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Addressing the big data knowledge gap

08 June 2017

Leor Barth warns that to ensure that SME manufacturers remain competitive in the future they need to start to make use of their plant data. 

Manufacturers are often told that insights from big data analytics will offer a competitive advantage. Research published at the end of 2016 by Warwick Analytics, on behalf of the Alan Turing Institute found that tools such as Enterprise Resource Planning (ERP), Business Intelligence (BI) and Customer Relationship Management (CRM) are helping manufacturers ‘identify hidden information which can be used by organisations to provide valuable insights.’ 

However, for many new adopter manufacturers – particularly SMEs – there does remain a lack of knowledge about how this works in practice. Half of respondents to the same survey stated that they did not clearly understand the difference between business intelligence, big data analytics, and predictive analytics. 

Essentially, big data is the large volume of data – structured and unstructured – that inundates a business on a day-to-day basis. Big data or rather, big data analytics in manufacturing is about more than the amount of data in the industry. It is about using a common data model to combine structured business system data; structured operational system data; unstructured internal; and external data with a view to uncovering new insights through advanced analytical tools.

Big data analytics has already redefined many sectors of the manufacturing industry and has moved beyond being a buzzword. For many it is now a highly effective growth tool, one that informs decision-makers, helping to drive performance. It’s not all about technology – it’s also strategy, and if SMEs have both, they are off to a good start.
So, for food companies looking to better understand how big data can help their business, where is a good place to start? The first step is to see how much data the company has at its disposal. Most plants will collect volumes of process data, typically only using it only for tracking purposes, and not as a basis for improving operations. For these players, the challenge is to invest in the systems and skillsets that will allow them to optimise existing process information. For example, by centralising data from multiple sources, data analysts can draw actionable insights and useful, decision-making information. A key outcome is being able to predict future activities. This can help manufacturers improve margins, boost energy efficiency and sustainability, address regulatory concerns, increase product quality and reliability, save on costly IT and human resources.

A big data strategy
For food companies looking to implement a clear big data strategy, the following steps provide a good structure to help get started:

• Define a strategy – Set priorities and goals and build a roadmap – define what you’re looking to achieve.
• All aboard! – Get the right players in your court, all C-level management, IT and an outside consultant if needed.
• Set / define CSFs – Critical Success Factors (CSFs) determine the value of your project from the get-go, so establish, implement and agree before you begin.
• Data requirements – Spell it out beforehand, ask (and answer) questions like: What’s the volume of data received? Who/what will be the recipient of this data? How do we retrieve it, process it, manage it, secure it?
• Structure is key – Use open, flexible and scalable tools that yield quick (but viable) results as opposed to monster-sized app or system.
• Shop wisely – There’s no ‘one tool fits all’ so carefully scan and evaluate the market and its many vendors, as they come in all shapes, sizes and prices.
• Pilot phase – Before you embark, it’s highly recommended to sponsor (pay for) and run a pilot project to build management’s confidence and validate your project strategy and roadmap.

Big data analytics is set to have a big impact on the manufacturing world when it is really put to good use - when it is analysed, not just collected and stored. Food companies need to act fast to make sure they are able to generate the ‘right’ data and, most importantly, are able to turn it into actionable information. This will drive growth, and ensure companies are able to improve their performance levels in an increasingly competitive environment.

Leor Barth is vice president of R&D at Priority Software.

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