Providing data for decision making with cross-site data reporting

Published: 9-Oct-2017

Martyn Williams, Managing Director of industrial software expert, COPA-DATA UK, explains why manufacturers should invest in cross-site data reporting

Remote monitoring is not a new phenomenon. In fact, the first remote monitoring systems were installed in industrial facilities more than 25 years ago, originally using dial-up phone connections to reach the site.

Today, remote monitoring of manufacturing facilities is a much more sophisticated affair.

According to the KOF Index of Globalisation, the UK is the eighth most globalised country on the planet. For manufacturers, this globalisation has led to sprawling supply chains and companies with production sites spanning several countries, or even continents.

To maximise the efficiency of these supply networks, manufacturers must understand precisely what is happening at each location at each stage of operation, but with such a broad geographical scope, this is not an easy feat.

One of the biggest challenges caused by globalisation is that manufacturing plants have historically been run as self-serving operations and business decisions have been insular.

Naturally, when insight is limited to just one manufacturing facility, decisions will be made based on what is best for that specific plant — not the entire business network.

Without the data to hand it is not possible to make an informed decision and without insight to the entire supply chain, it is almost impossible for manufacturers to pinpoint where improvements need to be made.

Manufacturers need to make comparisons and benchmarks with other production facilities to accurately interpret key performance indicators (KPIs).

Consider the array of data collected from one manufacturing facility: energy consumption, waste, productivity levels and inventory data are just a handful of sources that generate data.

It is difficult to digest all of it and make an impartial decision based on this information. As a result, manufacturing decisions are often made based on the preferences or priorities of the employee signing off the decision.

A finance officer, for example, will prioritise budgets and the financial implications of a decision, whereas a production manager could make choices based on other criteria, such as staff productivity and resources.

Using smart factory software, this decision-making responsibility is taken away from employees and instead, the decision is impartially based on actual data.

By acknowledging consumer trend data and including it in the decision-making framework, manufacturers can prepare. However, this has a knock-on effect for the entire supply chain.

Using cross-site data reporting, manufacturers can decide on how to begin production in the most effective way.

The software can also identify other factors that could affect production. Could one facility be holding too much inventory? Are there ways to reduce logistics costs? Are there opportunities to save energy? Software can automatically weigh up the positives and negatives of each decision using collated data and automatically generate the best method for production.

Naturally, manufacturers can not access this insight from such high volumes of data without the software to support it. COPA-DATA’s zenon, as an example, supports over 300 drivers and protocols that have all been developed in-house. This ensures that, regardless of the make or model of the equipment being monitored, manufacturers can thoroughly compare multi-site data from anywhere in the world.

Remote monitoring is no longer about controlling production from outside the factory walls. Modern industrial software has the power to remotely collect and visualise data from even the most complex of supply chains.

The ability to benchmark across all facilities and plants is the only way for manufacturers to truly understand the state of their supply chain.

Better visibility of data means better business decisions and to achieve this, intelligent automation software is the only option.

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