When competitors began to chip away at this branded-foods producer’s dominant market position by rapidly modifying products tailored more precisely to the emerging needs of consumers and distribution channels, senior management decided to improve its own Product Lifecycle Management (PLM) capabilities. They quickly identified, acquired, and deployed PLM technology to replace the existing informal process that operated on a loosely structured mix of digital and hard-copy documents. The PLM vendor promised “instant” gains in productivity, cycle times, and transparency once the new system went live. Instead, however, performance nosedived soon after the new technology was launched.
Most noticeably, cycle times for product modifications and updates increased by multiples, effectively choking the flow of these processes. Formula-control capabilities eroded, creating issues with the global network of production plants, slowing product shipments, and impeding approvals by regulatory authorities. Product modifications were still possible, but the process now required numerous, inefficient manual workarounds. The situation was not sustainable.
A special projects team performed a “blitz analysis” of product-modification issues. They discovered that the new PLM technology was not highly structured “out of the box”; the buyer was responsible for adding this (even though it had been demo’ed this way by the vendor). Additionally, the buyer was responsible for connecting the PLM application to existing systems. Although neither task was unexpected, the level of “under-standardization” of the existing product-modification process was stunning. This gap in perceptions was largely to blame for the snafu with the launch of the new PLM technology.
At the time of purchase, virtually everyone involved with the existing PLM modification process perceived it as “standardized,” and it was. However, it was standardized only to a level sufficient for management by highly-experienced employees—who would take this knowledge with them when they retired or switched employers. The process was not standardized to “machinable” levels. The broad, semi-flexible standards did not deliver the rigorous, linear, step-by-step instructions with binary decision points (rules) that digital automation (such as RPA, Workflow, AI, and of course their PLM software) required. Now, the employees performing the daily workarounds could see—painfully—the slight, undocumented differences in everything from data structures to product specifications, regulatory requirements, distribution channel service levels, and more.
The special projects team felt overwhelmed. Everywhere they looked, they discovered more examples of under-standardization impeding automation. They brought their findings to the senior management team, who contacted The Lab for standardization assistance.
At more than $10 billion in worldwide revenue, this century-old company produces and markets over 25 brands of canned, frozen, and fresh foods. Healthy eating trends and growth in new distribution channels drove the need for frequent modifications of recipes and packaging for existing products. New product development was handled separately and excluded from the scope of this engagement. Three sites were engaged in the product management/modification business processes, spanning organizations with approximately 2,000 employees.
The original PLM technology deployment targeted three major improvement objectives: shorter modification development cycles, reduced modification costs, and increased management control (more precise, more visible) of formulas/ingredients. The Lab was engaged to help with standardization that would end the current chaos and get the original PLM technology back on track to achieve its intended objectives.
The Product Management Standardization Initiative began with an eight-week Phase I analysis covering several interconnected organizational areas, including:
The Lab’s database of product-management standardization templates—including industry-standard KPIs, related data taxonomies, process maps, benchmarks, best practices, automation “use cases,” and more—enabled rapid, remote documentation and analysis of more than 85 percent of employee work activities (approximately two minutes each), while only requiring one hour per week of any subject matter expert’s (SME’s) time. The analysis was conducted across three locations.
During the Phase I analytical effort, The Lab identified over 150 operational improvements. Just over 50 percent of these represented non-technology standardizations that improved data quality, reduced avoidable rework, and/or enabled automation. While the remaining improvements were technology-dependent, no new systems were required. Roughly half could be automated using the existing PLM technology after the work was standardized. The remainder were automated using robotic process automation (RPA), and small, low-code applications (e.g., automated forms, workflow tools, etc.). Many could be further augmented with artificial intelligence (AI) for simple decision-making and proactive, real-time notifications.
Better yet, all improvements could begin implementation immediately. Progress could be achieved incrementally, without the risk of a “big-bang” event like the PLM go-live launch. Each area could proceed at their own pace as part of a coordinated, transformational roadmap.
Analysis revealed two sets of opportunity:
The six-month, self-funding Phase II implementation effort reduced product modification cycle time by over 40 percent, decreased avoidable errors and rework, and improved employee productivity by eliminating duplicative operations.
A standardized set of about 15 Key Performance Indicators (KPIs) was defined to track the leading and lagging indicators of project success. Simple, automated management dashboards were published to provide constant visibility. Improvement goals for each KPI were established by area, and the organizations involved were free to perform the work with any mix of resources they chose: internal resources, The Lab’s resources, or others. The Lab maintains a three-tiered service-offering structure (plus ongoing post-implementation support) to make this approach as flexible and sustainable as possible for clients:
The Lab provided hassle-free, post-implementation hourly sustainability support for this client to maintain automations, process standardization, and operational data analytics models implemented during the Phase II engagement. If the client’s team was not up-skilled enough to perform any needed automation updates, they leaned on The Lab for Tier 3-level support. If analytics dashboards required additional views or data connected, The Lab’s team was a simple phone call away.
At The Lab, we’ve spent three decades refining every aspect of our transformation engagement model. We’ve made it easy for clients—from the C-Suite to the front line—to understand and manage the initiative:
• Minimal use of client time: One to two hours each week, maximum.
• Measurable benefits: Typical 12-month ROI is 3x to 5x.
• Pre-built templates and tools: Process maps, data models, bots, and more.
• U.S.-based, remote delivery: Nothing is ever outsourced or offshored.
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