Overview over the available report types

Efficiency Report

EfficiencyReport.rptdesign

Bar Graph

Past

This report shows the efficiency of the production as bar graph with of the option of grouping in two stages with regard to:

• Machine (Machine)
• Shift (Shift)
• User Efficiency Report by Machine (Operator Efficiency by Machine)
• User Efficiency Report by Shift (Operator Efficiency by Shift)
• Patterns (Pattern)
• Sequence (Sequence)
• Ticket (TicketUid)
• Production ID (ProductionId)
• Production Sub-ID1 (ProductionSub1Id)
• Production Sub-ID2 (ProductionSub2Id)
• Customer ID (CustomerId)
• Item Number (ArticleId)
• Day (Day)
• Week (Week)
• Month (Month)

The output compares the respective productive times and standstill times
Organizational measures can be taken to optimally distribute the current orders.
Problems with specific machines or during specific shifts can be detected at an early stage.

Two of the evaluations are more specific. They specify the efficiency of the users in relation to a potentially achievable performance, and must only be regarded as an indication. Low productivity, for example, can mean that the machine users are overworked, require additional training, or that the patterns were not yet technically mature:

• User Efficiency Report by Machine
• User Efficiency Report by Shift

You can refer to the OPERATOR_PRODUCTIVE_DURATION and OPERATOR_NON_PRODUCTIVE_DURATION lines in Efficiency Evaluation to establish which columns and stand stills the productive and unproductive times were calculated from.

The result can be filtered in accordance with the following secondary parameters:

• Shift
• Sequence
• Patterns
• User-defined states
• Sintral States
• Item ID
• Customer ID
• Production ID
• Production Sub-ID1
• Production Sub-ID2
• User name
• You can also sort in two stages

Event List

EventList.rptdesign

Table

Past

This evaluation lists all machine events together with additional information, such as the currently loaded pattern.

The result can be filtered in accordance with the following secondary parameters:

• Shift
• Sequence
• Patterns
• User-defined states
• Sintral States
• Item ID
• Customer ID
• Production ID
• Production Sub-ID1
• Production Sub-ID2
• User name
• Event Type / Event ID
• Event-Param Filter
  This filter filters by character strings in the EVENT_PARAM data column in accordance with the following notation:
  [not] '%StringToSearch%' [and|or|and not] '%StringToSearch%' [and|or|and not] ...
  The string to be searched for can be assumed to be included, excluded or linked with and\or. The search string must be enclosed in '%...%'.
  You should first generate an event report to formulate a suitable filter based on the outputs of the EVENT_PARAM column.
  Example:
  1) You are searching for the frequency of a certain stop motion with the number 8725. Filter for all lines containing the value: '%8725%'
  2) You use Defining enhanced user-defined events and wish to determine the reasons for stops during production with the ID 132154684 and the comments (/C=...) by cause /P1=99, which you have defined. The EVENT_PARAM column contains, for example:
  /U=EKCUser /M=1234 /PI=132154684 /P1=99 /C=Yarn ripp
  The filter entry would be: '%/PI=132154684%' and '%/P1=99%'
• You can hide data columns in the report.

Expected Quantities

ExpectedPieceCountReport.rptdesign

Bar Graph

Future

This report lists the forecasted produced piece numbers per machine, day, week or other parameter. This report is suitable for determining the number of items that will be produced in the near future and align the personnel expenses and logistics with it. The customers could be informed about the expected production progress.

The following grouping parameters are available:

• Machine (Machine)
• Patterns (Pattern)
• Production ID (Production_Id)
• Customer ID (Customer_Id)
• Item Number (Article_Nr)
• Day (Day)
• Week (Week)
• Month (Month)
• Year (Year)

All the tickets on the involved machines are taken into consideration and the data used as the basis of the forecast, i.e. the quantities that are expected to be produced within the given time period are determined.

The result can be filtered in accordance with the following secondary parameters:

• Patterns
• Item Number
• Customer Number
• Production Number

Pattern statistics

PatternReport.rptdesign

Table

Past

This report provides information about the quantity of good / bad items and the average, minimum and maximum production time. Who produced during which shift and which events happened during the shift. The evaluation can be grouped by:

• Machine
• Shift
• User name
• Patterns
• Sequence
• Ticket UID
• Production ID
• Production Sub-ID1
• Production Sub-ID2
• Customer ID
• Item ID

The result can be filtered in accordance with the following secondary parameters:

• Shift
• Sequence
• Patterns
• User-defined states
• Sintral States
• Item ID
• Customer ID
• Production ID
• Production Sub-ID1
• Production Sub-ID2
• User name
• Event Type / Event ID
• Data columns that are of no interest can be hidden.
• Sorting is possible in two stages
• Filter by net times:
  Only include times during which the machine has set the productive flag. These are the times from 'pattern loaded' until 'stop piece counter is 0'.
• Filter by times during which production took place with tickets but without service tickets.
• Filter out the fabrics which were active for less than x seconds.

Machine production statistics

ProductionReport.rptdesign

Table

Past

The report lists each individual produced or canceled piece (Complete=0) and can be grouped by tickets in chronological order with additional information such as starting time, knitting time, shift, etc.

The result can be filtered by different parameters (secondary parameters).

• Shift
• Sequence
• Patterns
• User-defined states
• Sintral States
• Item ID
• Customer ID
• Production ID
• Production Sub-ID1
• Production Sub-ID2
• User name
• Data columns that are of no interest can be hidden.
• Sorting is possible in two stages
• Group by Ticket UID.
  The ticket UID is assigned by the PPS and clearly identifies each ticket. In this way, you can evaluate the production for each individual ticket.
• Filter by net times:
  Only include times during which the machine has set the productive flag. These are the times from 'pattern loaded' until 'stop piece counter is 0'.
• Filter by times during which production took place with tickets but without service tickets.
• Filter out the fabrics which were active for less than x seconds.

Stop History

StopHistory.rptdesign

Bar Graph

Past

This report lists the standstill times clearly sorted by day, week or month. The report is based on the data of the stop motion statistics (see below) and is a reduced overview of the stop motion statistics.

The result can be filtered in accordance with the following secondary parameters:

• Patterns
• Item Number
• Customer Number
• Production Number

Machine stop motion statistics

StopStatistic.rptdesign

Table

Past

The stop motion statistics make an efficiency analysis possible, and can be grouped by patterns, sequences, production numbers, tickets etc. for example

The following parameters are available for grouping:

• Machine (Machine)
• Shift (Shift)
• User (UserName)
• Patterns (Pattern)
• Sequence (Sequence)
• Ticket (TicketUid)
• Production ID (ProductionId)
• Production Sub-ID1 (ProductionSub1Id)
• Production Sub-ID2 (ProductionSub2Id)
• Customer ID (CustomerId)
• Item Number (ArticleId)
• Day (Day)
• Week (Week)
• Month (Month)

The result can be filtered in accordance with the following secondary parameters:

• Patterns
• Item Number
• Customer Number
• Production Number

It is also possible to hide data columns.

Yarn Demand

YarnDemandReport.rptdesign

Table

Future

This report lists the forecasted demand for yarn. Based on this report, the logistics of the yarn from a central storage location to the production facility can be optimized. The result can be grouped in two steps, by time and by subject.
For example: "Display all yarns grouped by days and machines."

Time Parameters:

• Day (Day)
• Month (Month)
• Year (Year)

Subject Parameters:

• Machine (Machine)
• Patterns (Pattern)
• Simple Pattern (Simple_Pattern)
• Production ID (Production_Id)
• Customer ID (Customer_Id)
• Item Number (Article_Nr)

All the tickets on the involved machines are taken into consideration and the data used as the basis of the forecast, i.e. the tickets that are expected to be produced within the given time period are determined.

A result can only be generated if the pattern contains yarn and yarn consumption information for each piece. In order to do this, you must do the following at the patterning system:

• In the yarn fields, enter the used yarns with the yarn IDs as they appear in the yarn database
• Fill-in the material definition in the Sintral Check.
• Save the data after executing the Sintral Check and then export the pattern.

Only then can the PPS determine the yarn demand for all sequence elements and planned pieces.

The result can be filtered in accordance with the following secondary parameters:

• Patterns
• Item Number
• Customer Number
• Production Number