In a business context, operational efficiency is a measurement of resource allocation and can be defined as the ratio between an output gained from the business and an input to run a business operation. When improving operational efficiency, the output to input ratio improves.

Inputs would typically be money (cost), people (measured either as headcount or as the number of full-time equivalents) or time/effort. Outputs would typically be money (revenue, margin, cash), new customers, customer loyalty, market differentiation, production, innovation, quality, speed & agility, complexity or opportunities.

The terms "operational efficiency", "efficiency" and "productivity" are often used interchangeably. An explanation of the difference between efficiency and (total factor) productivity is found in "An Introduction to Efficiency and Productivity Analysis".^[1] To complicate the meaning, operational excellence, which is about continuous improvement, not limited to efficiency, is occasionally used when meaning operational efficiency. Occasionally, operating excellence is also used with the same meaning as operational efficiency.

Improving operational efficiency begins with measuring it. Since operational efficiency is about the output to input ratio, it must be measured on both the input and output side. Quite often, company management is measuring primarily on the input side, e.g., the unit production cost or the man hours required to produce one unit. Even though important, input indicators like the unit production cost should not be seen as sole indicators of operational efficiency. When measuring operational efficiency, a company should define, measure and track a number of performance indicators on both the input and output side. The exact definition of these performance indicators varies between industries, but typically covers these categories:

• Input: Operational expenditure (OPEX), capital expenditure (CAPEX), people (measured either as headcount including headcount of partners, or as total number of full-time equivalents)
• Output: Revenue, customer numbers/distribution between segments, quality, growth, customer satisfaction

To effectively measure operational efficiency, various metrics can be employed, depending on the industry and specific operational functions. Here are some common metrics:

• Cycle Time: This measures the time taken to complete a process from start to finish. Reducing cycle time can lead to increased production efficiency and customer satisfaction.
• Capacity Utilization: This metric assesses how close you are to reaching your maximum production capacity. High utilization rates can indicate efficient use of resources, though they must be balanced to avoid overworking machinery or personnel.
• Cost Efficiency: Analyzing the cost to produce a unit of product or service is crucial. This involves monitoring direct costs, indirect costs, and overheads to ensure optimal spending.
• Overall Equipment Effectiveness (OEE): This is used mainly in manufacturing to evaluate how effectively a piece of equipment is used. It combines availability, performance efficiency, and quality of output into a single metric.
• Employee Productivity: Measures output per employee. Enhancements in training, technology, and process improvements can drive better results.
• Inventory Turnover: High turnover indicates efficient management of stock, less money tied up in inventory, and reduced risk of obsolescence.

If the intention is to compare numbers with others through benchmarking it is important to define, measure and track performance indicators for load and complexity as well. Even within the same industry, customer behaviour might e.g. be significantly different between two markets (or two countries) leading to one company having to assign more resources and cost to handling of customers. Not measuring such load and complexity factors might lead to incorrect conclusions on operational efficiency.

When interpreting the quantitative results of the benchmarking, it is important to consider the strategic differentiation:

"Cost is generated by performing activities, and cost advantage arises from performing particular activities more efficiently than competitors. Similarly, differentiation arises from both the choice of activities and how they are performed."^[2]

When qualitatively interpreting the quantitative results of the benchmarking, one has to take the company strategy into consideration - as well as the individual strategies of the other members of the peer group. If not done, quantitative results that are a consequence of strategy, not of inefficiency, can't be eliminated.

One company might have a strategy to differentiate with low price. For that company, it is critical to have low unit production costs and high efficiency in distribution. For another company, differentiating with premium quality, the unit production cost is not that critical (but still important to know, of course). Instead, it is critical to have satisfied and loyal customers and a high absolute revenue per customer. Understanding actual quality levels is also key.

When improving operational efficiency, companies have a few alternatives. The most common are:^[3]

• Same for less, i.e. same output for less input
• More for same, i.e. more output for same input
• Much more for more, i.e. much more output for more input

It is a common misconception that costs, in absolute terms, are always cut when improving operational efficiency. It is true for the "same for less" alternative, but not for the two other alternatives. It can be operationally efficient to increase cost - as long as the output is increasing more.

One example of a same for less alternative is when a manufacturing company reduces its total personnel (and thereby personnel cost) while still producing the same volume of goods. This can e.g. be achieved through centralization, automation or optimization of working processes.

An example of a more for same alternative is a manufacturing company reducing its output of faulty products (and thereby reducing after sales cost) without using more money or resources. This can e.g. be achieved through use of quality management systems, addressing quality in existing training programs for personnel or introduction of higher quality requirements when prolonging subcontractor agreements.

An example of a much more for more alternative is when a manufacturing company invests in a new production plant that lets them produce products with more refinement than what they could produce in the old plants. They can sell these products at a premium that more than compensates for the additional cost. Another example of "much more for more" is when a service company invests in expanding its customer service to increase customer satisfaction and customer loyalty.

Leveraging the right tools and technologies is essential for measuring and improving operational efficiency:

Enterprise Resource Planning (ERP) Systems: These integrate various functions like finance, HR, manufacturing, and supply chain into a unified system, providing transparency and real-time data for better decision-making.

Business Intelligence (BI) and Analytics: Tools that provide deep insights into business operations through data analysis, helping identify trends and areas for improvement.

Automation and Robotics: Automating repetitive tasks can significantly reduce cycle times and increase productivity while reducing human error.

Internet of Things (IoT): IoT devices can be used to monitor equipment performance in real-time, predict maintenance needs, and ensure continuous operation without downtime.

Technological advancements have dramatically influenced operational efficiency. Automation, real-time data analytics, machine learning, and AI are transforming how businesses operate. They enable predictive maintenance, smarter decision-making, and more personalized customer experiences. Technology not only speeds up processes but also provides tools to measure and analyze efficiency in ways that were not possible before.

While measuring operational efficiency is beneficial, it comes with challenges:

Data Overload: Organizations must sift through vast amounts of data, requiring robust data management systems.

Change Management: Implementing new processes and technologies can meet with resistance from employees.

Cost of Implementation: Initial investments in technology and training can be high, though they usually pay off in the long run.