#Benchmark analytics how to
#Benchmark analytics pdf

Communicate the results of the study to key stakeholders.

Agree on expected targets for improvement.

Compare the existing process with that of appropriate reference models to identify differences and innovations.

Collect information to identify the scope for improvement.

Identify appropriate reference models as a starting point for your assessment.

Document the existing process for conducting the activity.

Confirm the key activity performance measures or indicators.

Identify the resources required for the study.

Determine the broad business process and tasks to benchmark.

The benchmarking process usually encompasses four steps: Quantitative analysis can provide metric-based outcomes, while qualitative comparisons often reveal best practices. Focuses on general approach to the development and management of core competencies, innovations, and change strategies.īenchmarking analyses often rely on both quantitative and qualitative measures to generate meaningful results.

Strategic - seeks to evaluate the organization’s strategic maturity against others across various sectors.

Focuses on costs, technical quality, ancillary service features, and performance characteristics (also called competitive benchmarking).

Performance - compares product and service as a way to assess the organization’s competitive position against same-sector peers.

Process maps are used to facilitate benchmarking.

Process - evaluates specific business processes (e.g., purchase planning, e-procurement, service delivery).

Many types of benchmarking exist the most commonly recognized are: Which shed light on future research in sand dust image reconstruction.Benchmarking analysis is a specific type of market research that allows organizations to compare their existing performance against others and adopt improvements that fit their overall approach to continuous improvement and culture. On SIRB as baseline to illustrate the generalization of SIRB for training CNNs.įinally, we conducted the qualitative and quantitative evaluation toĭemonstrate the performance and limitations of the state-of-the-arts (SOTA), InĪddition, we adopted the existing image transformation neural network trained Then constructed a Sand-dust Image Reconstruction Benchmark (SIRB) for trainingĬonvolutional Neural Networks (CNNs) and evaluating algorithms performance. In this paper, we presented aĬomprehensive perceptual study and analysis of real-world sand dust images, Objective evaluation of algorithm performance. To advance the development of deep learning-basedĪlgorithms for sand dust image reconstruction, while enabling supervised

Moreover, due to the absence of large-scale benchmark datasets, thereĪre no well-known reports of data-driven based method for sand dust imageĮnhancement up till now.

#Benchmark analytics how to

It is unclear how to quantitatively analysis the performance of theĪlgorithms in a supervised way and how we could gauge the progress in theįield. Performance with no-reference way using few selected real-world images from

To our best acknowledge, however, most methods evaluated their

#Benchmark analytics pdf

Download a PDF of the paper titled A comprehensive benchmark analysis for sand dust image reconstruction, by Yazhong Si and 3 other authors Download PDF Abstract: Numerous sand dust image enhancement algorithms have been proposed in recent