illumr Blog

illumr have been approved to join the Joint Supply Chain Accreditation Register (JOSCAR) which enables companies in the Aerospace, Defence, Security & Space sectors to identify qualified suppliers. 

The accreditation mark is valued by some of the largest purchasers in the defence community and indicates that illumr have been through the process and have demonstrated commitment and appropriate credentials. 

JOSCAR covers company capability, accreditations’, information security, corporate social responsibility, financial history and ethical operations. This cross-sector collaborative solution reduces the time, cost, resources and duplication currently needed to provide information.

What is JOSCAR?

JOSCAR is a collaborative tool used by a growing number of prime contractors in the aerospace, defence and security industry to act as a single repository for pre-qualification and compliance information.

The cross-sector collaborative solution reduces the time, cost, resources, and duplication currently needed to provide information to major prime customers in the defence aerospace sectors. JOSCAR holds common supplier information in a central system, allowing information to be accessed by all participating buying organisations.

Why is accreditation important?

To attain accreditation, illumr  was assessed across a wide range of appropriate business parameters including our policy on counterfeit materials, insurance and third-party certification, health and safety, environmental and sustainability, IT security, anti-bribery and corruption, product safety and quality.

JOSCAR membership is designed to improve efficiency of both the supplier and buyer communities by ensuring that companies only use products and solutions of the highest quality and that comply with best practices.

We are pleased that we have satisfied the requirements to become fully compliant on the JOSCAR supplied accreditation register. Becoming a member assures our clients and partners that the service we provide is of the highest quality and comply with the industry standards.

Data Analytics [DA] and Machine Learning [ML] are structured, quantitative approaches to answering difficult questions about datasets. The promise of DA and ML is that the insights gained about the world can be much more complex than those which can be found by humans, and also that those insights will be free of human bias. This essay will focus on the second promise, the total objectivity of DA/ML. It has long been recognized that the outcomes of DA/ML can vary significantly depending on the choice of methodology, which already strikes a blow to the claims of objectivity. However, lately a more fundamental problem has emerged — the data used for DA/ML often contains human biases and DA/ML performed on such data replicates them.

Computers are reasonably good at analyzing large datasets, but there is one class of problem where they require a bit of help from puny humans – high dimensional datasets. By “high-dimensional” we mean “wide”, as in lots of columns. When we have wide data, it’s very hard to spot commonalities across a number of those columns. For example, if we have data from a large number of sensors, and all of them have something to say about what’s going on, it’s very hard to detect what is similar about all those readings when a particular type of event occurs.