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Digital Shift Handover ensures smooth transitions between shifts, reducing downtime, improving communication, and enhancing operational continuity through real-time data sharing and structured workflows. For more information on implementing Digital Shift Handover in your operations, contact us at VDI. Leverage IoT sensors and AI to monitor safety conditions, ensuring a safe working environment and adherence to compliance requirements. Use automated reporting systems to track and share KPIs such as OEE, takt time, and scrap rates, saving time and increasing visibility.

Automated KPI Reporting improves performance monitoring, enhances decision-making, and fosters operational transparency through real-time data integration, AI analytics, and dynamic dashboards. For more information on implementing Automated KPI Reporting in your operations, contact us at VDI. Equip supervisors with AR tools to guide operators through complex tasks or provide on-the-spot training, improving efficiency and reducing errors. COVID-19 Health Retain Staffing Levels Employee Health Minimize Absenteeism Employee Confidence in Leadership Employee Trust Traceability Keep Facilities Open Keep Shipping Fulfilling Orders Minimiize Liability Financial Branding Confidence Trust Safety Incidents Retain Staffing Levels Minimize Liability Employee Focused Employee Confidence in Leadership Employee Trust Regulatory OSHA Risk Avoidance Area Access Limitations Restricted Areas Evacuations Verify People have left the building Directing Responders EMT Fire Police Worker Grounding Safety Pieces Damaged Returns Replacement Parts Warranty Traceability / Root Cause Analysis Quality Certifications Calibration Guidelines ISO Certifications Testing Quality Documentation Blanket Compliance Statement Detail to Device Number Storage Conditions / Moisture Tracking Max/Min Humidity Max/Min Temp Traceability Downtime Material Unvailaibiity Cost of fix Aiblity to be specific in exposures Root Cause Analysis Alerting Monitor temp/humidity trend Avoid problems as trending happens Dynamic based on what is stored there Overall Labor Efficiency # of Workers # of Stations Increase Throughput On Time Performance Training Tracking who is trained for what Who does jobs - are they trained Identify where additional training is required Time to proficiency Employee Theft Tag High Value Parts Reduce Inventory Losses Quality Certifications Calibration Guidelines ISO Certifications Testing Quality Documentation Blanket Compliance Statement Detail to Device Number Route Efficiency # of Forklifts required Maintenance Expense # of drivers required Utilization # of Forklifts required Maintenance Expense # of drivers required Location Sensing Ability to respond quickly Safety Low Ceilings Obstructions Personnel Virtual Fence Restricted zones Analytics Heat map of usage by time of day Strategic location of Pallet Jacks - Periodic reset Predictive Maintenance Maintenance Expense # of Forklifts Required Repurpose LHP solution once contact tracing is no longer required Safety Training Worker Access Route Efficiency # of jacks required Maintenance Expense # of people required Utilization # of jacks required Maintenance Expense # of people required Location Sensing Ability to respond quickly Safety Low Ceilings Obstructions Personnel Virtual Fence Restricted zones Analytics Heat map of usage by time of day Strategic location of Pallet Jacks - Periodic reset Locating nearest Jack Time for personnel Efficiency improvement Safety Route Efficiency # of Forklifts required Maintenance Expense # of drivers required Utilization # of Forklifts required Maintenance Expense # of drivers required Location Sensing Ability to respond quickly Safety Low Ceilings Obstructions Personnel Virtual Fence Restricted zones Analytics Heat map of usage by time of day Strategic location of Pallet Jacks - Periodic reset Predictive Maintenance Maintenance Expense # of Forklifts Required Repurpose LHP solution once contact tracing is no longer required Safety Training Worker Access SOTI If that has an open API, being able to combine that data with the additional data is a benefit Thingworx as a hub Scanners and other items Similar to Pallet Jack by Machine by Product By Machine By Part Total By Component In the simple case, this could be based on current downtime This could also be based on "excess" downtime within a given time frame It could also be driven by predictive analytics of issues that may yet occur Obviously, these issues could also be driven by Quality Downtime Defects Rework Schedule Adherence Inspection failures Absenteeism Reported Safety Issues etc. Sensor Failure Sensor Outlier PLC Cascade Missing Tags Repetitive Tags Tag Profiling by Shift/Operator different parts different shifts different operators different timeframes Training of new operators Commissioning parts or equipment Root Cause Identification Utilize n-field attributes to track setup types Show a visualization of the setup matrix (matrix of sparklines?) Recommend portions of matrix to analyze? Auto-capture time for each step of setup Automate SMED Analysis Amount of variance PM Effectiveness Before / After Comparison Measurable Impact Measure effectiveness of PM activities Where is PM being done vs where failures are occurring Where should I be doing more PM? Where should I be doing less? PM Timing Automatic ticket creation Leaving grinding wheels on too long produces bad quality parts Taking grinding wheels off too soon spends excess money on tooling Replacing/refurbishing wheels is a very large cost each year Current state is to replace wheels after producing set number of units Number of units varies based on type of product produced Automatically collect detailed tag / process variable data from PLC’s First pass: Use individual metrics to determine boundaries for when tool change is required Use visual controls and alerts to notify personnel Long term: Feed multiple tags through machine learning algorithms to determine tool changes Defect rate vs Tool life Track Tool ID Cost of tool Optimize Tooling Supplier Impact Display Boards Andon Boards TPM Towers SQDC Boards Production pitch tracking chart monthly pitch log job by job tracking chart priority board hourly status chart completion heijunka late load log daily accountability board A-3 project plan form Photo of an A-3 project plan board Attendance matrix labor and rotation plan photo of a labor planning board sample skills matrix entries suggestion system idea board Photo of an idea board Value Stream Mapping Process Mapping Swim Lane Flowchart Process Observation Transportation/Spaghetti Diagram SIPOC Time Value Maps Value Add / Non-Value Add Analysis Value-Add Chart Process FMEA (Downtime) Product FMEA (Quality) SPC Inspections / Audits Process Capability (Cp/Cpk) Preventive Maintenance Schedule / Instructions Predictive Maintenance Andon Display Production Counts & Status Shift Boards By machine By Part By Machine By Part Histogram Correlation with Downtime, Quality Does longer run lengths help "dial in" ideal? Graph of Reject Rate & Downtime rate vs runtime For each run, start clock at zero Map rates vs time as run proceeds average or sum this across all runs or separate by machine, product, etc. Histogram Correlation with Downtime, Quality Does longer run lengths help "dial in" ideal? Downtime Events Downtime Hours Part Machine Type Mfg process data Testing data Rework / NCM data Lab data etc. Ideally in tree structure / 5 Why / RCA format Tied into Process FMEA Ideally in tree structure / 5 Why / RCA format Tied into Product FMEA Scheduled Run Job / Batch / whatever Accumulated costs Schedule vs Production On Time Performance Schedule Adherence Map Activities to trend to see if actions have desired impact Training vs Big Brother Best Practices Shift Day Week Month Quarter o Vendors o Customers / Distributors Startup / Shutdown Guidance Deviations Settings Monitoring / Alerting Robot / Human Interaction Failure Mode Detection Predictive Visual Guides / AR Machine Settings Error States Welding Output Measurements Error Proofing AR Visual Picking Visual Assembly Weld Quality Predictive Quality Integrated Testing Grounding Assurance (Digi-Key) Vision System Instant Quality Feedback Process Measurements Deviations During Process Temperature Vibration Power Draw Etc. Correlate to Output Deviations Predictive Quality Notify when Inspection is Required Input Measurements Tolerance Stacking Visibility Across Processes Video Analytics Standard Work Adherence Computerized Delivery AR Overlay Pick and Place Welding Feedback Augmented Reality Screen-based Reporting Creation of Work Tickets Unexpected Stops Minor Stops Speed Losses When to Change Tooling When to Inspect Parts When to Perform Maintenance PM Activity Tool Change Subtopic Run Until "Almost" Failure Run Until Performance Change Optimize Constant Duration Automated Tracking Automated Messaging / Alerts Potential to "Lock Out" Until Completed Show operator a list of work instructions for today's autonomous maintenance tasks Provide work instructions for maintenance workers Embedded Intelligence Connected Solutions Product Enhancements Smart R&D (PD IoT) Digital Twin Digital Thread Advanced Simulation Capability Value Based Pricing Account Management / Collaboration Channel Management Warranty Claim Management Aftermarket Part Pricing Distribution Channel Management Consensus Demand and Supply Planning Inventory Targets and Placements Risk and Exceptions Management Smart Utility Management Building Management Employee Mobility Health and Safety Productivity Does current behavior correlate to larger problems coming? for example, excess vibration can cause quality issues or minor stops, but can also be a symptom of a larger problem that could "blow up" soon In general, this means mapping process variables to likely outcomes This generally requires a great deal of historical data to: Determine which process variables cause or correlate well with the outcomes to be predicted How the various inputs to the model relate to one another Refine the predictive model parameters to minimize the Type 1 and Type 2 errors Example Use Cases Predictive Maintenance Predictive Tooling Predictive Quality Vision Systems for Inspection Furnace Efficiency Monitoring Lubricants & Filters Cooling System Monitoring Paint Shop Monitoring Warranty / Shop Floor Analysis Auto-MMS Ticket Generation One goal is to automatically identify, classify and prioritize problems; then present the findings to the users OEE systems today make the user explore the data to find where the issues are and perform the analysis Is the performance the same between different shifts & operators? Is the problem material dependent? Does this material have the same problem on multiple machines? Is the problem dependent on day of week or time of day? How long has the problem existed? Is the magnitude of the problem changing? Minimize the steps someone has to take before making improvements Launch a project in a single click Use the "suggested actions" feature to recommend steps such as SMED, error proofing, or particular fixes if similar problems have been seen before Allow project details to be captured later Issues with sensors Outlier data Missing data Operator input Overly repetitive Random Very different than other operators on the same machine