Putting the ‘man’ back into manufacturing

SINCE the industrial revolution the holy grail of manufacturing has been a fully automated system that runs on a demand basis at full efficiency and minimal labour cost. Yet the reality is that quality standards can only be guaranteed with human involvement and with consumers increasingly looking for product customisation, the assembly line approach is not cost effective.

SINCE the industrial revolution the holy grail of manufacturing has been a fully automated system that runs on a demand basis at full efficiency and minimal labour cost. Yet the reality is that quality standards can only be guaranteed with human involvement and with consumers increasingly looking for product customisation, the assembly line approach is not cost effective.

Measuring that efficiency has typically used a technique such as Overall Equipment Effectiveness (OEE). Plant managers can evaluate the performance of a single piece of equipment, or even an entire factory, by looking at three factors: availability, performance rate, and quality. Where OEE comes up short is its inability to provide visibility into the interaction of labour and equipment and provide a more complete understanding of workforce performance.

The question then is how do you optimise the performance of your workforce while achieving visibility to effectively measure it? Overall Labour Effectiveness™ (OLE) is a complementary key performance indicator that considers the impact of the workforce in a manufacturing environment. OLE quantifies, diagnoses, and ultimately predicts the performance of the workforce. It helps managers understand how the workforce influences profitable production and points to the root causes of ineffective labour deployment. As a result, OLE can show how assets and employees come together to drive great performance.

Like OEE, OLE can be determined by analysing the cumulative effect of three workforce factors on productive output: availability, performance, and quality.

Availability

Many factors influence workforce availability, including:

• Absenteeism and utilisation – employee illness, approved and unapproved leave, and unavailability due to meetings, training, and other company-defined activities.

• Scheduling – beyond merely providing a worker you must consider employee skills and certifications, as well as flexible work schedules.

• Indirect time – material delays, idle time, shift changeover, and machine downtime all contribute to indirect time.

Performance

The performance component of OLE records output — did it take as long to produce or deliver a product or service as your labour standards indicated? Factors to consider include:

• Availability of processes – instructions, tools, and materials. Shop floor issues such as worn tooling or material shortages will slow production.

• Training and skills – do employees know how to complete their assigned tasks?

• Indirect support staff – an insufficiently skilled workforce will require additional support from supervisors, maintenance, and quality assurance personnel.

Quality

Ultimately you need to know if the output of production met your specified quality levels. While quality is certainly a function of the materials used, it is also impacted by human factors, such as:

• Do employees understand the quality drivers for their operations? Knowledgeable operators understand how processes operate, how variability affects quality, and what adjustments keep processes in specification. They also know when to stop production for corrective actions if quality falls below specified limits.

• Are employees using instructions and tools properly?

What OLE Can Tell You

The power of OLE is its ability to show cause and effect. It identifies problems that cut into profitability and shows how investments in human resources pay off. Some examples of what managers can learn from OLE include:

• Root cause insights. Recognising that a maintenance crew spends an inordinate amount of time in one production area, a manager sees that a particular piece of equipment needs to be replaced. Digging further into OLE, the manager finds the root cause – a higher volume on the equipment that correlates to a change in incentive pay, which unfortunately, promoted bad operational practices.

• Predictive measures. OLE can provide insight into the causes of manufacturing inefficiencies. For example, after overtime rose 10 per cent in a recent period, a manager learns that when several new technicians were hired, the average skill level dropped and the average time spent per assembly rose 15 per cent.

• ROI on training. Measuring the effect of education is something every manufacturer wants to do but few have been able to achieve. Using OLE, manufacturers can pinpoint the cause, invest in training, monitor specific increases in quality and performance, and recognise the improvement in productivity. Most importantly, the results of training can be monetised for ROI calculation and justification.

By having a software application to collect the information and collate it into an easy to use portal, OLE can show how assets and employees come together to drive great performance. Interdependent variables and difficult-to-identify relationships are exposed in real time, showing how changes made to improve one area may lead to a negative impact elsewhere. Trends that individually are too small to be noticed are highlighted earlier because their cascading effect on total performance is apparent. Executives throughout the organisation have hard facts to use in analysing the effective contribution of the workforce and can act on them almost immediately.

OLE helps manufacturers develop a highly motivated, effective workforce by helping them identify where people need better processes, materials, training, or indirect support. It’s a productivity tool for managers which provides the visibility required to manage better as they convert labour dollars into profits.

* Peter Harte is general manager of Kronos South East Asia.