“If you always do what you always did, you will always get what you always got.”- Albert Einstein

As per recorded history, any technology or revolution that connected people, objects or places has created disruption, mostly for the greater good. Inventions like airplanes, cellphones, and the internet are a great example. Organizations like Facebook, Uber, Skype are real-world examples of digital revolutions. In the middle of all these organizations and technologies which have joined the list of household names, there is one concept which is lesser-known by the general public: IoT (Internet of things).

An environment where devices, objects, humans or animals are assigned a UID with the ability to transfer data over a network without requiring any human-to-human or human-to-computer interaction. Without a doubt, IoT is connecting the dots and making systems more efficient, and as a consequence, there is increased adoption of IoT across industries, especially in the manufacturing industry with a 300% increase in adoption in the past five years.

If you are involved with a manufacturing business in any capacity, chances are high that either you are already leveraging IoT or are about to. Invention of the wheel was a revolution and today we see it everywhere from moving a toy car to landing a 640 tons Antonov An-225. Similarly, the internet came as a revolution and applications like IoT are creative manifestations.

And now there is a new concept in manufacturing and logistics which is leveraging IoT to transform the way these two industries work and improve their efficiency: Preventive Maintenance. According to ITIC, an hour of downtime is costing 81% of enterprises an average of $300k. Often miss-calculations are made while marking maintenance dates on the calendar leading to either over or under maintenance of the machine. If your setup is not given the required care at the correct time, it comes back demanding a replacement of an expensive part of files for retirement altogether, in both cases, drilling a deep hole in your pocket.

Preventive maintenance is an AI-based solution from Pluto7 that leverages machine learning and predictive analytics to determine the remaining useful life of the machine or equipment and predicts when they are going to fail. Working along with Google IoT Core, the solution takes your IoT infra to a whole new level and there is more than one reason to start using our Preventive Maintenance Solution :

Seamless device management:

A coarse-grain way of device management. All the devices are given a unique identity and are supported with an authentication mechanism when the connection is attempted. All devices can be remotely controlled with the cloud.

A robust protocol connection:

Protocols are provided with endpoint connection using a protocol bridge which also enables automatic load balancing for all the connected devices. Uploads all the device telemetry to Cloud which can be accessed by downstream analytics systems.

Enhanced end to end security:

Enables end-to-end security using asymmetric key authentication over TLS 1.2; CA signed certificates can be used to verify device ownership. Devices supporting the IoT structure security requirements can deliver full-stack security.

Your global kingdom of devices:

All your devices can be connected to the cloud environment to make it a global that can be managed from one place. The solution uses Pub/Sub underneath, which retains data for seven days.

Unmatched data analytics:

Gathering data is one thing and deriving meaningful insights from it is another. Preventive Maintenance solution leverages machine learning technologies like Google BigQuery to deliver downstream real-time analysis of your equipment’s conditions.

Horizontal and vertical scaling:

Once you deploy the solution on a smaller set of devices, Preventive Maintenance can become essential for your entire setup. This solution is powered by Google Cloud, it scales up easily for any number of devices and can serve the demand for performance enhancements.

Governance:

The solution ensures data security and relevancy by applying IAM roles to device registries to control user access across the infrastructure.

Bring ML to the edge:

Preventive maintenance solution connects the devices with Google Cloud IoT edge and leverages Edge TPU to run the trained model on edge devices to deliver unprecedented speed for real-time response.

High frequency with lower latency:

Preventive Maintenance is about avoiding downtime and keeping your machines healthy. Keeping that mind, we built this solution where commands are fast, frequent, and one-time directives sent to devices.

Caters offline operations and resource-constrained devices:

Gives your resource-constrained devices offline capabilities by connecting them with a gateway, which performs tasks on behalf of devices like communicating with the cloud, connecting to the internet and authenticating credentials.

Gain real-time metrics:

Using Stackdriver Monitoring, the solution creates dashboards to provide real-time metrics and also sets up alerts based on metrics thresholds making your infrastructure immune to any unforeseen situation.

Reality check:

All these words might look theoretical or at times even fiction to some readers here is a real-world example of a manufacturer who makes our weekends memorable: ABInBev.

Anheuser-Busch InBev (AB InBev) makes some of the world’s oldest and most popular beer brands — including Budweiser, Corona, and Stella Artois. Before process transformation, they used meters to monitor and react to adverse conditions, such as a change in pressure. A top priority at AB InBev was optimizing the K Filter that kicks in at the end of beer brewing, right before packaging.

The challenge faced by them was regulating the pressure in and across the filter, which is a complicated process, involving many unpredictable variables. All these tasks being manually checked, brought in human errors which influenced the taste of beer. The AI solutions built by Pluto7 Consulting Inc, helped AbinBev to increase in the filter run length by 40%-50%. Our smart solutions not only helped AbinBev to cut their manufacturing costs but also brought consistency in the taste of beer produced. Here is the entire case study.

Here is a detailed interview with ABInBev leadership explaining their experience with Preventive Maintenance: Cheers!

Getting hands dirty:

With all your IoT data being processed appropriately, our Preventive Maintenance ML solution can let you transform your manufacturing centers into factories of the future.

At Pluto7, we know it is important that structural components of your core functional aspect do not break down without any prior warning, and that is what makes Preventive Maintenance essential for you. You can get started by launching the solution on the GCP marketplace or reach out to us for a free trial.

Demand Forecasting for Retail Industry

WHAT IS DEMAND FORECASTING 

Demand Forecasting is a crucial part of a retail company. Demand forecasts are basically estimates of expected consumer demand. Retailers rely on forecasts to plan the number of goods and services their customers will purchase in the future. Long Term strategic and operational plans like planning and scheduling production, acquiring inputs, making provisions for finances, formulating pricing, and planning marketing come from forecasts.

HOW DO COMPANIES DO IT NOW 

There are different theories and ways to forecast demand. There are qualitative and quantitative methods, as forecasting is seen as an art and a science. 

• Passive forecasting is for stable businesses with conservative growth plans. 
• Active forecasting is done for diversifying businesses with aggressive growth plans. 
• Short term forecasts are easier to predict, they are up to any forecast that is up to 12 months. 
• Long term forecasts are anywhere from 12 to 24 months and are more complex in nature.

The three most common techniques are the Trend projection method, the Barometric technique, and the Econometric forecasting technique. All three require meticulous calculation and are often off considerably as unplanned events occur.  

CHALLENGES COMPANIES FACE 

Retailers are facing challenges staying ahead of the competition with traditional demand forecasting methods. Some of the challenges they face are as follows: 

• Predicting variables that affect demand like weather or consumer trends

• Cost-effective buying for future seasons based on historical purchases, and other factors. 

• Maintaining enough inventory to meet demand in a timely, efficient way without stockouts or overstocked inventory. 

• Planning is usually a very manual process. 

• Make fast decisions to compete with large retailers

WHAT IS MACHINE LEARNING 

Normally, when you want the computer to do something, you have to program it with explicit rules. For example, if you want to look at a product on a manufacturing line and figure out if it is faulty or not, you have to make sure to code the rules for when the product is bent, cracked, broken, the wrong color, etc. Machine Learning is completely different. Instead of coming up with rules for how the product might be faulty, you give the computer information on what a good product is and what a faulty product is. Then you let the computer figure out how to tell if a product is good or faulty so that the computer learns to make a decision based on the data. Similarly, with demand forecasting, the historical data, market trends, and other key indicators train the model to make decisions and predict the forecast with higher accuracy because they are do not have the same limitations that humans do.  

PLUTO7 CASE STUDY 

California Design Den is a manufacturer and wholesaler of high-quality bed linens. They are also an e-commerce retailer, selling their products directly to consumers online. A couple of years ago, CDD partnered with Pluto7 and Google Cloud to leverage machine learning and artificial intelligence to predict demand and balance it with supply. Previously, the CDD team had to dig through spreadsheets and run scenarios to get a sense of how a particular product had sold and manually find relevant data points.

With Machine Learning they are now able to make decisions quickly to optimize profitability, pricing, and inventory. CDD was able to reduce its inventory carrying costs by over 50%, improve the accuracy of demand planning quarter over quarter, and gain insights into how individual SKUs are performing. They were able to do all of this without an army of data scientists by leveraging Google Cloud Platform.

Click here to read the entire case study  

WHY PLUTO7 

Looking to improve your demand forecast accuracy by more than 90%? Pluto7 has created an omnichannel analytics solution to drive effective sales and operations plans with exception-based demand forecasting methods. Demand ML™ leverages AI to manage complex and unpredictable fluctuations in demand volumes. It understands the pattern in your supply chain data to more accurately source the number of products to meet customer needs and deliver the best customer experience. This AI-Driven solution modernizes your supply chain systems and evolves your dynamic assortment planning. 

Contact us today at contact@pluto7.com

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