There may be some confusion in manufacturing regarding what big data actually is and the difference between Big Data and analytics – and the vendors are not always helping. However, I see that manufacturing can benefit from ‘Big Data’ tools (such as Hadoop and others) to pull together structured, unstructured and time-stamped data. Manufacturing is also not only plants or factories in isolation, but the complete value-stream, which includes the supply chain for larger organisations.
In this article, I will look at and describe some of the benefits proposed to the manufacturing industry by Big Data vendors and proponents.
Having actual (not theoretical) takt-time information and throughput figures per product per line, with planned maintenance schedules, accurate breakdown reasons and historic data of the time to repair, will enable planners to forecast ‘available to promise’ dates far more accurately. To do this, information is required from the MOM system, the ERP system, the maintenance system, the plant historians, the quality system, and even from the warehouse system. This will also require the analysis of user-entered text/comments normally associated with maintenance systems, often difficult to trap and structure for analysis with normal analytical tools. Planners or customer service representatives will then be able to inform the customer in advance if these dates change as a result of a breakdown, ie, not only on the day when delivery was scheduled to happen. I believe better plant capability forecasting will be possible when using Big Data tools. In the bigger supply-chain, Big Data will also improve demand forecasting on the plant. Combining the capability and demand forecasting information will improve planning and scheduling operations.
More understandable multiple metrics
I believe Big Data is about analytics. What Big Data tools do better than their normal EMI counterparts is to identify patterns over time. For instance, if metric 1 increases by 10% then metric 2 typically reduces by 2,5%. Unless you build your EMI solution to specifically look for this, you will not see the pattern. Big Data tools can also be used to identify relationships between ‘silo’ metrics, for instance the relationships between stock-turns, throughput, yield, final quality, maintenance disciplines, product, customer, breakdowns, shifts, personnel and time of year or seasonality. EMI tools are good at providing real-time operational intelligence of defined relationships. Big Data tools are good at finding relationships between data sets.
Faster service and support for customers
In my first point I referred to this. Having proven and validated capability information will make it much easier to provide accurate ‘available to promise’ delivery dates and times. Having proven cause/effect information available at all levels will improve the ability of customer service agents to inform their customers (in advance) when things go wrong at plant level.
Real-time manufacturing analytics
EMI provides these, but once again only looks at defined data sets, while Big Data tools enable ‘machine learning’ within the factory. For instance, if a factory complex has 200 pumps of five different sizes, Big Data will be able to analyse the state of the pumps over time and develop patterns for ‘healthy’ and ‘imminent breakdown’ states. If a breakdown happens, it will be able to identify the state prior to breakdown and will be able to associate this state to all the other pumps of the same size in the factory. This will enable the system to warn of imminent equipment failure well in advance of the actual event. If this warning is then combined with accurate capability information, the customer service agent will be able to predict planned maintenance and delayed deliveries for a certain period. This is a maintenance example, but as per the multiple metrics example above, it is about finding patterns and relationships.
Correlation of manufacturing and business performance
Think about this, we all know that an improved OEE should translate into better business (financial) performance. We know that this is generally accepted in industry, otherwise people would not use that metric. But what is the actual financial impact of a one percent drop in OEE for a specific company? In the silo way we structure, store and analyse manufacturing data, coupled with the volatility of the market and stock cycle times, normal BI and EMI tools will find it more difficult to provide an accurate answer to that question than Big Data tools.
In saying all of the above, I am not proposing that every manufacturer should run and get a Big Data solution – far from it. What I propose is that as the manufacturing fraternity, we investigate what Big Data does for companies and evaluate how we can apply the concepts in manufacturing, with or without actual Big Data tools.
For more information contact Gerhard Greeff, Bytes Universal Systems, +27 (0)82 654 0290, firstname.lastname@example.org, www.bytes.co.za/solutions/manufacturing-operations
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