Pohan Lin – Senior Web Marketing and Localizations Manager
Believe it or not, our world is run by data. Data drives every major decision of governments, companies and, whether they know it or not, individuals.
Data can be anything, but in the world of business it typically includes:
- Behavioural data, e.g., how a consumer uses an application or website
- Identity data, including names, date of birth and contact information
- Location data
- Quantitative data, like click-throughs in an email marketing campaign (which an email finder can help with)
- Qualitative data, e.g., written consumer feedback or focus groups
In this article we’re going to take a closer look at big data and the role it plays in manufacturing. Let’s dive in!
What is big data?
Big data, in its purest form, refers to very large sets of data collected across time. It can play an essential role across different industries, helping businesses optimise their processes and increase their revenue.
There are three factors that typically define and categorise big data:
- The speed or velocity at which data is collected and processed
- The volume or amount of data in the set
- The variety of the types of data that you have in your set – big data systems typically collect more than one type of data
Big data is typically data that, by virtue of its size and duration, reveals a pattern of behaviour in a group. The value of big data is that it can be mined for information by those who can access it, generally the company or organisation that collects it.
Examples of big data in manufacturing (and other industries)
Big data is everywhere. The digital age and rise of social media have made big data more popular and it is now being gathered across almost every industry.
Consumer
Ecommerce is a haven for big data. Not only do ecommerce and subscription services often require the collection of personal data at the point of payment, but big data is incredibly useful for monitoring customer behaviour.
Consider, for example, streaming services – Netflix, Amazon Prime, Disney +. All incredibly popular, and all constantly changing. One of the biggest ways in which streaming services use big data is to recommend new content that they think consumers will like based on their previous viewing. They collect viewing history from all users and identify watching patterns, recommending new watches based on a collective viewing history for a medium or genre.
From here, consumer-facing businesses typically use a quality of service solution, otherwise known as a QoS solution accelerator.
Services
The development of new software is driven by big data. For example, cloud software development, a growing industry with growing applications in the world of hybrid working, benefits widely from collecting large data sets.
In this instance, the data set might be collected by a mobile app development company from all users of a particular software. Important subsets of data may include user location, common problems, unsolved pain points, organisation size, and industry. This data may be collected slowly at first, but quickly grow with the customer base. It is likely also collected over a long period of time.
Industry
A bit more niche but no less valuable, heavy industry is a metaphorical goldmine of big data. The oil/gas industry in particular, lives or dies by the dictates of big data. On a global scale, the fuel industry collects behavioural and financial data to predict and set the cost of oil. The cost of oil dictates, to one extent or another, the price of mobile car detailing, and consequently, everything. The value and influence of big data cannot be overstated.
Big data in manufacturing
Big data in manufacturing is as essential as in every other industry. In fact, as a sector that can involve high costs and potential safety concerns, manufacturing benefits from big data in more ways than many other industries. These are five of the most common ways that you can use big data analysis to optimise your manufacturing business.
1. Process optimisation
First and foremost, big data exists to optimise internal and external processes and bring efficiency to a business. Most companies actually already have big data sets regarding their business processes, but may not be using them effectively.
You should cast a wide net when it comes to process optimisation data, including:
- Target reports and met and missed deadlines
- Production rates and volume
- Written and verbal feedback from floor teams
- Industry trends and legal changes
- Customer feedback and issues with distributed products
For example, if a manufacturing company works with a wholesaler, then they should consider reviewing their inventory management using big data. This helps them to look at the big picture – activities of the wholesaler, supply chains and company reputation. They might find that wholesale inventory management software, for example, is more efficient than manual inventory management.
From here, you have the evidence to consider more radical overhauling of your business processes. For example, you could integrate business process software from Process Bliss to better track and optimise business processes.
2. Safety
Perhaps more than any other industry, safety is vital to the continued operation of a manufacturing business. While it’s helpful to analyse individual incidents (or aversions of an incident) in isolation, in the long run it can be more helpful to turn to big data to make big safety changes.
This is because you may miss patterns of danger or negligence when treating each incident individually.
Be sure to keep a detailed record of each safety procedure – whether relevant to an incident or not. Only by collecting a wide range of data on every health and safety procedure can you properly identify patterns that result in an incident.
From here, you can properly integrate new safety procedures, backed by a full collection of evidence.
3. Quality Assurance
Quality assurance (QA) is vital to a successful manufacturing operation. Whilst it’s perfectly possible to run good QA without big data, it’s easier to spot weaknesses and patterns of behaviour with long-term activity tracking.
To optimise your QA processes with big data, consider the following:
- Changes in manufacturing processes over time
- Customer experience data and complaint reports
- Qualitative data from staff members
- Industry-wide changes, e.g., new materials, new safety standards
- Inconsistencies between staff members and teams
It may then be useful to engage data and SEO analytics tools. This is for a number of reasons, but largely because of the potential size of the data sets. In addition, it can be easier for an external consultant to identify quality issues than internal stakeholders.
4. Specialisation and diversification
Like other industries, manufacturing survives by adapting. Big data is an invaluable tool when it comes to identifying areas of the manufacturing process that need adapting through specialisation or diversification.
Particularly within niche industries, some manufacturing processes don’t change nearly as frequently as in other sectors. As a result, manufacturing businesses may find that they fall behind service-focused competitors. You may find that within your industry you are missing out on new methods to create higher-quality products or to improve business sustainability.
It’s beneficial here to collect cross-industry data. Collect as much data as possible on competitor products and long-term industry trends. You might also find consumer experience and wholesale data useful.
5. Loss prevention
There are a number of ways that loss can result from manufacturing. We’ve already covered safety and quality, but information breaches can be equally damaging to a manufacturing business. You can use big data and data analysis to help prevent future loss and learn from past ones.
Data breaches are serious errors, regardless of industry. While some industries have to be careful of customer data safety, manufacturing companies have to also be mindful of patented or trademarked information, as well as information on suppliers and distributors.
You should be collecting data from every potential or actual data breach from your company and, where possible, from other companies in the same industries. When analysing this data, look for patterns, including the causes, nature and outcome of workplace cybersecurity threats or data breaches.
Preventing data loss can, in turn, prevent financial loss through disciplinary action or reputational damage.
The case for big data
Big data has garnered a difficult reputation in recent years. Some consumers have visions of faceless corporations using their data for nefarious purposes. However, when used correctly, big data can have a hugely positive impact on business processes with tailored solutions for the manufacturing industry.
When collecting big data sets for your manufacturing department, remember to think carefully about the type of data you’re collecting and whether it’s relevant to manufacturing specifically.
Ultimately the goal of big data is to process disparate data sets as one in order to provide a holistic view of your business processes and how they can be improved, reducing costs and saving valuable resources.
If you can, cast a wide net in terms of what is helpful for manufacturing optimisation, as the point of big data is to mine diverse data for a wealth of information. Things like consumer habits can be as valuable as incident rates and wastage for optimising manufacturing processes using big data.
