2020 Overall Pricing Effectiveness: The New OEE | Automation.com

2020 Overall Pricing Effectiveness: The New OEE

2020 Overall Pricing Effectiveness: The New OEE

by Gabriel Smith

Overall Equipment Effectiveness (OEE) looks at availability, performance, and quality for each machine; these data can then be assessed against the entire complex of production equipment. In this equation, availability is operating time and planned production time. Performance translates to total pieces/operating time/ideal run rate; quality is defined by number of good pieces/total pieces. The goal is to measure and improve manufacturing processes to manage, predict, and reduce variable costs and thus improve operating margin.

Evaluating equipment availability, performance, and quality output is consistent with lean manufacturing. OEE is a quality systems management tool which enables manufacturers to assess a plant’s effectiveness. Used wisely, OEE allows a manufacturer to identify major losses, create a road map to find problems, and enhance capacity.

 

Eliminating Pricing Waste: Overall Pricing Effectiveness (OPE)

There is an even more effective means to improve profitability than variable cost reduction. In fact, according to many studies, improving variable cost typically results in a <8% improvement in profitability whereas improving price results in an >11% improvement in operating profit.

 

Percentage Increase in Operating Profit as a result of 1% improvement.  Source: Harvard Business Review Managing Price, Gaining Profit.

 

2020: the New Iteration of OEE is OPE

OPE can be measured by optimizing and controlling pricing, as well as managing margin leakage with a price waterfall which allows manufacturers to track and adjust price points.

 

Price Waterfall: Visualize Price Effectiveness and Margin Leakage

 ©2019 Pricefx

Ignoring optimized pricing, measuring the realization of price changes, and automation of purchasing trends (including rate of reorders) are leaving money on the table. When a machine capable of producing 100 units per hour is only producing 70 units, it is not fully optimized. The machine equivalence in OPE occurs when the manufacturing enterprise is fully optimized for profitability. Companies use plant capacity as an input for pricing algorithms to maximize utilization and account for fixed and variable manufacturing costs. This is how OPE can accurately optimize margin.

Pricefx is one example that works to optimize and automate prices and accounts for leakage. The very process of customer acquisition, orders, and commitment compliance are used to identify pricing sensitivity by customer and product. By using automated data-driven analysis embedded in the business OPE paradigm, manufacturers are enabled to make better decisions and achieve optimal outcomes.

Insight is at the heart of a powerful pricing system. It is the essential ingredient in developing robust frameworks and weaponizing the data manufacturing organizations gather. Insight drives automation.

Managing pricing strategy is one of the most critical elements of maximizing earnings and profits. From price policy definition and setup of price guidelines to the management of complex off- and on-invoice conditions, this practice is both highly complex and impactful. Managing pricing must be part of the manufacturing automation plan.

Realization. All the analysis and strategy in the world is meaningless without the capacity to operationalize it. Pricefx works closely with manufacturers as the organization integrates technology and tools that generate results. Results in a SaaS model achieve a rapid automation ROI (return-on-investment).

These three OPE elements, insight, managing, and realization, mirror the prongs of OEE, availability, performance, and quality output.

 

Logical Migration from OEE to OPE Automation

The OPE SaaS automation model makes sense to manufacturers; looking at maximum profitability from each customer is not all that different that achieving optimized output from every machine on the plant floor. 

About the Author

Gabriel Smith is a Six-Sigma Green Belt with20 years-experience in Pricing and Quote to Cash.  He has been in the price optimization software industry for 12 of those years and worked with leading companies across many industries such as 3M, Seagate, IBM, Emerson, and Siemens.  Gabriel started his career at Cisco Systems, where he was part of manufacturing team that structure the Sales BOMs and wrote the CPQ logic for configuring and pricing Cisco’s products. Smith holds an interdisciplinary degree from UC Berkeley focused on the use of technology to gain competitive advantage in the market. 

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