Faster, better, cheaper business intelligence

At textile rental business Berendsen, low-cost RFID tags sewn onto items such as sheets and bath towels are read by RFID scanners as they pass through the company’s 30 nationwide laundries, inbound from—and outbound to—customers such as hotels and hospitals.

It’s undoubted big data: a million tags a day, equating to a data capture rate of several items every second.

And once captured, the data is sent to Microsoft’s Azure Cloud for storage and processing, analysed by a set of business intelligence and analytics tools such as Microsoft Azure Stream Analytics, Microsoft Azure SQL Database, and Microsoft Power BI.

The goal: slick analytics interpreted via operational dashboards and reports, all firmly targeted on reducing asset loss; improving asset utilisation; driving better levels of customer service, and closer customer relationships.

Implemented by award-winning Microsoft implementation partner eBECS, it’s a showcase example of cutting-edge business intelligence (BI), and a perfect illustration of the rapid development of BI in recent times.

BI and analytics

From in-memory computing to wholesale cloud deployment, few areas of enterprise software have seen so much change, so quickly.  But what does it mean for manufacturers? And how can they benefit from the powerful new BI and analytics technologies that are now on offer?

Talk to insiders and it’s clear that this rapid pace of change has genuinely delivered new opportunities. Never before has it been possible to capture and analyse so much information, from so many diverse sources, and to do so as quickly and cost-effectively as is possible today.

And the numbers are impressive. According to research conducted jointly by the Centre for Economics and Business Research and BI and analytics specialist, SAS, the combined impact of big data and the Internet of Things will deliver an £84bn boost to the UK’s manufacturing sector between 2015 and 2020.

“Collecting and storing data is only the beginning. It is the application of analytics that allows the UK to harness the benefits of big data and the Internet of Things,” said Graham Brough, the Centre’s chief executive.

“The key lies in making sure that manufacturers’ investments in Business Intelligence solutions are extracting maximum insight, which is then turned into business actions.”

And the scope for that extends far beyond sales and finance analytics, which is where BI solutions have traditionally been found. Again and again, observers are these days extolling the applicability of business intelligence in almost every area of a manufacturer’s operations.

In supply chains, for instance, there’s an opportunity to use BI-derived insights to better predict demand, and match that demand to actual orders, according to Tim Clark, UK and Eire manufacturing industry solution specialist, SAS.

“With better analytics, you can predict demand more accurately than by just using past data,” he pointed out. “It’s possible to factor in pricing strategies, seasonal consumption patterns, and trade promotions, as well as the impact of weather and promotional activity.”

BI adoption

At Ford, meanwhile, warranty data is being mined to analyse manufacturing issues, supplier quality, dealer repair trends, and other factors that help the company build better vehicles, and maintain happier customers.

Indeed, said Peter Walker, European vice-president at BI specialist Information Builders, manufacturers are regarded as being at the forefront of BI adoption precisely because of the significant amounts of data that are to hand within most manufacturing businesses.

“We’re seeing a dawning realisation among manufacturers that they can use this data to enhance their competitive edge, and improve their operational performance,” he observed.

That said, this enhanced competitive edge and improved operational performance is decreasingly associated with traditional ‘report style’ business intelligence solutions.

Instead, says Leor Barth, vice-president of R&D at Priority Software, businesses are demanding at-a-glance performance measurement, delivered through dashboards.

“They want the added element of judgement against a context of KPIs, goals, and historic performance, which helps managers to instantly see what their priorities are,” he explained.

“Simple colour-coded ‘dials’ and ‘gauges’ provide just this kind of at-a-glance visibility.”

What’s more, added Jonathan Orme, sales and marketing manager at Exel Computer Systems, there’s a growing trend for these contextually-enhanced dashboards to be both role-based and mobile, available on low-cost devices such as smartphones and tablet computers.

“If information is important, it needs to be available to people wherever they are, and not just when they’re at their desks,” he said.

“And with role-based dashboards, you’re making those dashboards highly-specific, and tuned to individual managers’ individual requirements.”

Cloud hosting

The combination of big data, multiple data sources, contextually-enhanced reporting and clever analytics undoubtedly increases the computing workload, possibly past the point where many manufacturers’ traditional servers could cope.

Enter the cloud, and in-memory computing, says Lionel Albert, European manufacturing and supply chain director at Oracle. While in-memory analytics has found favour with large enterprises who have the budgets to support large-scale on-premise deployments, the appeal of the cloud is much broader, he explained.

“Simplicity is the key,” he emphasised. “Solutions provided through the cloud don’t require manufacturers to buy and manage expensive hardware. They can just get on with the business of analysing and learning from their data, and transforming those insights into actions.”

“It’s often been difficult for smaller manufacturers to justify an investment in Business Intelligence,” summed up Matthew Scullion, managing director at cloud-based Business Intelligence specialist, Matillion.

“But put the data warehouse and the associated analytics and reporting in the cloud, and the risk and upfront capital expenditure involved in on-premise business intelligence simply vanishes.”