakdowns) or Planned Stops (e.g., Changeovers). Both provide opportunities for improvement.

Formula: Planned Production Time − Stop Time

Example: 420 minutes − 47 minutes = 373 minutes

Good Count

If you do not directly track Good Count, it also needs to be calculated.

Formula: Total Count − Reject Count

Example: 19,271 widgets − 423 widgets = 18,848 widgets

Availability

Availability is the first of the three OEE factors to be calculated. It accounts for when the process is not running (both Unplanned Stops and Planned Stops).

Formula: Run Time / Planned Production Time

Example: 373 minutes / 420 minutes = 0.8881 (88.81%)

Performance

Performance is the second of the three OEE factors to be calculated. It accounts for when the process is running slower than its theoretical top speed (both Small Stops and Slow Cycles).

Formula: (Ideal Cycle Time × Total Count) / Run Time

Example: (1.0 seconds × 19,271 widgets) / (373 minutes × 60 seconds) = 0.8611 (86.11%)

Performance can also be calculated based on Ideal Run Rate. The equivalent Ideal Run Rate in our example is 60 parts per minute.

Formula: (Total Count / Run Time) / Ideal Run Rate

Example: (19,271 widgets / 373 minutes) / 60 parts per minute = 0.8611 (86.11%)

Quality

Quality is the third of the three OEE factors to be calculated. It accounts for manufactured parts that do not meet quality standards.

Formula: Good Count / Total Count

Example: 18,848 widgets / 19,271 widgets = 0.9780 (97.80%)

OEE

Finally, OEE is calculated by multiplying the three OEE factors.

Formula: Availability × Performance × Quality

Example: 0.8881 × 0.8611 × 0.9780 = 0.7479 (74.79%)

OEE can also be calculated using the simple calculation.

Formula: (Good Count × Ideal Cycle Time) / Planned Production Time

Example: (18,848 widgets × 1.0 seconds) / (420 minutes × 60 seconds) = 0.7479 (74.79%)

The result is the same in both cases. The OEE for this shift is 74.79%.

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Increase Worker’s Efficiency https://thinkstudio.in/increase-workers-efficiency/ https://thinkstudio.in/increase-workers-efficiency/#respond Thu, 12 Mar 2020 10:33:56 +0000 https://thinkstudio.in/?p=211875 Indian manufacturing sector depends on hiring casual workers for its production planning. However, we all know that contract or casual workers have some issues which is a routine problem for all of us. In order to solve this challenge, we used AI to raise warning in case your worker is performing at a speed less […]

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Indian manufacturing sector depends on hiring casual workers for its production planning. However, we all know that contract or casual workers have some issues which is a routine problem for all of us.

In order to solve this challenge, we used AI to raise warning in case your worker is performing at a speed less than required. such interventions can automate your production , best part is this software can work even for packaging, labelling, packing, whatever be the task of your workers, we can analyse their rate of doing it and present the same to you in easy to use excel format.

We think production needs instant action, so instead of waiting for production manager to give caution to workers , our voice enabled software can give advice/ cautions to workers instantly , this system instills the belief in workers that they are being watched and reduces following issues of:

  1. Slow rate of work
  2. idle gossips on shop floor
  3. frequent breaks
  4. theft cases are reduced as they now know that intelligent system is catching their hand movements
  5. Floor supervisor gets more time to focus on quality and other activities rather than focusing on time keeping.

Product is robust and can perform in all conditions and temperature, comes with full warranty for 1 year.

For Demo you may contact : Sanjeev Sharma ( Our Authorised Dealer for Uttrakhand) at +91-6399935149

Authorised Dealer
HS Enterprises, Haridwar

 

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Assembly Line Manager by TDI https://thinkstudio.in/assembly-line-manager-by-tdi/ https://thinkstudio.in/assembly-line-manager-by-tdi/#respond Thu, 12 Mar 2020 10:11:27 +0000 https://thinkstudio.in/?p=211872 ALM or Assembly Line Manager is designed to help manufacturers achieve the following objectives : 1- Know who is efficient worker/workers in your plant 2- Helps in production planning and forecasting 3- Reducing your machine running time, thereby saving maintenance cost 4- Helps in optimizing production cost 5- Screen or allocate resources to various assembly […]

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ALM or Assembly Line Manager is designed to help manufacturers achieve the following objectives :

1- Know who is efficient worker/workers in your plant

2- Helps in production planning and forecasting

3- Reducing your machine running time, thereby saving maintenance cost

4- Helps in optimizing production cost

5- Screen or allocate resources to various assembly line set up by using benchmark set by you or by intelligent analytics in ALM.

6- Leads to great reduction in overtime and this saves lot of cost.

ALM – is based on scientific concept of time and motion study used by most big corporations, however due to such software being very costly most MSME are not able to use such tools , but TDI has used advanced VIDEO ANALYTICS along with ANOMALY DETECTION algorithms to arrive at best results.

It comes with very easy to use and intuitive interface and can update management in real time about various key metrics like:

1- Do we need to increase or decrease assembly line speed.

2- Historical trends of efficiency

3- Day end projected production numbers.

4- Your entire working staff gets ranked automatically as low, medium and high efficiency bucket , which may help in mix and match strategy.

For Demo you may contact us at 9568924536

 

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