Increasing productivity without increasing production hours

03 February 2020

A whitepaper from AVEVA explores why longer production hours do not always translate into higher productivity and capacity. It also offers advice on how to turn this around.

In most manufacturing facilities a top Key Performance Indicator (KPI) is to fulfil customer orders On Time and In Full (OTIF). With ample planning and scheduling of production operations and resources, meeting this KPI seems viable, but there are remain several pain points that hinder efficiency. In these situations, it is common to turn to immediate remedies such as adding production overtime and delaying or shortening planned maintenance in an attempt to make way for more production capacity. However, this does not necessarily correlate to an increase in profitable output. 

A 2013 McKinsey report  entitled ‘My manufacturing plant is better than yours – or is it?’ highlighted that when looking at optimising production processes many companies only gather and focus on high-level data an this can obscure deeper insights on specific processes involved in the production line. 

Poor metrics and lack of transparency are among the factors that draw a challenging picture. Improvement actions and internal best practices really need to go hand-in-hand to get the best out of the production lines. 

Identifying these gaps and the root causes of low productivity in time-critical production processes can help companies realise how to get systems and operations on the right track. 

Poor productivity
According to the known Total Productive Maintenance (TPM) and lean manufacturing programs, the most common causes of poor productivity have been identified and categorised as the ‘Big Six Losses’ which affect the availability, performance and quality factors of the overall efficiency of the operations: 

Availability: Unplanned stops (equipment failure); and planned stops (setup and modifications) Performance: Small stops (idling and minor stops); and slow cycles (reduced speeds)

Quality: Production discards (process flaws); and startup rejects (reduced yield). 
In order for manufacturing plants to be efficient, information such as production operations, quality, asset health and inventory status are essential for monitoring, analysing and continuous improvements. Data collection in a timely and effective manner is the first critical step to ensuring this information is accurate, meaningful and in time to support data-driven business decisions.

Manufacturing plants that are using spreadsheets for data handling often find that personnel are regularly buried in paperwork. This results in operators spending a considerable amount of time and effort to record production data while supervisors will need to manually assign jobs and consolidating data. Engineers will reach out to various sources to obtain data for analysis while plant managers will struggle to make operational decisions that may impact production targets without access to up-to-date or current information. 

In time, all these manual processes lead to low supervision with minimal feedback on what is happening on the production lines. With a whole lot of possible faults that may cause line stoppages, the plant floor could be a firefighting zone through the day. 

The ensuing low productivity is an indication of time wastage and effort with non-productive activities such as: 

• Spending time to find information to help make critical decisions. 
• Spending time to understand the cause and impact of production losses so that corrective actions can be taken. 
• Inability to efficiently plan production and operational activities based on available resources and capacity. 
• Inability to efficiently respond to unplanned events. 

Poor production performance and unplanned losses lead to low productivity. Broadly speaking there are three main unplanned loss categories – Equipment failure or stoppages (due to shortage of materials or downstream operations); process failures (unable to reliably produce with the expected throughput, rate or speed); and quality failures (the finished product is not in line with the specified or required quality). 

Low productivity impacted by these production performance losses leads to lower production capacity, unmet production targets, quality issues, waste and lower yields among other critical points. 

A vicious cycle
Some immediate remedies to the above issues has, traditionally, been to add production overtime and delay or shorten planned maintenance. Although these actions may open up more production capacity initially, they can lead to a higher probability of human error due to worker fatigue and equipment breakdown, which may eventually negate those initial improvements in production capacity. Such decisions also fail to address intrinsic process issues, such as material availability at the line. 

Stretching the limits of workers may also distract them from working on value-adding activities and continuous improvements that eventually result in more efficient processes and add to the bottom line of the business.

With this shorter-term view, production performance eventually takes a dip and the pressure on production operations increases. This forces manufacturers to open up more production capacity and so the vicious cycle of production goes on. 

Continuous improvement of equipment effectiveness and operational enables manufacturers to take advantage of new technologies and look towards digital transformation of operational processes to help achieve lower cost of operations, optimised productivity and capacity. 

But, with so many available technologies on the market, where should businesses start in their digital transformation journey? The first step must be to assess where they currently are in their digital maturity journey. There are broadly three phases: 

Fundamental phase: Digitising operational excellence practices on the plant floor 
Site phase: Addressing specific operational issues at-site and applying model-driven MES based on industry best practices 
Enterprise phase: Driving reduced cost and revenue growth through operational best practices, agility and standard score cards, and applying multi-site model-driven MES based on a line of business templates 

Once manufacturers determine where they are currently, the next step is to evaluate solutions that match the business objectives and readiness. There are ROI opportunities for manufacturers at every digital maturity level, but there is no ‘one size fits all’ solution. 

Increase worker productivity 
Worker productivity related to unplanned outages is best addressed by giving visibility into line operation and production losses. 
The implementation of manufacturing operations management systems needs to support production staff to measure, visualise and correct variations to expected efficiencies around labour, quality and equipment utilisation, and waste elimination. The main expectations that a factory with a system-driven production line would look forward to are: 

• Data collection – fully automated or semi-automated 
• Data categorisation or arrangement – fully automated 
• Dashboards for at-a-glance understanding of performance and issues 
• Ready reports and analytics for analysis and continuous improvement activities 
• Mechanisms to ensure the right action is done by the right personnel at the right time

Manufacturers of all sizes will have some methods of measuring and decision-making support in place, even if it is based on manual data collection, but it is important to replace this patch work of manual, partial systems with an integrated single digital system that brings operational visibility to all personnel within a plant and across multiple plants. An effective manufacturing operations management system is beneficial for optimisation of human resource and equipment. 

Short Interval Control (SIC) is a lean concept based around the provision of feedback to an operator of results within a short timeframe, enabling them to initiate immediate corrective actions. Systems like Line Performance are essentially SIC tools giving visibility into the unplanned losses in a near time basis, via both a ‘score’ (Overall Equipment Effectiveness) and the underlying events that are affecting the current score. This allows operators to focus on what is currently impacting their scores and increase their productivity. 

Improving equipment reliability and utilisation should be a two-fold approach:
• Adopt a maintenance strategy that sustains smooth equipment operations; 
• Deploy a continuous improvement program that analyses loss data and determines root causes which can be addressed either through maintenance improvements, equipment replacements or other changes, such as upgrading projects. 

For equipment reliability and utilisation: 
• Automatically categorising and arranging data collected by the system to display details and analytics means that the information is ready whenever the engineer needs it for continuous improvements activities. This includes highlighting where equipment performance issues are and reveals where they should focus efforts. This shortens the time required for investigation. 
•Capturing the resolution into the system enables continuous improvements to always be enforced. This ensures that the same issue will not recur and performance can be sustained. 

Conclusion
It is essential to obtain and sustain cost and productivity targets on manufacturing operations. With the use of digital technologies, manufacturers will be equipped to discover and locate process weaknesses and maintain maximum throughput. Systems need to be easy-to-use; should have a rich, out-of-the-box functionality that provides faster time-to-value and should be able to adopt and complement individual operational practices. Finally, such a system should be able to deliver low total cost of operations (TCO). 

A model-driven approach – which encapsulates industry best practices and continuous improvement methodologies – will enable the manufacturing operations management system to go beyond production loss identification, to include performance sustainability, standardisation and governance, workforce engagement and accountability.


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