For food & beverage manufacturers, diversifying product lines to capture market share in consumer micro-niches can be a double-edged sword. Multiple product lines can help grow revenue and margins, but it creates challenges for operations. Each new product line brings a new set of recipes, regulations, quality regimens and optimization challenges to the manufacturing plant. In the face of increasing consumer and retailer concerns, manufacturers cannot be driven by choosing between cost and quality—they must have comprehensive operational strategies that address both.
As labor and capital prices in developing countries rise and new FSMA regulations strain imports, supply chain investments of the last decade are in need of upgrades.
Through the 1990s and the early parts of the 21st century, food & beverage manufacturers focused much of their attention on external capabilities such as contract production and sourcing; however, the next source of strategic advantages will be found within manufacturing facilities themselves.
The parallel initiatives of improving profit margins and quality commonly compete for financial and human resources, resulting in less effective outcomes on both fronts. The net result is that manufacturers carry higher costs of production through their entire supply chain, while increasing their risk exposure.
High Quality/Product Safety Initiatives
In general, quality and safety regimens are concerned with capturing and correlating: reference data like execution recipes, bills of material and quality specifications; Operating data like raw material characteristics, quality information, consumption/genealogy data and process parameter data like temperatures, speeds, pH reading, viscosity; and off-line quality test data like humidity, air pressure and cleaning process data.
Correlating this data to production lots and to production lines creates the record set that eases decision support for final product release, smooths customer or regulator auditing and also supports root cause analysis when quality issues occur.
However, these quality assurance tools are generally applied primarily to prevent release and distribution of at-risk products. If no significant deviation from standards is detected, then deeper scrutiny may not occur.
Thus, quality systems are often positioned as “insurance” and perceived as a cost of doing business, rather than a source of insight into opportunity to bolster profit.
Continuous Improvement Initiatives
Continuous improvement programs tend to focus on improving asset utilization, and yields, and will often also be aimed at defining capital improvements that can provide a “structural,” sustainable improvement in output, and/or to incorporate new processing or packaging capabilities.
Common practices require that teams gather, correlate and analyze reference data sets like engineering standards for machine performance and operating data like machine/asset performance data, including downtime, idle time, changeover time and other non-productive time.
Companies that have progressed along their continuous improvement journey will expand this kind of regimen to address the effect of quality on throughput and schedule performance, and will thus analyze additional factors like the operating, manufacturing and reference mentioned earlier.
Overlap of Separate Initiatives
Looking at the data elements with which each type of program is concerned, and also identifying the sources of information each program relies on highlights the significant overlap between these separate initiatives for quality and improvement.
Most of the differences between the groups’ usage of data are in the area of analytics — how different elements can be correlated and compared, and what boundaries or filters can be placed on different data sets to isolate blocks of data.
From an information perspective, quality/ safety data and continuous improvement data are really two sides of the same coin — manufacturing data.
A Single-Platform Approach to Manufacturing
This is the area where properly architected technology support can really bring both worlds together. To create rich data sets, it is necessary to incorporate as much process and asset data as possible into the regimens. While automation data is readily available via Ole for Process Control (OPC) or other technologies, to be useful, the data extracted must be contextualized to the same “markers” as non-automation data – orders/lots, assets, process stages, specific quality or safety checks, etc.
This is, in fact, one of the areas where quality and continuous improvement programs have diverged in the past. Quality systems have been focused so much on specific, relatively infrequent (minutes or hours vs. sub-second) events such as quality tests or logging of ingredient consumption, that many of them have been deployed in fairly traditional operator-entry styles.
Continuous improvement programs aimed at asset utilization or process tuning have tended to focus on large sets of non-contextualized machine data, looking for trends or patterns independently of “markers” such as materials in process or product types.
While recent trends in OEE analysis and yield analysis have driven engineering teams to include more contextual data in their analyses, the reverse is not as true for the adoption of rich automation-based data into quality and safety regimens.
As a result, a quality issue may lead to a uniquely designed investigation that must be re-invented when the next risk is recognized, and the next, and the next and the next…
By automating the reporting regimens used for daily performance monitoring as well as for deeper analysis, such platforms ease the investigative processes that are triggered by exceptions in relation to quality or to process performance.
Reporting and Correlation
Extending the contextualization of the core models to automation data provides the basis for a truly powerful correlation capability that enables a manufacturer to extend the analysis of product risk or throughput losses across a number of dimensions.
Such multidimensional analysis is eased when supported by a detailed process and event model; the model creates additional markers that can be used to delineate the boundaries of different inquires.
It is in this area that the specific forms or tools applied tend to be similar between the quality and improvement teams.
Sustaining Gains Over Time
Beyond the simple consideration of easing deployment, the common platform creates additional value:
• A single platform as the source of manufacturing data eliminates doubts about corrective action or remediation activities that are caused when multiple stakeholders present conflicting views of an issue.
• The common toolset makes cross-pollination of reports and analysis easier. As quality and improvement teams develop a deeper understanding of the interrelationships between the factors they analyze separately, the common framework will foster cross-functional collaboration, and development of improved work processes and related analytical support.
Food & beverage companies’ product diversification strategies create opposing forces that act on profits and potentially on brand equity. While market share and profit margins are both reinforced, the rapid pace of product launches can reduce operating efficiencies.
A better approach to supporting both sets of stakeholders is to develop work processes that incorporate data collection for both sets of requirements, and to supplement those work processes with systems that reduce the intrusiveness of data collection on production workers.
With a robust set of data available, addressing the specific needs of risk management teams and continuous improvement teams becomes a matter of tailoring information integration and reporting to support the specific requirements of these critical functions.
Strategically, this approach equips a food & beverage manufacturer to accelerate new product launch cycles, knowing that manufacturing is equipped with the tools to manage safe, profitable high-mix production.