Snack Food Manufacturer: Standardization & Automation Transform Supply Chain Operations

Client situation

Disruptions in the distribution landscape threatened to overwhelm the company.

Costs were skyrocketing for both direct (ingredients and packaging) and indirect (MRO or maintenance, repair, and operating supply) materials alike. Despite the company’s hefty ERP and CRM investments, these systems had been configured pre-Amazon. And that was just the tip of the iceberg: The company was still configured to serve larger, traditional consumer packaged goods (CPG) channels—wholesalers, grocery chains, and route sales distributors (such as re-stocking convenience stores). But a host of new customers and channels had arisen. These included a company-owned e-commerce site, and discount clubs, with their unique needs for bulk, palletized packaging and in-display boxes.

Meanwhile, aggressive new competitors from Latin America were closing in. Fortunately, no capital spending was needed for new core systems; the existing ones already boasted the required capacity and functionality. But the good news ended there: The data for vendors, products, and customers was structured for the existing distribution customers. And even so, it had lots of flaws that had developed over time that caused order-entry errors, reduced forecast accuracy, and disrupted customer-service delivery.

The existing systems suffered from numerous shortfalls in both functionality and integration, e.g.,

• Duplicate entries
• System-to-system reconciliation
• Poor usability of master data

Things only ran smoothly thanks to the tribal knowledge that resided solely in the heads of tenured employees who were now reaching retirement age and leaving the company. The executive team needed to see benefits from the previous investments in ERP and CRM systems and upgrades—and was still waiting for the “seamless integration” vendors had promised. They needed to act quickly to stave off the growing competition and maintain—or better yet expand—competitive advantages that the company had enjoyed for decades.

Client Description, Project Scope, Objectives

The company is a U.S.-based family-owned maker of branded, baked goods, distributed across the North American market through both traditional CPG channels and, more recently, a direct-to-consumer (D2C) channel of growing importance.

The improvement effort focused on supply-chain operations, including 3,000 employees in five major business areas:

• Sourcing
• Order management
• Production
• Distribution
• Customer service

One of the first priorities was a rapid update of order management, including master data (product, customer, vendor, and order master) to accommodate the growth in distribution channels that drove operational complexity.

The objectives were clear:

• Increase order management productivity
• Reduce waste (including rework, order queueing, and customer wait time)
• Cut costs
• Introduce newfound flexibility to scale up or down as needed and remain continuously competitive in a fast moving, dynamic market space

Overview: Phase I, Process Mapping, Analysis and Discovery

The enterprise-wide process standardization and transformation initiative began with an eight-week, Phase I analysis. It documented the end-to-end (E2E) business processes from customer onboarding through order intake, sourcing, production, delivery, product promotion processing, and returned goods.

The Lab’s standardized process map and discovery templates enabled granular documentation of more than 85 percent of work activities (approximately two minutes each, or less), while only requiring one hour per week of any subject matter expert’s (SME’s) time.

The Lab’s consulting team, Business Standardization Platform and data science models mined, compiled, and generated insights for the maps from internal client applications:

• Event logs
• Work product volumes
• Cost-center data
• And more

The Lab’s external industry data provided benchmark comparisons, best-practice comparisons, and automation use case comparisons. Coordinating and reconciling the E2E maps with internal data and external benchmarks resulted in maps that could easily be directly reviewed, refined, and validated by thousands of the company’s employees.

This Phase I effort delivered a self-funding business case and work plan that launched a six-month, Phase II transformation project. Improved performance was achieved with process standardization, robotic process automation (RPA), workflow automation (minimal), and more effective and extensive use of existing systems and data. 

Overview: Phase II, Implementation

The six-month, self-funding, Phase II implementation effort was able to:

• Increase productivity
• Compress cycle times
• Improve customer service
• Deliver cost reduction

The Lab’s improvement templates accelerated the effort. These transformed the one-off activities of knowledge workers into standard work methods. The benefits of this process standardization were dramatic:

• Order-entry errors were cut by half
• On-time deliveries doubled
• Cycle time fell 30 percent
• Productivity rose 20 percent

Improvement goals were established by area and/or by E2E business process. And the organizations involved could perform the work with any mix of resources they chose:

• Internal resources
• The Lab’s resources
• Others

Improvement Examples, Supply Chain Operations Standardization

The Lab consolidated over 200 improvements into manageable “modules” for implementation. Examples:

IMPROVEMENT EXAMPLE #1 Channel Needs Standardization

Despite ten different “named” distribution channels, many characteristics were shared, e.g.,

• Demand patterns
• Order routing paths
• Service-level agreements (SLAs)
• Shipping mode
• And more

Re-grouping customers based on these channel “needs” enabled simplification to four, fundamental channel models with similar, likely order and operations issues. Digital checklists for customer service reps helped preempt half of the order errors at order intake—and also simplified the downstream supply-chain tasks.

IMPROVEMENT EXAMPLE #2 Automated Master Data Management

Existing product, vendor, and customer master data were organized according to the previous, complex distribution channel definitions. To make matters worse, master data needed to be frequently reconciled across multiple systems to support order management, customer service, and forecasting activities. The Lab tackled this issue in three phases:

• Data Definitions: First, The Lab defined a set of standard metadata, or categories, to increase the relevance of existing master data. These included SLAs, delivery preferences, order entry paths, and more for each customer and SKU.
• Master Data Reconciliation: The Lab built an RPA bot to ensure that master data definitions, quantities, and related data were in sync across more than ten systems and reference guides.
• Automated Reporting: With these upgrades in place, The Lab developed automated reports to compare major supply-chain metrics (e.g., forecast accuracy, on-time order rate, and more) across the newly-defined customer and product segments.

IMPROVEMENT EXAMPLE #3 Individual Variance Reduction

Performance varied widely among individual employees (customer reps, forecasters, analysts, operations workers). A historical review of orders and SLAs revealed that variance of 2x to 5x—or more—was common for:

• Order-entry errors
• Follow-up activities
• Cycle times for identical tasks
• And more

Existing metrics were calibrated for groups and locations. These indicated much narrower ranges of variation, typically in the 20 to 40 percent range. New, digital dashboards, powered by RPA bots reported up-to-date individual performance, helping reduce overall averages by more than half.

Post-Implementation Support, Sustainability & Automation

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.

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