How to achieve the golden batch
The golden batch describes the perfect production batch, against which all others are benchmarked. With efficiency and productivity front of mind for most manufacturers currently, how can industry get closer to achieving golden batches? George Walker explains how operations management systems can help.
There is a quote, generally attributed to management consultant Peter Drucker, that is often repeated by many in management circles: “what gets measured, gets managed.” While the practical accuracy of the statement is sometimes argued, what is indisputable is that measurement allows businesses to identify what can be improved and how process adjustments impact output.
We need only look at the rise of Industry 4.0 in the manufacturing sector in the past decade to reaffirm this, particularly among highly regulated batch production businesses such as food and beverage manufacturers and pharmaceutical producers. Many plant managers in these sectors have spent a significant amount of time pursuing the so-called golden batch and attempting to achieve it reliably and repeatably. As such, an increasing number of sensors are introduced to production lines to measure processes.
Unsurprisingly, achieving the ideal batch is no simple task. It relies upon careful measurement of several variables. In the food and beverage industry for example, the ideal batch might feature the correct quantities of ingredients, processed for the right duration, maintained at a precise temperature and handled throughout in the right way to avoid waste or deviations.
With this many variables, measuring each batch against the profile of the golden batch requires several data collection points across a factory, alongside several systems such as Historian software to collate this data. However, despite the rise of connected systems and wider industrial availability of sensor technologies, many manufacturers continue to struggle with replicating the golden batch.
One reason for this challenge is because there may be several factors outside the core processes that can impact the profile of a batch. For example, raw ingredient quality and weather or environmental conditions can cause deviations in the final product. From Novotek UK’s experience working with manufacturers in the UK and Ireland, we’ve even observed instances where certain machinery or processes in a plant can interfere with the product quality on other lines.
Variables such as these should also be monitored and considered when evaluating the factors that influence batch quality. Effectively measuring and reviewing them requires a capable system; one that can collate data from several disparate sources and present them to stakeholders at different levels in the business. The systems should also be able to draw on historic data to help managers identify trends; if ingredient quality is consistently an issue affecting batches, its valuable to be able to cross-reference batches with supplier information.
It’s here that an effective operations management system is vital. It not only allows manufacturers to pull together data from various sources and systems, but it makes it accessible and understandable to multiple stakeholders. The system can run calculations on data and present the analysis in various views, depending on the area of interest for the stakeholder.
In terms of the golden batch, being able to dive into all the interrelating factors that influence batches makes it easier for plant managers to identify the precise conditions that contribute to optimum batch quality. In this instance, simply measuring is not enough to effectively manage batch quality; plant managers must be measuring all the right sources and presenting that information in the right way. Doing so makes achieving the golden batch a very real possibility for manufacturers.
George Walker is managing director of industrial data expert Novotek UK and