The world of collecting and aggregating data to gain better insights is something the manufacturing industry is beginning to build upon. Understanding your supply chain, or reducing overheads like labour costs is now becoming a data-driven process.

In most industries, the use cases for big data could be drawing in external data sources like weather or social data. However, in manufacturing, it is subtly different. In manufacturing, it’s critical to have a much more holistic approach to data including traditional business intelligence, predictive, and big data sources.

Factors Impacting Manufacturing

One of the well-known key factors that impacts manufacturing profitability is overheads. Unfortunately though sometimes actually defining and understanding the root causes behind overheads can be very challenging.

For instance labour costs in manufacturing averagely equate to 30-40% of overall costs, and having a data-driven process to track labour means being able to analyse which labour activities are productive, and which activities raise overheads and impact the overall profitability of the business. Big data in manufacturing is usually generated internally rather than blending external data as is typical in other industries.

Machines continuously generate data, and this data has been traditionally used in a maintenance capacity for monitoring alerts if certain operating conditions were met. However, organisations are now starting to use predictive technologies to pre-empt serious failures, which reduces overheads in fixing and replacing expensive machinery.  The clever organisations are also collecting, storing, and processing ALL of the data their machines can generate; and are using sensory technology to expand the range of data collection to be used for reporting.

By considering a manufacturing business in more of a holistic way and extending cost forecasting to include the whole supply chain, delivery schedules, and even perhaps retail operations to sell your manufactured goods allows suppliers to track all raw material orders; and means that receiving data allows you to understand where in the supply chain a given commodity or part is located. The benefit of this is that it starts to eliminate manufacturing delays and costly downtime, and improves logistical efficiencies

It’s critical in Manufacturing to have well-structured and managed business intelligence reporting capability to understand profit generating and non-profit generating activities. It is critical to change reactive machinery alert systems to proactive predictive pre-emptive monitoring and reporting to reduce machinery replacement costs. Finally, it is important to embrace sensory technology across your whole business and supply chain to achieve crucial efficiencies and cost savings. This way you can start to define and understand the root causes of overheads and plan a reduction plan to improve which will ultimately lead to greater profitability and company stability.

Some industries could think of Business Intelligence, predictive, and big data almost as separate things or add-ons which are blended together at some stage, but in manufacturing it’s absolutely key to think about all of these internet of things (IoT) as a single design or solution, all working ubiquitously together.

If you recognise any of the above or you are interested in finding out more about data-driven solutions to improve your business, my advice would be to start building an ecosystem of partners who have the skills and capabilities to assist you on your journey to greater profitability.

Posted in Big Data

August 25, 2016