CHEMICAL PLATING AND HEAT TREATMENT MANUFACTURER
- Jul 17, 2024
- 3 min read
BUSINESS PROBLEM
This chemical plating and heat treatment manufacturer performs specialized processes such as plating and heat treatment for fasteners and other parts sold by their customers to larger manufacturers. Entering a critical growth phase, the company faced several data-driven challenges that were limiting profitability and operational efficiency across its multi-facility operations. Specifically, the company lacked accurate job costing data, meaning pricing decisions were based on historical precedent rather than actual costs. This was resulting in some jobs being performed at a loss and others being overpriced to the point of driving customers elsewhere. Operational data was incomplete or inconsistent across departments, maintenance records were fragmented between multiple systems and spreadsheets, and institutional knowledge was concentrated among a handful of senior employees with no structured way to transfer it to newer staff. With over 30 separate data assets spread across systems like their ERP, accounting software, a CMMS platform, spreadsheets, email, and even paper files, there was no unified view of the business to support strategic decision-making.
SCALESOLOGY IN ACTION
Scalesology conducted a comprehensive Data Needs Assessment that included structured interviews with staff across 12 functional areas, including Sales, Order Entry, Operations, Maintenance, Quality Assurance, Human Resources, Finance, and Leadership. These interviews were grounded in real-world processes to uncover the full scope of data collected daily and identify where data quality breakdowns were occurring.
Through this assessment, Scalesology identified 22 key data issues and developed a scoring methodology to rank each one based on potential impact to revenue, customer churn, and operating expenses. This allowed the team to prioritize recommendations by return on investment, level of effort, and business impact.
Scalesology's recommendations fell into two categories:
Data Analysis Using Existing Data (approximately 462 hours of effort), including:
KPI dashboards using Microsoft OneLake and Power BI to provide real-time visibility across all departments
A job costing model using Random Forest machine learning to accurately determine the cost of each process by analyzing electricity, gas, water, labor, chemicals, maintenance, and downtime data
A dynamic pricing model to identify optimal price points that maximize total profitability by balancing margin against order volume
Order projection models using time-series and regression techniques to forecast future demand
Stochastic what-if modeling combining costing, pricing, and order projections through Monte Carlo simulations to evaluate scenarios such as shift changes, equipment modifications, and discount strategies
Downtime prediction and maintenance optimization using historical maintenance data to proactively schedule preventive maintenance and minimize unplanned outages
Data Process Improvements (17 initiatives ranked by a "bang-for-the-buck" score), including quick wins such as establishing communication protocols to stop using email and messaging apps as data stores, completing the migration of maintenance data into the CMMS platform, and populating a centralized data warehouse. Longer-term recommendations included integrating standalone spreadsheets and disconnected data collection tools into the ERP, implementing a centralized CRM, deploying a Learning Management System to capture institutional knowledge, and consolidating quality documents into a purpose-built Quality Management System.
Scalesology also performed an initial proof-of-concept for the job costing model, testing multiple machine learning approaches against the company's electricity consumption and job processing data. The Random Forest model demonstrated the strongest predictive accuracy, achieving a Mean Absolute Error as low as 574 kW for one facility, validating the approach for full-scale deployment across all cost categories.
RESULT
Scalesology delivered a prioritized, ROI-focused roadmap that balances near-term wins with long-term data maturity. The data analysis initiatives are projected to begin delivering value within five months, starting with executive KPI dashboards and accurate job costing that will directly improve pricing accuracy and gross margins. Quick-win process improvements such as standardized communication protocols and completing the CMMS migration can be implemented internally within one to two months, immediately improving data reliability and interdepartmental coordination. The full implementation plan spans two years and positions the manufacturer for scalable, data-informed growth with improved margins, reduced unplanned downtime, greater operational efficiency, and an enhanced customer experience.
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