Qsee Insights: Complete KPI Setup & Analysis Guide

Table of Contents

• Introduction

• 1. Understanding KPIs

  • What is a KPI?

  • Types of KPIs Supported

  • The Value of KPI Analysis

  • Before You Begin

  • Best Practices for KPI Management

• 2. Creating a New KPI

  • Required Information

  • Step-by-Step KPI Creation Process

  • Best Practices for KPI Creation

• 3. Resource Management for KPIs

  • Understanding Resources

  • Required Resources for KPI Analysis

  • Tagging Resources for KPI Analysis

  • Resource Preparation Workflow

• 4. Preparing for KPI Analysis

  • Prerequisites for Successful Analysis

  • Data Preparation Checklist

  • The Pre-Analysis Review Process

  • Optimizing Analysis Outcomes

• 5. Initiating and Managing Analysis

  • Starting Your First Analysis

  • Understanding Analysis States

  • Managing Multiple Analyses

  • Navigating Analysis Results

  • Taking Action on Analysis Results

  • Troubleshooting Analysis Issues

• Need Additional Help?

Introduction

Welcome to the comprehensive guide for Qsee Insights' KPI setup and analysis capabilities. This guide will walk you through the entire process of creating, managing, and analyzing Key Performance Indicators to improve your manufacturing operations.

Whether you're new to KPI analysis or looking to optimize your existing processes, this guide provides all the information you need to leverage the full power of Qsee Insights.

1. Understanding KPIs

What is a KPI?

Definition and Purpose

Key Performance Indicators (KPIs) are quantifiable measurements used to evaluate the success of an organization, department, project, or specific activity in reaching its targets.

In Qsee Insights, KPIs function as measurable values that demonstrate how effectively your manufacturing processes are achieving key business objectives. They serve as navigational tools that help track progress, identify inefficiencies, and highlight opportunities for improvement.

Types of KPIs Supported

Qsee Insights supports various categories of KPIs to address different aspects of your operations:

• Process Efficiency KPIs: Measure how efficiently resources are used in manufacturing processes

  • Examples: Production Efficiency, Cycle Time

• Quality Control KPIs: Track defect rates, rework percentages, and other quality metrics

  • Examples: Defect Rate, First Pass Yield

• Cost Management KPIs: Monitor production costs, waste, and resource utilization

  • Examples: Cost per Unit, Material Waste

• Performance KPIs: Evaluate overall equipment effectiveness, throughput, and output metrics

  • Examples: OEE (Overall Equipment Effectiveness), Throughput

• Sustainability KPIs: Measure environmental impact, energy usage, and sustainability goals

  • Examples: Energy Consumption, Carbon Footprint

The Value of KPI Analysis

Implementing KPI analysis through Qsee Insights provides numerous benefits:

• Provides data-driven insights instead of relying on intuition or assumptions

• Uncovers hidden patterns and relationships in your manufacturing data

• Identifies root causes of performance issues before they escalate

• Quantifies potential savings and efficiency improvements

• Enables benchmark comparison against industry standards

Before You Begin

Account and Access Requirements

• A verified account with completed profile information

• Sufficient analysis credits available for KPI processing

• Appropriate subscription level for your analysis needs

• Basic understanding of your manufacturing data structure

Preparation Essentials

• Identify which performance metrics are most critical to your operations

• Gather historical data relevant to these metrics (ideally in CSV format)

• Understand the upper and lower acceptable limits for each metric

• Document any existing target values for performance goals

• Consider the cost implications of measurements or defects

Data Requirements Overview

• Valid CSV files with relevant parameters for each KPI

• Sufficient historical data to establish patterns (minimum recommended: 6-9 months)

• Data that includes both normal operations and anomalies for comprehensive analysis

• Clear documentation of measurement units and timeframes

The KPI Workflow

Four-Step KPI Analysis Process

1. Define: Create KPI definitions with appropriate parameters and limits

2. Resource: Upload and tag data resources that relate to your KPIs

3. Analyze: Process your data against KPI definitions to generate insights

4. Action: Review reports and implement recommended improvements

Integration with Other System Features

• KPIs connect directly with the reporting system for detailed analysis views

• The AI assistant can provide deeper insights into specific KPI performance

• File management allows organization of data resources by KPI categorization

• Dashboard displays KPI performance at a glance for quick monitoring

Credits System and Analysis Runs

• Each KPI analysis consumes one credit from your account

• Analysis credits are provided according to your subscription plan

• Premium plans include more credits for comprehensive KPI monitoring

• Additional credits can be purchased if you exceed your monthly allocation

Best Practices for KPI Management

Start Small, Scale Intelligently

• Begin with 3-5 critical KPIs rather than tracking everything at once

• Focus on areas with the greatest impact on quality or cost reduction

• Expand your KPI monitoring as you become familiar with the system

Maintain Consistent Measurement

• Ensure KPIs are measured consistently using the same methodology

• Document any changes to measurement processes or data collection

• Compare performance over time using standardized time periods

Review and Refine

• Periodically review KPI definitions to ensure they remain relevant

• Adjust upper and lower limits based on improved performance

• Update target values as your operations evolve and improve

Coordinate with Teams

• Share KPI definitions with relevant stakeholders for alignment

• Ensure data collection processes support accurate KPI measurement

• Use KPI insights to drive team discussions and improvement initiatives

2. Creating a New KPI

Required Information

KPI Name and Description

• Choose a clear, descriptive name that accurately represents the metric (e.g., "Defect Rate", "Production Efficiency")

• Include a detailed description explaining what this KPI measures and why it matters to your operations

• Consider including industry-standard terminology for better analysis results

Upper and Lower Limits (Mandatory Fields)

• Define the acceptable range for your KPI measurements

• Upper limit: The maximum acceptable value before performance is considered problematic

• Lower limit: The minimum acceptable threshold (must be lower than upper limit)

• Consider historical data and industry benchmarks when setting these values

• Example: For a defect rate, lower limit might be 0% and upper limit 5%

Volume Metric and Measurement Frequency

• Specify the volume of items or events measured annually

• This helps the system understand the scale of your operations

• Include units of measurement when relevant (pieces, transactions, etc.)

• Example: "10,000 units/year" or "500 measurements/year"

Cost Information and Currency

• Enter the cost associated with each measurement or instance

• Select the appropriate currency ($ or € currently supported)

• This information helps calculate potential savings and ROI

• Example: "$10 per measurement" or "€5 per defect"

Target Value (Optional but Recommended)

• Set an ideal performance goal for this KPI

• This value should fall between your upper and lower limits

• Used to calculate performance gaps and improvement opportunities

• Example: For defect rate with limits of 0-5%, target might be "<2%"

Step-by-Step KPI Creation Process

Accessing the KPI Creation Interface

1. Navigate to the KPIs section from the main dashboard

2. Click the "Create KPI" button in the KPI header section

3. A dialog will appear with the KPI creation form

Filling in the Mandatory Fields

1. Complete all required fields marked with an asterisk (*)

2. The system will validate your inputs as you complete the form

3. Pay special attention to the relationship between upper and lower limits

Validation and Error Handling

• The form validates inputs in real-time as you type or when you move to the next field

• Common validation errors include:

  • Upper limit being less than or equal to lower limit

  • Missing required fields

  • Invalid numeric formats for limits, volume, or cost

• Error messages appear below each field with validation issues

Saving Your New KPI

1. Click the "Create KPI" button at the bottom of the form

2. The system will perform a final validation of all fields

3. Upon successful creation, you'll receive a confirmation message

4. Your new KPI will appear in the KPI list on the main KPIs page

Verifying and Editing Your KPI

• After creation, review your KPI details in the list view

• You can edit your KPI at any time by clicking the edit icon

• Consider creating several related KPIs for comprehensive analysis

Best Practices for KPI Creation

Keep KPI Definitions Consistent

• Use the same naming convention across similar KPIs

• Maintain consistent measurement units within KPI categories

Start with Critical Metrics

• Begin by creating KPIs for your most important business metrics

• Focus on areas where you already have data available for analysis

Consider Data Availability

• Create KPIs for which you have historical data to upload

• At least one valid CSV file will be needed before analysis

Plan for Analysis

• Each KPI analysis will require credits from your account

• Prioritize KPIs based on business impact and available resources

Document Your Methodology

• Consider noting how the KPI is calculated in the description

• Include any special considerations for data collection or interpretation

3. Resource Management for KPIs

Understanding Resources

Types of Acceptable Resources

• CSV Files: The primary data source for KPI analysis, containing structured data in rows and columns

• PDF Documents: Supporting documentation that provides context or explanations for KPI data

• DOCX Files: Additional text-based resources for supplementary information

• External Links: References to web resources related to your KPIs or industry standards

CSV File Requirements

• Header row with clear parameter names

• Consistent data format throughout columns

• Proper date formatting (YYYY-MM-DD recommended)

• Numerical data without special formatting or currency symbols

• Maximum size of 100MB per file

Data Structure Best Practices

• Time-series data should include clear date/time columns

• Separate columns for each measured parameter

• Consistent units of measurement throughout the dataset

• Complete datasets without large gaps or missing values if possible

• Clean data free of syntax outliers or recording errors

Required Resources for KPI Analysis

Minimum Data Requirements

• At least one validated CSV file tagged with the KPI name

• Sufficient historical data to establish patterns (6+ months recommended)

• Complete data covering all essential parameters for the KPI

• Properly formatted columns that match the KPI's measurement needs

• Error-free data that has passed the system's validation checks

Validation Process

• All uploaded files undergo automatic validation

• The system checks for:

  • Proper file format and structure

  • Consistent data types within columns

  • Header row presence and clarity

  • Data completeness and quality

  • Potential outliers or anomalies

• Files receive status indicators: "validated," "invalid," or "indication" (potential issues)

Handling Invalid Resources

• Resources with validation errors are marked as "invalid"

• The system identifies specific issues that need correction

• KPI analysis cannot proceed until all required resources are validated

• Options for resolving include re-uploading corrected files or excluding problematic parameters

Tagging Resources for KPI Analysis

The Tagging System

• Tags create direct connections between resources and KPIs

• Each resource can be tagged with multiple KPI names

• The tagging system ensures only relevant data is included in analysis

• Tags appear in the file management interface for easy reference

• During upload, you can select from existing KPI names or create new tags

Creating Effective Tags

• Use consistent naming that exactly matches your KPI names

• Avoid generic tags that don't connect to specific KPIs

• Consider creating hierarchical tag structures for complex organizations

• Tag both primary data sources and supporting documentation

• Review and update tags when KPI definitions change

Parameter Selection and Exclusion

• For CSV files, you can specify which columns (parameters) to include

• The "Exclude Parameters" feature allows you to omit irrelevant columns

• Excluding parameters helps focus analysis on meaningful data

• Commonly excluded parameters include:

  • Internal ID or reference columns

  • Redundant or duplicated information

  • Columns with poor data quality

  • Parameters unrelated to the KPI's measurement

Organizing Resources Effectively

File Management Best Practices

• Create a logical organization system for your resources

• Group related files by KPI category or time period

• Use consistent file naming conventions

• Include date ranges in file names when appropriate

• Regularly review and clean up outdated resources

Resource Description and Documentation

• Add detailed descriptions to each uploaded resource

• Document the source of the data and collection methodology

• Note any special considerations for interpretation

• Record known limitations or issues with the data

• Include contact information for the data owner or expert

Managing Resource Updates

• When uploading updated versions of existing data:

  • Maintain the same parameter names and structure

  • Note changes in the resource description

  • Consider archiving rather than replacing old files

  • Retag the new file to maintain KPI connections

  • Verify validation status before proceeding with analysis

Resource Preparation Workflow

Step 1: Data Collection

• Identify data sources relevant to your KPI

• Extract data in CSV format from your systems

• Ensure data is clean, consistent, and properly formatted

• Prepare additional context documents if needed

Step 2: File Upload

• Navigate to the Files section in the system

• Use the "Add Resource" button to upload files

• Select files or drag and drop into the upload area

• Wait for initial processing to complete

Step 3: Resource Configuration

• Add descriptive information to each resource

• Apply appropriate KPI tags to connect resources to KPIs

• Review and exclude irrelevant parameters if needed

• Check for validation messages or warnings

Step 4: Validation Review

• Address any validation errors that appear

• Review "indication" warnings and decide on appropriate action

• Ensure all critical resources show "validated" status

• Confirm that each KPI has sufficient validated resources

Step 5: Analysis Preparation

• Verify all resources are properly tagged

• Ensure excluded parameters are appropriately set

• Confirm data coverage meets minimum requirements

• Check that you have sufficient analysis credits available

4. Preparing for KPI Analysis

Prerequisites for Successful Analysis

Complete KPI Definition

• Ensure all required fields in your KPI definition are completed

• Verify that upper and lower limits are set appropriately

• Double-check that the KPI description is clear and accurate

• Confirm target values are realistic and within your defined limits

• Review cost and volume information for accuracy

Sufficient Data Resources

• At least one validated CSV file tagged with the relevant KPI

• Adequate historical data (minimum 3 months recommended)

• Data covering the full range of operating conditions

• Files must be properly tagged and validated

• Consider including both normal operations and exception periods

Credit Availability

• Verify you have at least one analysis credit available

• Each KPI analysis will consume one credit from your account

• Credits are replenished monthly based on your subscription plan

• Premium plan users receive priority processing for analyses

• Consider credit allocation for multiple KPIs if running several analyses

System Requirements

• Ensure you're using a supported browser (Chrome, Firefox, Edge, Safari)

• Stable internet connection for uninterrupted analysis processing

• Sufficient screen resolution for optimal report viewing (1280×720 minimum)

• Latest system updates applied for security and performance

Data Preparation Checklist

Quality Assurance

• Remove duplicate records in your datasets

• Fill or properly mark missing values

• Correct any obvious data entry errors

• Standardize date formats across all files (YYYY-MM-DD preferred)

• Ensure numerical data is free from text or special characters

Parameter Alignment

• Match column names with standard terminology where possible

• Verify units of measurement are consistent across datasets

• Document any parameter conversions or calculations applied

• Group related parameters logically within your datasets

• Consider aggregating high-frequency data if appropriate

Time Period Considerations

• Include data from multiple production cycles if possible

• Ensure data spans different operational conditions

• Consider seasonal variations if relevant to your industry

• Include both high and low performance periods for contrast

• Tag date ranges that coincide with special circumstances

Contextual Information

• Document any unusual events during the data collection period

• Note system changes or improvements implemented

• Record environmental factors that may impact performance

• Identify external influences on your metrics (supply chain disruptions, etc.)

• Include information about measurement tools or methods used

The Pre-Analysis Review Process

Data Validation Final Check

• Review all validation status indicators for your files

• Address any "indication" warnings before proceeding

• Verify that corrected files pass validation after re-upload

• Ensure all critical parameters are included in the dataset

• Check that excluded parameters are appropriate

Tag Association Verification

• Confirm all resources are properly tagged with the correct KPI name

• Check for spelling differences between KPI names and tags

• Ensure supporting documentation is also properly tagged

• Verify that older versions of files are appropriately handled

• Review external linked resources for relevance

Parameter Coverage Assessment

• Verify that your data covers all essential KPI parameters

• Check for balanced data across all time periods

• Ensure sufficient sample size for statistical significance

• Confirm that outliers are either addressed or documented

• Validate that key performance factors are represented

Analysis Objective Clarification

• Define specific questions you want the analysis to address

• Establish clear improvement goals based on current performance

• Identify specific processes targeted for optimization

• Determine how analysis results will be used in decision-making

• Set realistic expectations for insights and action items

Optimizing Analysis Outcomes

Strategic Data Selection

• Focus on the most relevant time periods for your current goals

• Include benchmark periods of both good and poor performance

• Consider isolating specific production lines or processes

• Balance between enough data for significance and too much noise

• Tag specific incidents or changes for correlation analysis

Setting Realistic Expectations

• Understand what the analysis can and cannot determine

• Recognize limitations based on data quality and quantity

• Prepare for potential unexpected correlations or findings

• Consider how results will translate into actionable improvements

• Plan for follow-up analyses to explore initial findings

Preparing for Results Interpretation

• Familiarize yourself with the reporting dashboard layout

• Understand different visualization types and their meanings

• Know how to filter and adjust report views for different insights

• Prepare to share results with relevant stakeholders

• Consider who needs access to analysis outcomes

Planning for Implementation

• Set aside time to review analysis results thoroughly

• Identify team members who should be involved in discussions

• Prepare to translate insights into concrete action plans

• Establish metrics for tracking improvements post-analysis

• Schedule follow-up analysis to measure effectiveness of changes

5. Initiating and Managing Analysis

Starting Your First Analysis

Accessing the Analysis Interface

1. Navigate to the KPIs section from the main dashboard

2. Locate the KPI you wish to analyze from your list

3. Click the "Analyze" button beside your selected KPI

4. The system will perform a pre-analysis check of resources

5. Analysis dialog will appear with a summary of available data

The Analysis Confirmation Dialog

• Review the summary of files that will be included in analysis

• Check the number of validated files available for this KPI

• Note any files with indication warnings that might affect results

• Confirm you have sufficient credits (1 credit per analysis)

• Review potential data issues highlighted by the system

Analysis Options and Settings

• Choose between standard or advanced analysis modes

  • Standard: Focuses on pattern recognition and immediate insights

  • Advanced: Includes predictive modeling and detailed correlation analysis

• Select the time period range if you want to limit the analysis scope

• Choose whether to include automated recommendations in your report (enabled by default)

Initiating the Process

1. Click "Proceed with Analysis" to start processing

2. Confirm credit usage when prompted

3. The system will begin data processing and analytics

4. You'll be redirected to the reports section automatically

5. A progress indicator will show the estimated completion time

Understanding Analysis States

In Progress Status

• Analysis typically takes 2-3 hours, depending on data volume

• Progress indicators show the current processing stage

• Each KPI can only have one active analysis at a time

• In-progress analyses cannot be paused

• System notifications will update you on significant progress

Completed Analysis

• Receive notifications when the analysis is finished

• Results are immediately available in the Reports section

• Dashboard indicators update to show analysis completion

• Previous analyses are archived but remain accessible

• Summary email sent to registered account (if enabled in preferences)

Failed Analysis

• Specific error messages identify the cause of failure

• Common reasons include:

  • Insufficient data for statistical significance

  • Critical inconsistencies discovered during deep analysis

  • System processing limits exceeded

  • Invalid parameter correlations

  • Unexpected data format issues

• Failed analyses do not consume credits from your account

• Guidance provided for resolving issues before retrying

Canceled Analysis

• Analyses can be canceled during the in-progress state

• No results are generated from canceled processes

• Credits are returned to your account for canceled analyses

• The system may ask for feedback on cancellation reasons

• You can initiate a new analysis if desired

Managing Multiple Analyses

Analysis Queue Management

• Monitor all current and past analyses from the Reports dashboard

• Filter analyses by status, date range, or KPI name

• Sort by most recent or high-priority indicators

• Bulk actions are available for archiving or exporting multiple reports

• Visual indicators show credit usage across different analyses

Prioritizing Multiple KPIs

• Premium accounts can queue multiple analyses simultaneously

• Standard accounts process one analysis at a time

• Set priority flags for urgent analyses (premium feature)

• Balance credit usage across critical vs. routine KPIs

• Consider using scheduled analyses for regular monitoring

Tracking Analysis History

• Complete history of all analyses maintained in your account

• Compare results across different time periods

• Track improvement trends based on implemented changes

• Document specific actions taken after each analysis

• Set performance benchmarks based on historical results

Credit Management Strategies

• Monitor credit usage through the account dashboard

• Prioritize high-impact KPIs when credits are limited

• Schedule critical analyses at the beginning of billing cycles

• Consider credit add-ons for periods of intensive analysis

• Premium plans offer more credits and rollover options

Navigating Analysis Results

The Reports Dashboard

• Centralized view of all KPI analyses

• Filter and sort capabilities for finding specific reports

• Quick status indicators show completion and insight levels

• Preview cards highlight key findings for each analysis

• One-click access to detailed report views

Detailed Report Navigation

• Hierarchical information presentation for easy comprehension

• Executive summary provides quick overview of key findings

• Interactive charts and graphs for deeper data exploration

• Collapsible sections for specific analysis components

• Export options for sharing or presenting results

Understanding Report Components

• Pattern Analysis: Identifies recurring trends and anomalies

• Correlation Matrix: Shows relationships between parameters

• Performance Indicators: Highlights areas exceeding or below targets

• Improvement Potentials: Quantifies possible gains with optimization

• Recommendation Section: Provides actionable improvement suggestions

Interactive Features

• Drill-down capabilities for investigating specific data points

• Custom filtering to focus on particular time periods or conditions

• Comparative views between different analyses or KPIs

• Annotation tools for marking important findings

• Bookmark function for saving important views

Taking Action on Analysis Results

Implementing Recommendations

• Review system-generated improvement suggestions

• Prioritize recommendations based on potential impact

• Create implementation plans with clear responsibilities

• Set realistic timelines for changes

• Document baseline measurements for comparison

Collaboration Tools

• Share reports with team members via secure links

• Add custom notes and annotations to specific findings

• Assign action items directly from the report interface

• Schedule review meetings based on analysis completion

• Track implementation progress within the system

Measuring Improvement Impact

• Run follow-up analyses after implementing changes

• Compare before and after performance metrics

• Calculate actual savings or efficiency gains

• Document unexpected consequences or benefits

• Adjust future recommendations based on results

Continuous Improvement Cycle

• Establish regular analysis schedules for key KPIs

• Create a feedback loop between analysis and implementation

• Develop a historical record of improvements and outcomes

• Refine KPI definitions based on analysis insights

• Gradually expand analysis to additional performance areas

Troubleshooting Analysis Issues

Common Analysis Challenges

• Insufficient data volume for meaningful results

• Parameter inconsistencies across different data sources

• Outliers causing distortion in trend analysis

• Seasonal factors creating misleading patterns

• Multiple changes implemented simultaneously obscuring cause-effect

Resolution Strategies

• Data augmentation techniques for limited datasets

• Parameter normalization methods for inconsistent measurements

• Statistical methods for handling outliers appropriately

• Seasonal adjustment calculations for time-based patterns

• Controlled implementation approaches for clear cause-effect analysis

Getting Additional Support

• Access the Knowledge Base for analysis and troubleshooting

• Review typical case studies for similar industries

• Contact support for assistance with complex analyses

• Schedule a consultation with data analysis experts

• Join community forums to discuss common challenges

Advanced Analysis Options

• Custom parameter weighting for specialized analysis

• Manual correlation investigation for unexpected results

• Multi-KPI combined analysis for system-wide insights

• External data integration for broader context

• Specialized analysis modules for specific industries

Need Additional Help?

If you need further assistance with Qsee Insights, please don't hesitate to:

• Visit our Help Center: Access comprehensive documentation, tutorials, and FAQs

• Contact Support: Submit a ticket through your account dashboard or email support@qsee.io

• Request Training: Schedule a personalized training session with our experts

• Join Our Community: Connect with other users to share best practices and tips

Our support team is available Monday through Friday, 8:00 AM to 6:00 PM CET to assist you with any questions or challenges you may encounter.