Food Logistics serves the entire Printing Industry with targeted content for the large commercial print segment.
Issue link: https://foodlogistics.epubxp.com/i/295107
S P E C I A L E D I T I O N 28 A P R I L 2014 ❯❯ FO O D LO G I S T I C S W W W . F O O D L O G I S T I C S . C O M Two Sides to a Coin, Quality Assurance and Continuous Improvement By Katie Moore, Global Industry Manager - Food and Beverage, GE Intelligent Platforms F or food & beverage manufactur- ers, diversifying product lines to capture market share in consum- er 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 manu- facturing plant. In the face of increasing consumer and retailer concerns, manu- facturers cannot be driven by choosing between cost and quality—they must have comprehensive operational strategies that address both. As labor and capital prices in develop- ing countries rise and new FSMA regula- tions strain imports, supply chain invest- ments of the last decade are in need of upgrades. Through the 1990s and the early parts of the 21 st century, food & beverage man- ufacturers 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 com- pete for financial and human resources, resulting in less effective outcomes on both fronts. The net result is that manu- facturers 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 correlat- ing: reference data like execution recipes, bills of material and quality specifications; Operating data like raw material charac- teristics, quality information, consump- tion/genealogy data and process param- eter data like temperatures, speeds, pH reading, viscosity; and off-line quality test data like humidity, air pressure and clean- ing 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 prod- ucts. If no significant deviation from stan- dards is detected, then deeper scrutiny may not occur. Thus, quality systems are often posi- tioned as "insurance" and perceived as a cost of doing business, rather than a source of insight into opportunity to bol- ster profit. Continuous Improvement Initiatives Continuous improvement programs tend to focus on improving asset utiliza- tion, and yields, and will often also be aimed at defining capital improvements that can provide a "structural," sustain- able 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 ana- lyze 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 con- cerned, 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 dif- ferent data sets to isolate blocks of data. From an information perspective, qual- ity/ safety data and continuous improve- ment data are really two sides of the same coin — manufacturing data. A Single-Platform Approach to Manufacturing This is the area where properly archi- tected 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 con- textualized 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 pro- grams 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 ....SKU Proliferation As more buyers demand endless variations on familiar products, SKU proliferation is going to become more important to a growing number of consumer goods producers in the food and beverage sector. To a supply chain manager, added complexity means more of everything: raw materials, suppliers, inbound orders, safety stock, packaging. In the process, companies find themselves unable to make informed decisions about how much product to make, which SKUs to retain in the portfolio, and which ones to dump. One way to quantify the cost of complexity is to start with a SKU density analysis: which are the products that are driving 80 percent of revenue and margin? Which are the high- and low-velocity items? FLOG_28-29_0414 CS Two Sides.indd 28 4/3/14 5:24 PM