Operation Time Prediction
Operation Time Prediction is a tool that uses machine learning to forecast machine run times
for shop order operations. This helps you gain insights into potential delays and improve production efficiency by
highlighting factors that impact the standard pace of production. This information is presented in lobby elements
and can be utilized for operational insights and decision-making. The goal is to assist those involved in planning
and running operations by providing insights that can prevent problems before they happen and improve production by
identifying factors that affect the usual pace of production. A trained algorithm also generates model statistics
and dataset profiles, which can be useful for gaining insights into the dataset and factors contributing to
deviations in shop order operation machine run times.
Basic Data Setup
To get the predicted operation machine run time on the Operation Time Prediction lobby
page, the machine learning model needs to be trained and activated. This needs to be done on the
Solution Manager/Automation and Optimization/Machine Learning/Machine Learning Models
page. After training, the model needs to be set to Active status to enable the use of the
prediction model. The model can be retrained based on business requirements to ensure the most accurate predictions
using recent transactions.
AI Model Identifiers
- AI Model Name: OP_MACHINE_RUN_TIME_PREDICTION
- AI Model Use Case: op-machine-run-time-prediction
- Use Types:
- Trainable
- Batch prediction
Lobby – Operation Time Prediction
The Operation Time Prediction lobby provides an overview of the predictions and includes the following
elements:
- Operation Time Prediction – List Element: Displays shop order operation information based on
predictions. It compares the predicted operation machine run time with the planned machine run time in hours. Each
row can be expanded to view the operation details on the Shop Order Operations page.
Lobby filter values can be applied to limit deviations, such as delays in hours or percentages of the planned
time.
- Accumulated Time Offset per Work Center – Bar Chart: Shows the accumulated predicted offset per
Work Center. Note that if you filter on threshold values in the lobby filter, the summation can be inaccurate.
- Maximum Predicted Offset – Counter: Displays the maximum delay in terms of hours.
- Maximum Predicted Offset Percent – Counter: Displays the maximum delay in terms of percentages of
the planned time.
Lobby Filter Options
- Site: The site where predictions have been made (this should be the same as set in the trainable model
parameters).
- Past Days: The number of days prior to the current date for which historical predictions should be
displayed.
- Time Fence: The horizon extending from the current date into the future that should be displayed (note
that a limiting factor here is the prediction horizon used in the scheduled prediction background job).
- Work Center: Enables filtering on specific Work Centers.
- Shop Order: Enables filtering on specific shop order numbers.
- Part No: Allows filtering on specific part numbers.
- Order Status: Allows filtering on order status.
- Predicted Time Offset: Allows filtering delays greater than the given number of hours.
- Offset Percent: Allows filtering delays greater than the given percentage value.
Data Source
- Ml Planned So Operations: This queries stored predictions and links them to corresponding shop order
operations.
Conditional Format
It is recommended to update the conditional formatting to highlight operation delay according to your own
definitions.
Data Used in the Algorithm
The following attributes are considered when training the algorithm and making predictions:
- Part routing operation average machine run time factor during the training interval period
- Inventory part ABC class
- Inventory part accounting group
- Inventory part product code
- Operation Quantity (scrapped and used/planned)
- Operation Work Center resource
- Operation start month
- Operation start day of the week
- Operation start time of day
- Number of prior shop order operations
- Number of shop order operations left
- Work Center OEE
- Number of operation components
- Operation crew size
- Operation labor class
- Operation qualification profile
- Number of operations guidelines
- Number of operations analysis data points
- Number of operations tools
Based on this information, the algorithm predicts how the operation run time factor will deviate. This, together
with a pre-processed average machine run time and the planned operation quantity, is used to compare the planned and
predicted operation machine run times.
Processing Example
- Training Example: Operation 20 of routing alternate 1 and revision 2 of part x123 has 12 records for the
selected training period. The average machine runtime factor of these records is 0.88 h/qty. In this example one
registered operation ID, with a start and stop clocking and qty, has an individual machine run time factor of 0.90
h/qty. The training target input to the model for this specific record would be 0.90 - 0.88 = 0.02. In this
instance, the model will consider that the features for this record contribute to a machine run time factor offset
of 0.02 hours per quantity.
- Prediction Example: Shop order 202020, Operation 20 of alternate 1 and revision 2 of part x123 is
planned to start in 2 days. The plan is to produce 100 parts, and the machine runtime, from the routing, on the
operation is 0.85 hours per quantity. The planned operation time is 85 hours. During prediction, the attributes for
the planned operations are sent to the trained model. The returned value is a predicted offset from the calculated
average. In this case, the model reacted to the fact that the planned production start was late in the evening on a
Friday*. Based on this, the returned predicted target value is an offset of 0.05 hours per quantity. The average
machine runtime factor is 0.88 hours per quantity (stored from the training). This means that the predicted
operation machine run time in this example is 100 (0.88 + 0.05) = 93 hours, or 8 hours more than the original
plan.
*Note that this is a hypothetical assumption and should not be interpreted as the exact expected behavior of the
algorithm.
Data Quality and Prediction Quality
The algorithm is best at handling common patterns with known data. New information or unprecedented/novel
scenarios that are not well reflected in the training set will not be accurately predicted. If the data used as input
is not well maintained or accurate, the predictions will likely reflect this level of quality.
Configuration
To run the predictions the model must be configured by scheduling training and prediction intervals.
Step 1 - Training
The algorithm can be trained and scheduled from the Machine Learning Model or Machine Learning
Models page.
Mandatory Parameters:
- SITE: The site used to make predictions.
- TRAINING_RANGE_DAYS: The number of past days to include in the training dataset.
- TRAINING_START_DAYS_AGO: The number of days from the current day when the training dataset should
begin.
Optional Parameter:
- WORK_CENTER: Filters the training dataset to a subset of selected work centers. Use “;” as a
delimiter between work centers.
Step 2 - Predictions Background Job
Predictions can be scheduled in Database Task or Database Tasks using the “Predict Operation Machine Run
Time” task. When this task is executed and finished, the planned operations within the given parameters will
have a predicted operation time.
Mandatory Parameters:
- Site: The site used to make predictions.
- OP_HORIZON_NUMBER_OF_DAYS: The horizon into the future in the number of days from the time of
predictions.
Optional Parameter:
- WORK_CENTER: Filters the prediction dataset to a subset of selected work centers. Use “;” as
a delimiter between work centers.
Optional step - Delete Predictions Background Job
This can be scheduled in Database Task or Database Tasks using the “Mass Delete ML Shop Order Operation
Predictions” task.
Mandatory Parameter:
- REMOVE_ENTRIES_OLDER_THAN_DAYS: predictions older than the given number of days will be removed.
Scheduling Settings
The algorithm's training schedule can be customized based on specific requirements. A recommended approach is
to train the algorithm biannually using data from the previous one to two years. However, this frequency can be
adjusted according to the unique needs of different industries and business contexts.
The prediction background job should be scheduled to run outside of regular business hours, ensuring that results
are available at the start of the next workday. This scheduling can be tailored in various ways depending on the
industry and business context. For instance, one approach is to schedule the job to run daily with a 14-day forecast.
Alternatively, the job can be set to run weekly with a seven-day forecast horizon.
It is essential to configure the scheduling in a manner that aligns with the operational workflows and preferences
of the end users.
Limitations and Prerequisites
- To train the algorithm, operations must be accurately registered with start and stop clocking of the machine
run time.
- If the planned operation time is very small (within the margin of error for the prediction) it can produce
negative time predictions.
- This model expects that the planned start time of the operation will be the actual start time. If this is not
reflected in the historical data or the operational way of working, it will impact the quality of the
prediction.
- When a shop order operation Quantity or Work Center is changed then a new prediction job needs to be executed
for this change to update in the lobby.
- The model is limited to training and predicting on one site. Training and predicting on multiple sites are not
supported.
- The algorithm does not consider changes in efficiency factors when making predictions.
- The operations to be predicted must be planned to start on a future date and must be of the time factor type
quantity per hour or hour per quantity.
- Only operations with predictions will be visible in the lobby. Operations that are not candidates for
predictions will not be visible.
- The algorithm only learns during training. New data added after training will not be considered until a new
training is triggered and the new data is within the training range parameters.
- Implemented changes that affect production time can take a long time to be reflected in the prediction.
This is because the training dataset will still contain the old production times during training.
- Setup and labor times are not predicted with this algorithm.
- The model uses batch predictions. If the payload is large (greater than 10,000 operation predictions) in one
prediction job, it will fail. Consider splitting the predictions on different Work Centers or lowering the
prediction horizon to avoid this.
- Explainable AI (XAI) in predictions is currently not available.