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02/12/20 – Quality Series / Manufacturing / Digitalisation / AspenTech

Digital diligence: Digitalisation and food quality

Increasingly, the most progressive food and beverage manufacturers are turning to digitalisation to maintain optimum quality standards, writes Laura Stridiron, Senior Product Manager at AspenTech. 

Many food and beverage manufacturers today are looking at how they can best make use of digitalisation to drive operational efficiencies. Yet it is equally if not even more crucial that they focus on the role digitalisation can play in optimising overall product quality and thereby delivering a competitive edge.

Maintaining optimum quality standards is among the most important – and also most difficult – challenges facing manufacturers across this sector. While quality control has defined processing parameters of variables, including temperature, time and good manufacturing practices (GMPs), there are stricter regulatory requirements resulting from sources like the Food Safety Modernization Act (FSMA) in the US, and a new trend toward ingredient traceability as a key component to strengthening consumer trust.

Adding to the challenge, some factors impacting food quality are hidden. Even with specifications defined within a product formulation, minor variations in processing may arise. These differences can steer production off-course. In spite of traditional means of recording process data, quality compromises may arise from: minor, but acceptable discrepancies in raw material properties; variations in procedures; or changing environmental conditions.

Without understanding the exact variances (not always visible to humans), production cannot easily forecast the outcome or effectively compensate, potentially resulting in an expensive defective batch.

Seeking new insights

Often, an organisation’s existing data is crucial to improving their confidence in data. Especially in the food and beverage industry, mountains of data exist to comply with food safety tracking requirements. Yet information like maintenance work orders and compliance reports typically reside elsewhere in an organisation, potentially separated from related material.


This division of data means past efforts to mine it may not have yielded notable results. As digital transformation efforts increase, some businesses are looking to this historical information to give them an insight into how to enhance quality moving forwards.


So what is the first step in leveraging past production data to provide insight? With traditional data analytic methods, many businesses have to hire data scientists to conduct complex analyses. However, in addition to being unrealistic for most organisations, this approach ignores the other subsets of data in companies’ hands: the ‘hidden factories’ or inherent knowledge of those employees who are intimately familiar with the ins and outs of the process.


While they may not have been the creator of the product itself, they are familiar with how to execute on the product formulation given the surrounding circumstances. Even with sophisticated tools gathering and organising key data, a challenging task remains: Analyse the data to determine how best to adjust the batch process for each variable and accomplish an improved orders outcome. With many data visualisation and analysis techniques, it may still be a complex challenge for operators, engineers and analysts to sift through data patterns and identify how best to adjust processes. 


Delivering enhanced production supply quality

The more quickly you can turn raw data into actionable insights, the better – particularly if you can do it without teams of new resources. Multivariate analytics software can help solve your process and product quality issues, which could likely result in greater customer satisfaction.


These tools can help optimise production by: reducing off-spec product; minimising product rework needs; enabling more proactive schedule changes; and decreasing lead time for customer orders outcome.

AspenTech’s Aspen ProMV is one such solution, specifically designed for use by those most familiar with the process, rather than data scientists. One international food manufacturer uses it for maintaining quality while increasing supply chain flexibility. This company traditionally tested incoming raw materials as its primary method for predicting overall product quality – yet that methodology was resulting in unreliable outcomes and an unacceptable level of off-spec product. Having multiple raw material suppliers for each ingredient of a specific product, the customer was faced with a dilemma on how to best pre-determine the outcome of final product quality.

With the availability of historical data of the product’s raw material lots and variable processing conditions, Aspen ProMV was utilised to develop a comprehensive data model that correlated these two factors. The data associated with the model revealed two raw materials had no significant impact on final quality, while three others did. Therefore, the manufacturer was able to determine where to focus their raw material specification efforts to eliminate future off-spec product and adjust their manufacturing process to improve overall quality. By using Aspen ProMV, the customer could scale the same modelling technique across their whole product line. This helped them avoid raw material combinations that would have led to future poor final quality – ultimately avoiding potential customer satisfaction issues. Moreover, Aspen ProMV provides continuous monitoring to alert when potential combinations of process conditions could result in off spec product based on the raw materials in use.

It may be just a single example, but it nevertheless clearly illustrates how ultimately, for food and beverage companies, utilising multivariate analytics as part of a digitalisation strategy to gain enhanced visibility into raw materials and process conditions supports a fast track to improved product quality overall.

To learn more, visit:

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