Optimising the Quality Management System in Dairy Processing

Abstract

Milk has a solid reputation as a staple food since time immemorial. It is a complete food in its raw form, high in fat, protein, vitamins and minerals, including calcium. While the most beneficial first food for mammals is mammalian milk until weaning, cow’s milk and dairy derivatives are considered significant nutritional components in the human diet. While milk consumption has in fact sharply declined in recent decades, the consumption of liquid milk derivatives and dairy products has steadily increased. Quality in terms of product, process and the environment in a milk production plant can be measured through performance, reliability and durability. The quality management system, in whatever form that may take within a plant, is the pinnacle in ensuring how one organisation can differentiate from its competitors. Quality systems and analytical testing protocols, especially in the dairy industry, are seldom quantified or fine-tuned to guarantee their efficiency. Furthermore, the impacts of quality systems on process, product, and environmental optimisation are frequently overlooked. This chapter reviews the activities that allow for the optimisation of quality systems in a dairy processing environment. The outcomes of which highlight the importance of process based quality systems.

1. Introduction

Definitions of quality within the literature are manufacture and user based. W. Edwards Deming believed the definition of quality has a user based perspective in that the product fulfils the needs and expectations of the customer [1]. Philip Crosby believed the system of quality, was prevention, and conformance to requirements and was manufacturing based ([2], p. 116). A mixture of both concepts derived from Deming and Crosby can be highlighted through David Garvin’s perspective on the quality concept. He identified five approaches: The transcendent, a perspective that states quality cannot be defined exactly. The product based view, a perspective that states quality can be measured exactly through the desirable characteristics a product should possess. The user based view, stating that the customer judges quality. The manufacturing based approach, conveys production requirements and finally the value-based approach, whereby quality is defined in relation to cost and price [3]. All of these principles are easily applicable to the dairy industry.

Increasing consumer demands for high quality dairy products puts a burden on dairy producers to manufacture top grade product that is free from microbiological, chemical and physical contaminants. To comply with regulatory guidelines routine inspections are carried out from milk intake to finished product [4]. While milk composition is nutritionally important to consumers, its composition and safety is equally important to milk producers and processors [5], as the quality demands of the product will influence processing parameters and final yield [6]. Inspections are based on randomised sampling and process control samples, where analysis can occur hourly on the production line and on bags of finished product, all of which are outlined in a company’s SOP and analytical test matrices. Test matrices are simple tools for visualising how often and for what analysis, a sample of product should be tested.

Total quality management (TQM), is an application of quality management principles that are applied to all aspects of a business. The food safety management system (FSMS) is an integral part of the TQM in any food manufacturing company [7], including dairy manufacturing plants [8], examples of which include, HACCP, FSSC (Food Safety System Certification Standard), IFS (International Food Safety Standard), SQF (Safe Quality Food) and ISO (International Standards Organisation). HACCP [9], is a global quality assurance system, ensuring products are produced in the safest way possible and are compositionally stable for product formulations [10]. The HACCP system is widely used across the Irish dairy industry. TACCP (Threat Assessment Critical Control Point) and VACCP (Vulnerability Assessment Critical Control Point) are relatively new concepts. Where HACCP prevents food safety issues [11], TACCP identifies deliberate infidelities to a food product [12], finally VACCP assesses how vulnerable various points are along the process chain [13]. The general scope of VACCP is that raw material supply chains are protected, and TACCP protects all aspects contributing to raw materials from intake to consumer; both go hand in hand [13].

Significant gaps however, can be seen across the dairy FSMS globally. For example, melamine is a crystalline compound made by heating cyanimide [14], in 2008, the Sanlu Dairy Company in China were responsible for adding melamine to watered-down milk. Melamine is rich in nitrogen and the addition of the poisonous substance was to increase the apparent protein content in the product. More than 290,000 people, mostly infants ingested the contaminated product, six infants died, before the Chinese government issued a product recall [15]. More recently, Lactalis, a multinational French dairy corporation, was involved in a contaminated milk scandal at the end of 2017, when infants ingested formula which was contaminated with Salmonella agona at their production plant in Craon [16]. Both of these scandals involved flawed quality management systems whereby production operators, laboratory technicians and management were aware of contamination issues, without taking steps to control production.

To ensure high yield and exceptional quality [17], quality systems in any food industry must be established from raw materials right through to finished product [18], with sampling inspection plans [19]. This is achieved through analytical testing and sampling strategies [20] which are governed by legislative and regulatory guidelines and standards. Generally, within the Irish dairy industry inspection is based on EU Directives and Regulations. The EU Regulation (EC) No. 178/2002, states that it is necessary to consider all aspects of the food chain as a continuum to ensure food safety, furthermore, it states scientific risk assessment alone cannot guarantee food safety along the production chain and societal factors, environmental factors and ‘feasibility of controls’ should also be considered [21]. Guidelines on sampling can be product or process based [22] and are routinely carried out from milk intake to finished product.

2. Dairy processing and quality

Quality control for a safe product must be the objective of any food processing industry. In reality, the universal objectives to any process are minimising cost and maximising throughput, and/or efficiency ([23], p. 205). A network of interrelated activities that are repeated over time can be defined as a process ([24], p. 456), adjusting the process so as to improve specific parameters is termed ‘process optimisation’. Additionally, many factors can relate to the performance of a process, for example the condition of a machine/instrument, processing parameters, environmental variables and material quality ([23], p. 211; [25]). Food processing is defined as any procedure undergone by food commodities, after they have left the primary producer and before they reach the customer [26]. In terms of dairy production, processing can take many forms and vary in the degree of complexity, i.e. from fluids to solids. In its simplest form, processing may involve no more than controlled storage such as refrigeration. At more complex levels, commodities may be processed to yield ingredients that are later combined to yield foodstuffs, for example, the conversion of liquid skim milk to skim milk powder. The revolutionary changes in manufacturing and the advancements of the dairy industry over the past 30 years have increased the need for optimised quality assurance and quality control methodologies. ‘Total quality control (TQC) is a thought revolution in management’ [27] and is deemed as a continual practice [1]. It is a flexible system whereby both processes and methods can be easily transformed. Jagdev et al. [23] explored a range of techniques to enhance process optimisation and concluded that a knowledge based system, analysing historical and real-time events on an on-going basis, enables the identification of any variations likely to present within a process, which would contribute to overall process optimisation. Process optimisation and enhanced quality, affects the success of a manufacturing plant in terms of higher productivity and reduced costs due to out of specification (OOS) product and downgrades [28].

3. Techniques to optimise quality

Research findings suggest the basis for quality improvement needs to be understood by everyone at all levels of production ([24], p. 460; [17, 28]), for instance in a milk production plant, from milk intake all the way through to finished product. Dr. Kaoru Ishikawa defined ‘the seven improvement tools’ in response to the realisation that everyone in a company had to become involved in improvement works, thus they needed to be simple and effective tools [27]. The set of tools within the toolbox methodology can vary across the literature; however, the end objective is always the same whereby they are systematically designed for problem solving and optimisation. As the ‘Define’ phase of the Six Sigma methodology states, the collection of data is a fundamental step in the road to quality improvements [29], Ishikawa’s Seven Improvement Tools also demonstrated the importance of this step [27]. Histograms and Pareto charts, can be used as visualisation tools, as an important part of data analytics is to represent data efficiently and that can help in the decision of which problem should be solved first [30]. Once data has been analysed the root cause of problems within a process has to be defined. This can be achieved through the use of a cause and effect diagram [27, 29]. A tool that allows the description of causes that can possibly lead to quality problems and allows for the investigation of each cause, one problem at a time. One common problem however, with the use of cause and effect diagrams, is that a quality team can start with the major problems while the root causes of the minor ones become harder to solve [24, 31]. Stratification is another problem solving tool within the seven improvements toolbox, based on classifying data from different sources into subgroups and obtaining important information for improvement work [27]. Scatter plots can be used when stratification cannot, to identify process or product variations due to explanatory variables [32]. Finally, the use of control charts in quality optimisation has two main objectives, the first being to identify causes of variations and secondly to quickly detect when variations occur in a stable process [31].

4. Laboratory activities

A vital part of any quality system are laboratory quality standards [33, 34], and an increasing number of laboratories within the dairy industry are operating to accreditation standards. Calibration and testing activities can either be partially or fully accredited [35]. In Ireland, accreditation can be granted by the Irish National Accreditation Board (INAB), a voluntary scheme open to laboratories within the country, which offers accreditation to the international standard outlaying requirements on testing, personnel, confidentiality, laboratory facilities and environmental conditions [36]. In recent times, clinical laboratories have considered laboratory turnaround time (TAT) an important key performance indicator (KPI) of laboratory performance [37], defined as the time it takes for a test to be ordered to the time it takes for a result to be delivered. This is a topic however, that has not been widely researched in relation to food, and more so dairy manufacturing. Activities are carried out within dairy manufacturing plants with little consideration to downtime or the effect of sampling and analysis on personnel labour hours [34]. A study carried out in 2014, on methods of identifying unnecessary laboratory testing in a tertiary care hospital deduced that a user approach as well as a systems approach would ultimately reduce over-testing [38]. Within Dairy manufacturing plants however, over testing is more times than not, due to out of specification test results (OOS), defined as a test result that falls outside of the established testing criteria [39]. While an initial OOS result does not mean a failed batch, the concept of ‘testing into specification’ rather than applying an investigative approach to determine the root cause of such a result, often leads to unnecessary over analysis, ultimately negatively affecting cost and laboratory TAT. Investigating OOS test results should be thorough and timely and include the use of scientific decisions, justifications and risk based analyses [40]. Furthermore, applying a strategic in-process sampling system would minimise the level of OOS in final product. In-process sampling should always start at the beginning of the process to allow for early detection of processing issues. Finally, managing test results can be a complex process if it is not performed in an accurate manner. Improving how test results are recorded and communicated can achieve savings in staff time and improve the overall reliability and communication of the results. OOS results can be due to a number of reasons including failed equipment or reagents, in process issues and human error.

5. Advancing quality in dairy processing

The global dairy industry is fast evolving, as are trends in consumption patterns. The issues surrounding environmental sustainability and increased production demands are placing pressures on the dairy manufacturing industry to produce product in adherence to regulatory bodies and consumer requirements, as well as keeping costs to a minimum. Manufacturing product that is chemically and microbiologically safe for human consumption however, is the core of any production plant and ensuring that efficient quality systems are put in place to capture this is vital. The literature suggests however, that flaws can, and do exist within quality systems and this is evident within the dairy manufacturing industry. There are areas for optimisation in quality systems within the dairy industry. Trend analytics of historic data and making scientific and industry contrasts concerning quality systems will enable dairy manufacturers to become more in control of processes, which will effectively drive costs down.

To ensure high quality products are delivered to the consumer, the dairy manufacturer must evaluate milk quality throughout the value chain and be strategic in their approach to minimising product contamination and upholding quality. De Silva et al. [41] strongly recommended that following good management practices (GMP) will uphold product quality. GMP is described by the Codex Alimentarius; hygiene in the primary production, hygienic design of equipment and facilities, control of operations, maintenance and sanitation practices, personal hygiene, transportation, product information and consumer awareness and training [42]. Based on the PDCA (Plan, do, check, action) cycle, the implementation of GMP is a continuous process [43]. The roadmap to implementing GMP is led by strategic management where there are three basic elements; (i) The formation of a strategy, (ii) the implementation of a strategy and (iii) the control and evaluation of a strategy [44]. Considering the many variables involved in the milk processing and the effects they can have on quality and composition, having a strategic quality management system in place is critical. Moving from a product based testing regime to a process focused approach, has been widely adopted by the food and pharma industry in the last decade. An exclusive product based inspection system has proven to be an inefficient method of regulation, however a poorly monitored process based level of inspection can be just as incompetent.

An effective quality management system (QMS) will document processes, procedures and responsibilities, that will enable a company to meet customer and regulatory requirements [17]. Additionally, the effective management of data will facilitate a quality team’s decision making surrounding process and product variability. According to Strong et al. [45], the barriers to data accessibility are poor systems, the timeliness of inputting data and accessibility to data systems.

The successful management of quality and traceability is dependent on a company’s ability to demonstrate product quality and safety to regulators, customers and consumers. Influences on process optimisation and quality within the dairy industry can be multi-factorial including: seasonality; the quality of incoming milk and herd health; the level of skilled laboratory technicians; the level of production and the digital capabilities of a dairy processing company can all have an impact on optimising the quality management system.

6. Conclusion

This chapter critically assessed the factors that affect optimal dairy quality systems. The basic role of continuous improvement is – there is always a way to get improved quality. This review highlighted the necessity for heightened awareness needed among dairy processors to allow for a synergistic affect that all aspects of the quality system have on product quality and process performance. It also highlighted the value in-process based sampling has on final product output within a dairy plant as well as monitoring the efficiency of the process in terms of waste and energy usage. Finally, this review encourages any dairy manufacturer to consider all inputs and outputs of their entire product line to achieve high quality product and uphold brand reputation.

Funding

This research was funded by the Irish State through funding from the Dairy Processing Technology Centre’s program - Grant Number TC/2020/028.