Case Studies

Fortune 500 Branded Foods Manufacturing: Product Management Standardization Reduces Waste, Cycle Times

Fortune 500 Food Producer

Project Background

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 hardcopy 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 “understandardization” 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) required for digital automation with:

  • Robotic Process Automation (RPA)
  • Workflow, Artificial Intelligence
  • Produce Lifecycle Management (PLM) software

Now, the employees performing the daily workarounds could see—painfully—the slight, undocumented differences in everything:

  • Data structures
  • Product specifications
  • Regulatory requirements
  • Distribution channel service levels

The special projects team felt overwhelmed. Everywhere they looked, they discovered more examples of understandardization impeding automation. They brought their findings to the senior management team, who contacted The Lab for standardization assistance.

Project Overview

Project Sponsor: SVP—Chief Information Officer

Client: Fortune 500 Food Producer

Implementation Results
  • Annual cost: Down 15%

  • Modification cycle time: Up 75%

  • Labeling errors: Down 80%

  • Formula-related production errors: Up 90%

  • Break-even point: 6 mos.

  • ROI (12 month): 3x

Project Scope:
  • Product Modifications
  • Food Safety
  • Product Testing
  • Quality Management
  • Master Data Management
  • Labeling
  • Packaging

Client Description, Project Scope, Objectives

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.

Three sites were engaged in the product management/modification business processes, spanning organizations with approximately 2,000 employees. New product development was handled separately and excluded from the scope of this engagement.

The original PLM technology deployment targeted three major improvement objectives:

  • Shorter modification development cycles
  • Reduced modification costs
  • 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.

Initiative Objectives:
  • Compress product modification cycle time

  • Improve technology utilization (PLM, ERP)

  • Prevent production delays

  • Reduce product labeling errors

  • Decrease product management expense

Overview: Phase I, Analysis and Discovery

The Product Management Standardization Initiative began with an eight-week Phase I analysis covering several interconnected organizational areas, including:

  • Product modifications
  • Product testing
  • Master data management
  • Packaging
  • Food safety
  • Quality management
  • Labeling

The Lab’ analysis and data science team deployed our proprietary product-management standardization templates, including:

  • Industry standard KPIs
  • Related data taxonomies
  • Business process maps
  • Operations benchmarks
  • Best practices
  • Automation “use cases”

These 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 and the client’s internal improvement team identified over 150 activity-level 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 technology after the work was standardized.
  • The remainder were automated using robotic process automation (RPA), and small, low-code applications (e.g., automated forms, APIs, etc.).
  • Many automations could be further augmented with artificial intelligence (AI) for simple decision-making and proactive, real-time notifications.

All of these improvements, moreover, could be implemented in six months or less.  Better yet, progress could be achieved incrementally, without the risk of a “bigbang” event like the PLM go-live launch. Each area could proceed at their own pace as part of a coordinated, transformational road map.

Project Summary:

Self-funding operational improvement implementation:

  • No new core technology
  • End-to-end supply-chain operations

    • 8-week analysis

    • 6-month implementation


Findings: Phase I, Analysis and Discovery

Analysis revealed two major sets of improvement opportunities:

1. Lack of process documentation.

The issues here were predictably typical.

  • Existing processes were under documented.
  • Decision rules for alternatives and substitutes existed largely as tribal knowledge, fragmented and distributed across:
    • Product lines
    • Employees
    • Locations

These issues prevented the new PLM technology from automating significant amounts of product modification work activities. Standardization would address these, and progress could begin immediately. However, the modification process was stalled with cycle times worse than before the PLM technology deployment.

2. Concentration of product modification issues

This opportunity offered major, near term relief. Analysis of several years of data revealed that a small subset of products and modifications involved disproportionately high levels of employee time and effort.

The Lab identified these and worked with the client to immediately separate the processing of these “intensive” modifications and devote them to a dedicated team. To relieve pressure on the PLM technology reboot, these were processed manually until progress was achieved on the larger population of products and modifications.

Assets and Deliverables: Phase I, Analysis & Discovery
  • 7+ major end-to-end business processes documented at “nano-scale” detail
  • 70+ process-standardization opportunities identified
  • 45+ automation candidates identified
  • 25+ advanced analytics and KPI use-cases identified
  • Performance measurement dashboards
    • AI/ML opportunities
    • Ad hoc analysis: operational, strategy

Overview: Phase II, Implementation

The six-month, self-funding Phase II implementation effort delivered rapid results, including:

  • Reduced product-modification cycle time by over 40 percent
  • Decreased avoidable errors and rework
  • 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:

  • Organizational area
  • Functional team
  • Individual employee

And the organizations involved were free to perform the work with any mix of resources they chose:

  • Internal resources
  • The Lab’s resources
  • Others


Improvement Examples: Phase II, Implementation

The Lab identified more than 150 opportunities across the seven major process areas
reviewed. Related improvements were organized into major implementation work streams, including the three examples below.


Product recipes were considered somewhat “sacred,” but this longstanding perception was constantly changed to accommodate reality: packaging changes, ingredient availability, and similar factors. However, these changes were handled piecemeal. Standardization opportunities existed to review all recipes in a coordinated manner, identifying and prioritizing alternative ingredients and documenting the decision rules in a manner that automation could process—and be configured into the client’s PLM system. At the same time, artificial intelligence (AI) could make basic decisions and enable direct transfers to the ERP system to reduce cycle times and manual transfers by employees.


Many, but not all, on the marketing-management team believed that the graphics on product packaging were, like recipes, “sacred” and must be reproduced without any changes. Yet consumer research proved that small changes went unnoticed. Labels were often changed on a piecemeal, decentralized basis to accommodate the reality of changing market needs. But these labels were subject to extensive regulatory scrutiny, which was currently handled after the graphics review by marketing—often creating a rework loop, back through marketing to revise graphics and resubmit to regulators. (Even with these “fixes,” 15 percent of labels ordered in bulk by plants had to be discarded due to label changes.)

The process was redesigned to submit draft labels for regulatory review before marketing review/redesign. After this change, label operations were centralized, standardized, and automated in a similar manner as recipes.

IMPROVEMENT EXAMPLE #3 PLM “continuous update”

The PLM did not talk to the ERP. Consequently, the master-data department was required to map ERP product SKUs and other metadata to the PLM (and vice versa), adding two-plus days to formula-revision cycle times. This also caused issues for the manufacturing plants, i.e., recipes could be adjusted at the last minute in the PLM but not reflected in the ERP, leading to possibly critical/fatal issues in production.

To shore up this weakness, The Lab worked with both the product-management and master-data teams to standardize and connect data from the two systems, so that data from the ERP could automatically flow into the PLM. The Lab also built a bot to notify master-data and product management staff when data between the two systems could not be synchronized, preempting major issues with production (recipe specs), labeling, and others.

The Lab Makes it Easy

Organization-friendly engagement design

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.

Designed to reduce risk, increase success

Since 1993, The Lab has led the industry in eliminating risk for our clients. Whether your engagement involves a handful of bots or wall-to-wall transformation, we make it easy to do business with us:

  • Fixed pricing and clearly defined scope
  • Early-out checkpoints and options
  • Money-back guarantees

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