The Profitable and Safe Supply Chain

Transcription

1 GE Intelligent Platforms The Profitable and Safe Supply Chain Paper #1 in a Series: Manufacturing IT Support for Product Quality Also Preserves Operating Margin

2 The Profitable and Safe Supply Chain Introduction For consumer packaged goods companies, the smartest product strategies are double-edged swords: while diversifying product lines to capture market share in consumer micro-niches is a winning plan for growing revenue and margins, it creates challenges for operations, particularly in the areas of cost management and proof of quality, which is the foundation of a firm s brand equity. Each new product line brings a new set of recipes, specifications, quality regimens and optimization challenges to the factory; 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. Through the 1990s and the early part of the 21st century, best-in-class manufacturers used a combination of improved planning, contract production and capital investment to remove many hurdles that slowed their product diversification strategies. Extending sourcing to low-cost centers also allowed for some deflation in material price over the last few years. Now, as labor and capital prices in developing countries rise and as global media outlets focus on product safety issues increases, the supply chain investments of the last decade appear to be at risk. After so much attention and so many resources have been tasked to aligning external capabilities, it can come as a surprise to many manufacturers that the next source of strategic advantage can be found in the most unlikely places within manufacturing facilities. Even more surprising is the discovery that reinforcing either margins or quality practices creates the foundation to drive improvements in the other. We believe that parallel initiatives in these areas commonly compete for financial and human resources, resulting in lesseffective outcomes on both fronts. The net result is that CPG producers carry higher costs of production through their entire supply chain, while increasing their risk exposure. In this paper, we ll explore the commonality that underlies product risk management programs and continuous improvement programs in the areas of data collection and analysis. And we ll present concepts for more effectively deploying manufacturing IT systems that not only support both sets of stakeholders, but also bridge gaps in each group s tools and tactics by borrowing from the toolkit of the other. One Coin, Two Sides The importance of quality and safety in consumer goods is highlighted by the investment that is made in quality tools and practices. Quality programs and systems range from a robust Hazard Analysis and Critical Control Points (HACCP) plan, to Statistical Process Control (SPC) tools, ingredient track-and-trace systems, Inspection, Quarantine and Disposition workflows at material receiving and other critical junctions and a thorough process for quality review prior to shipping product. Supplier Quality Management programs reflect the same thoroughness. At the same time, internal teams concerned with continuous improvement, or Operational Excellence, are also equipped with formidable tools: Overall Equipment Effectiveness (OEE) systems, Preventative Maintenance (PM) systems and practices; even Lean and Six Sigma approaches are making headway in CPG companies. Unfortunately, the separation of these initiatives can make it difficult to understand the true profit potential of a production line, a plant, and ultimately a supply chain. And the same separation can make identifying sources of product-based quality and safety risks harder than necessary. However, looking closely at the components that make up productivity or quality programs, we find commonality that can help us close the gap. High Quality/Product Safety Initiatives Beginning with quality or product safety regimens, we can generalize and say that such programs are concerned with capturing and correlating the following types of data: Reference Data Execution Recipes Bills of Material Quality Specifications Operating Data Raw material characteristic and quality information Recipe execution data (adherence to setpoints, processing times, etc.) Consumption/Genealogy data Process parameter data (temperatures, speeds, ph reading, viscosity, etc.) Line-side quality test data Off-line quality test data Relevant environmental data (ambient temperatures, humidity, air pressure) Cleaning process data (temperatures, durations, etc.) Other counts, confirmations and checks (equipment checks, area inspections and so on) 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, 2

3 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 the following data sets: Reference Data Engineering standards for machine performance Execution Recipes Bills of Material Quality Specifications Operating Data Machine/Asset performance data, including downtime, idle time, changeover time and other non-productive time Machine/Asset fault and breakdown data Ancillary machine data (oil temperatures, motor loads, etc.) Recipe execution data Maintenance work order history 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: Ingredient/material consumption/genealogy Ingredient/material characteristic data In-line and offline quality test results Process parameter data Relevant environmental data Effective production rates (as opposed to modeled rates) More robust continuous improvement programs use all of these elements in different combinations to assess which factors truly have relationships with each other that can affect output and quality. Some common analytical approaches: 1. Pairing material characteristic data for a single critical ingredient (from many raw material lots) with finished goods data for a single product family. If variations in output rates and/or Figure 1 Combining data elements from quality and performance analysis regimens drives deeper understanding of the relationships between production inputs and assets, and the quality and performance results. quality results are seen, analyses are run to assess the impact of a series of other factors: Where run does a consistent material profile yield different results on different machines? Ambient/environmental factors do variations in temperature or humidity affect output rates? 2. Pairing machine event and fault data, with quality results tests over an extended analysis period. Most companies focus on eliminating faults and downtime for the sake of recovering capacity, and will tend to focus on engineering out root causes that drive the most downtime. However, a correlation of machine event data to quality results may expose relationships that would lead to a different prioritization of maintenance and engineering effort. In many cases, the opportunity to recover profit through yield increases outweighs fixes identified by more traditional analysis. Adding how fixed data from maintenance records to the analysis can also expose whether or not engineering efforts are actually correcting sources of loss, and if the correction is sustained over time. 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, we begin to see how these separate initiatives for quality and improvement have significant overlap. 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. 3

4 The Profitable and Safe Supply Chain From an information perspective, we can conclude that quality/ safety data and continuous improvement data are really two sides of the same coin manufacturing data. A Single-Platform Approach to Manufacturing Information Delivery Knowing that there is a case to consolidate much of the information infrastructure and toolset used by these stakeholders, the next step is to establish the principles that will guide implementation of supporting technology. At a high level, we can identify the following common needs that must be addressed: Modeling The most critical element in the manufacturing IT platform is its process and event modeling capability. The pace of product and process evolution demands that the heart of the platform be flexible enough to manage multiple distinct process types (batch, continuous process, discrete/packaging) and adaptable enough that as new equipment is commissioned, or processes are changed, the core models can be updated to reflect the new environment, without requiring disruption to ongoing production. Specifically, modeling capability is required for the following: Assets and routes Recipes and process operations Bills of materials Quality specifications, test regimens and disposition rules (by process and/or by product; linked to order execution) Asset events that will be tracked (downtime, faults, ancillary data) Alarms (based on asset or quality events) and escalation Data Collection This is the area where properly architected technology support can really bring both worlds together. To this point, we ve not addressed in detail the sources of the data that are used in quality or improvement practices. However, the sources are relevant very much as a practical matter. Limitations on the effectiveness of either type of program will be determined directly by whether a complete set of data can be made available for analysis. To create the rich data sets that we described earlier, it is necessary to incorporate as much process and asset data as possible into our 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. Figure 2 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 noncontextualized 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 Business Systems Plant Applications Plant Data Repository Plant Floor Automation Figure 3 Modeling process steps, bills of material, alarms and events enable manufacturers to automate much of the reporting that drives daily activities and provide the starting point to deeper analysis when exceptional events occur. Web Visualization PLC SCM ERP Efficiency Quality Production Batch CNC Production HMI/SCADA DCS CRM Dashboard & Web Reports Other Conceptual architecture of Proficy Plant Applications. Deep connectivity to automation data, combined with the context of rich process and event models, speeds quality and performance analysis. Web Development 4

5 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 Modern manufacturing IT foundations such as Proficy* from GE Intelligent Platforms (see architecture in Figure 3) combine both event-based data, recorded into relational database systems, with high volumes of granular time-series data collected from automation sources in order to support data collection and reporting regimens for both groups of stakeholders. 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. It is important to note that 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 as well that we see that the specific forms or tools applied tend to be similar between the quality and improvement teams. Each will begin its work by assessing Key Performance Indicators (KPIs) and drill through a mix of structured analytical tools such as SPC charts as well as relatively unstructured trend and event logs. Differences tend only to be found in what data is driven into a particular chart or view. Again, we can conclude that the correct approach is to provide an IT platform that provides common tools, with the flexibility to populate them according to the immediate needs of a given user. 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. Conclusion CPG firms product diversification strategies create opposing forces that act on profits and potentially on brand equity. While market share and profit margins are both reinforced by serving consumer micro-niches effectively, the rapid pace of product launches in manufacturing can reduce operating efficiencies. At the same time, the explosion of new recipes and specifications raises the difficulty of maintaining and providing product quality and safety. Traditional practices around quality and continuous improvement programs have led to internal competition for investment in supporting technologies, but there is significant overlap between these stakeholders with respects to data collection and analytics. 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. The financial value of this approach is realized in several ways: Continuous improvement teams are better equipped to focus their efforts on margin recovery, rather than on capacity improvement alone. Savings will be found in: - Reduced materials costs - Systemic capacity recovery (for example, yield gains in packaging operations will drive the ability to tune lot/batch sizes in processing operations.) Product Quality and Safety teams are better equipped to understand and eliminate the true root causes of product risk, rather than simply bracketing potentially unsafe or low quality products using post-production testing. Thus, quarantines are established based on specific, objective data, rather than approximations. Overlapping investment in technology and personnel support for these stakeholders can be eliminated. Strategically, this approach equips a CPG manufacturer to accelerate new product launch cycles, knowing that manufacturing is equipped with the tools to manage safe, profitable high-mix production. 5

6 The Profitable and Safe Supply Chain Expanding On This Foundation This paper is the first of a series that will explore the role manufacturing IT plays in creating financial and strategic advantage in extended supply networks. Having laid a foundation for systems in the factory that unifies support for critical functions within a single platform, we can now explore how integration, reporting and analytics that cross manufacturing IT and enterprise system boundaries are being used by leading companies to support strategic elements such as inventory and production balancing, managing supplier and contractor quality and optimizing material purchase and logistics plans. The remaining papers in the series will be released through late 2008 and early By Sean Robinson Global Industry Manager, Consumer Packaged Goods GE Intelligent Platforms Inc. GE Intelligent Platforms Contact Information Americas: or Global regional phone numbers are listed by location on our web site at GE Intelligent Platforms, Inc. All rights reserved. *Trademark GE Intelligent Platforms, Inc. All other brands or names are property of their respective holders GFT-713A