In the second part of my thoughts on manufacturing transformation readiness, we will explore what “good data” really means and how stronger data practices can directly improve planning, reduce waste, and drive better financial outcomes.  If you haven’t already, please check out the first part of this series here.

What Good Data Looks Like: Data Hygiene

One recognizable platform suggests seven key dimensions that define healthy, reliable data. These concepts apply across manufacturing operations, regardless of the system being used.

1. Accuracy – The extent to which data objects correctly represent the real-world values for which they were designed.
2. Completeness – The extent to which data is not missing.
3. Conformity – The extent to which data conforms to a specified format.
4. Consistency – The extent to which distinct data instances provide non-conflicting information about the same underlying data object.
5. Integrity – The extent to which data is not missing important relationship linkages.
6. Uniqueness – The extent of uniqueness in naming and representation of core data objects.
7. Timeliness – The extent to which data is sufficiently up to date for the task at hand.

It is important to not look at the data in a vacuum, always in context. Does the data serve SCES’s functionality? Does that serve the business? As businesses increasingly explore advanced analytics and AI tools, the quality of underlying data becomes even more critical. Poor data hygiene cannot be masked by smarter systems.

Improving Planning and Yield Through BOM Accuracy

The early part of my career was spent in a lipstick factory, for a leading global cosmetics company. We had a top-tier ERP provider selected and I was on the local implementation team. Part of this system transformation process was to evaluate material usage and scrap on the BOMs. The analysis provided a perhaps surprisingly impactful cascade of results. The manufacturing is split into two parts processing the bulk cosmetic and then filling and packaging the finished good on the production line.

Turns out our planned production run sizes did not align with the yield of the bulk processing. In a make-to-stock business, where the ratios of chemistry are important and batch sizes fixed based on processing equipment, we were under running and throwing away expensive bulk. It was self-fulfilling, components with three-month lead times were procured to the stated run sizes, so there were not more to run out the bulk. With the analysis in hand, we were able to update run sizes, improve planning, yield more finished goods from the same batch sizes, lower waste, and improve the key monthly plan metric of production plan achievement (the right total amount) and adherence (the right mix of product).

Leveraging Incoming Quality Data to Drive Right-First-Time Metrics

Fast forward a bit. The business is color, shade accuracy is of paramount importance to the consumer. But our right-the-first-time metric (RTFT) for lipstick is poor enough that leadership has taken notice. To process a lipstick batch takes roughly four hours. To rework a batch for color takes one and a half to two hours. Processing has a pre-weigh step where each ingredient is carefully weighed and staged. We have been making these shades for years, processes are well defined, and incoming materials arrive within specification. Why then would our lipstick batches incur high rates of rework? Red pigment is organic. Organic pigments have natural variation, natural variation that is allowable in the specification.

As an initial containment action, quality agreed to grab each batch sheet after it was printed and adjust the pigment amounts based on the concentration of the lot on hand to achieve the desired color.  But that is a lot of manual intervention. Long term solution, we discover an unused functionality in our ERP that allows for incoming quality to enter the pigment concentration for each lot, petition leadership to enable said functionality and successfully implement. This led to world-class RTFT metrics and a global rollout of the solution.

Unlocking Potential: Better Today, Ready for Tomorrow

In manufacturing, the stakes are high. Poor readiness does not just delay system launch. It can disrupt customer commitments, compromise regulatory compliance, and erode financial performance.

The commercial side of a business can often cover gaps and achieve step changes in revenue and margin through winning new customers, channel expansion, upselling, or broad-based price increases. Manufacturing is a cost center. Progress is incremental and hard-earned. There are fewer big levers to recover from a misstep. Executing the systems transformation properly can spell success or disaster.

The truth is, readiness does more than prepare you for transformation. It improves the present. It strengthens daily execution. Cleaner data, clearer ownership, and stronger processes allow businesses to live better inside their existing SCES, sometimes delaying the need for costly transformation altogether.

You do not necessarily want a new system; you want better readiness. When you do invest, you will be ready to maximize it. And along the way, you will start seeing the benefits now. Better today. Ready for tomorrow.

At St. Onge, we will not only guide you through the preparation but roll up our sleeves and dig in with you.
 
—Bob Swidarski, St. Onge Company
 
 

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