inVia’s modular automation system, which includes a fleet of 200 autonomous mobile robots and AI-driven software, will modernize Kantsu’s e-commerce fulfillment process.

It will automate the picking process and allow warehouse workers to sort at rates of 800-900 units per hour per person, says inVia.

This will overcome the limitations of manual picking and deliver a flexible robotics solution that can scale as Kantsu’s e-commerce order volumes continue to grow.

E-commerce sales grew 25% in Asia Pacific in 2019, more than both North America and Europe combined.

The region is forecast to reach $3 trillion in online sales by 2021, creating increased urgency to bring greater efficiency into the order fulfillment process in order to keep pace with demand.

Kantsu is one of the leading providers of logistics and warehousing services to e-commerce businesses in Japan.

The growing demand for their services has led to an expansion of more than 161,000 square feet of capacity and 100 employees per year.

Kantsu prides itself on providing customers with unmatched quality of service and the most advanced robotics technology available in the market, leading to their choice of inVia to automate order fulfillment.

Hisahiro Tatsushiro, CEO of Kantsu, says: “Our customers are being pressured to provide more products and faster service to their consumers, and inVia’s robots ensure that we are always delivering the right products quickly.

“inVia Robotics’ technology allows us to offer superior logistics services to our customers.”

inVia’s integrated system includes inVia Logic warehouse optimization software and a fleet of inVia Picker robots. inVia Logic employs advanced AI algorithms to adapt to fluctuations in demand in real-time and adjust warehouse mapping to create more efficient paths and processes.

The inVia Picker robot automates the storage and retrieval process to pick and move products through the order fulfillment process.

Kantsu has also chosen to manage inventory replenishment and cycle counting through inVia’s system, which helps maintain inventory accuracy, “ensuring customers receive the items they want when they want them”.

This advanced level of automation and quality control has become even more critical as online order volumes continue to spike due to the Covid-19 pandemic, says inVia.

Lior Elazary, co-founder and CEO of inVia Robotics, says: “Kantsu is leading innovation in Japan’s logistics industry, and their upgrade to mobile automation allows them to provide superior service to their customers.

“We are excited to bring the precision and speed of inVia’s robots to Japan and Asia. This region has set the bar high on robotics innovation, and our technology is a great market fit.”

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There is a new saying: “data is the new oil”. It serves as a lubricant in many industries. To have more data is life for companies. As such, companies mine data in order to generate new information. David Hand, the British statistician, summarises data mining as “discovery of interesting, unexpected or valuable structures in large datasets.”

The keyword here is “large”. For data mining to be effective, companies must collect more information than is necessary which violates the purpose limitation principle in the Nigerian Data Protection Regulation. Companies, according to the regulation, must always collect data for the purpose agreed to by the data subject.

Data mining, therefore, might seem a hard chore for most companies. However, with the right technical and organisation measures in place, companies can mine data in a legitimate manner.

A business can collect data for the purposes of ensuring that they serve their customers better.  For example, an air conditioner company can predict with greater degrees of accuracy when maintenance is required for their units located in various buildings. The company can decide to collect data such as temperature, vibration, noise, and images as such, giving them enhanced the ability to detect future faults with greater accuracy. This can be solved by (a) appropriate notification of data subjects (b) right consent mechanism.

From a business-to-business(B2B) perspective, a software company can help a farm carry out accurate analysis by mining data. This usually does not raise data protection issues. For example, the information from an agricultural solution, measuring the level of moisture in the soil, and potentially issuing a command to turn on a watering system for a given amount of time to reach a defined, set level, requires some level of data collection and analysis.

However, from the business-to-consumer(B2C) perspective, this can raise the data protection issue. A company provides a home automation system to customers, which provides temperature readings from a home, and automatically initiates air conditioning systems and window blinds to reach a cooler temperature with less sun impacting the levels and feeds this to an individual’s phone need to be wary of many things. Are we intruding in a way that would expose the customers to danger? Have we carried out a data protection impact assessment? Are we processing the right data?

There are pitfalls in data mining. If done wrongly it could expose a company to a data protection breach.  If data identifies an individual, it best practice not to use it for extra purposes.

Data that does not identify an individual can be used without consent and other beneficial purposes of the company. To be on the safe side, the company that would need to analyse data needs to incorporate a robust anonymization system.

Companies can carry out data mining however, they need to employ the right technical and organisational measures for them to be on the right side of the law. To avoid been fined or exposed to data breaches, companies should gain consent from customers directly or find another lawful basis.  Most companies, these days, mine data but it can be done legally and purposefully without violating or intruding into the privacy of individuals.

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