At a time when top managers can instantly drill down to any level of data about their finances, sales, customer relationships, supply chain and other functions, one crucial set of data still exists in isolation, separated from the rest of the enterprise by a virtual air gap.

Manufacturing productivity data.

Business intelligence tools built on robust mathematics let corporate leaders see how small changes in any area of their operations flow to the P&L statement. Any area except manufacturing, that is.

Manufacturing and finance people often speak different languages. Plant managers are focused on productivity, but commonly used measurements of productivity aren’t easily connected back to the dollars CFOs use to analyze performance.

Sight Machine has developed a new productivity metric — the Manufacturing Performance Index, or MPI — that lets manufacturers directly link productivity data to profitability. We believe this approach provides the flexibility and ease of use that manufacturers are looking for as they evaluate potential investments, including investments in digitization.

The MPI offers a common language that lets manufacturing and financial executives determine the potential impact on profitability of changes to the production process. And it offers it at the sensor, machine, line, plant and multi-plant level.

A VP of operations overseeing  20 or 30 factories can use MPI to identify which five factories to focus on next year to get the biggest improvement in profitability. Drilling down, MPI can show which four improvements within each factory will lead to the biggest profit boost. For example, increases in MPI and profitability may come from optimizing cycle times across the production processes, improving process yields or reducing the amount of scrap.

Making OEE Actionable

Manufacturers have historically used a number of metrics to drive performance improvement, with overall equipment effectiveness (OEE) being the most popular. OEE is a foundational metric used for process analysis and root cause problem solving. It measures the percentage of the theoretical maximum productivity that a manufacturing process is achieving, with an equation multiplying quality by performance (speed) by availability (uptime).

OEE = (Quality) * (Performance) * (Availability)

While OEE can be a good measure of the productivity of a machine or production line, it is limited in its usefulness beyond that. It lacks the depth needed to understand manufacturing performance.

Key constraints on using OEE:

  • Not directly connected to profitability: OEE is widely regarded as a problem-solving enabler. Most manufacturers are unable to link an improvement in OEE to a direct impact on profitability.
  • Hard to measure factory-wide OEE in real time: Due to the difficulties in collecting and tracking data required to calculate OEE, most manufacturers end up measuring OEE once per week or month as an isolated metric on individual lines.
  • Focuses on production line performance, not on factory-wide performance: Engineers supporting the production lines are the closest to OEE calculations and they end up using the metric, again in isolation, to drive root cause problem solving only on their individual lines.

Hence, the question is: How can we define a metric that enables measurement of manufacturing performance across a factory, not just on individual production lines, and that links productivity gains to factory profitability?