The meat processing industry is characterised by high demand. Quality control therefore becomes even more critical in such a scenario. This article highlights ColumbusFood’s ERP quality control feature on process data collection. The key topics covered will be - data control elements, open quality control activities, and open data collection sheets.
Data collection elements represent various properties that need to be reported in quality control in our data collection. They are assigned a type, which will instruct the system as to the input that will be recorded for that element. Types like bowling yes or no, date lookups, numeric or texts for example. A lookup allows for a processors’ own defined list of available options. During input, the user will select from this list. The actual target value is completed at the master record level setup will see this first end quality control and then again in data collection.
Columbus’s quality control and data collection are seamlessly integrated into Microsoft Dynamics NAV. Columbus created the quality control role centers specific for these two modules. Data control elements are a sign to the master record for the items. These will be used to define the item lot quality control tests based on the data control element type for each test, the target value is also populated. The system uses this target value to compare against the actual test results entered. This is the basis to determine the pass-fail. Must pass denotes that the quality control results entered must meet the targets in order for that lot to pass the quality control testing successfully.
Open quality control activities have a status of pending waiting for the actual quality control test results to be entered and the eventual status of the quality control activity change to pass or fail. Here, quality control technicians can be assigned as well as a scheduled date indicate when the results are due to be entered. The general fast tab maintains key information about the item and lot in quality control activity. Recording the actual test results occurs on the quality control result fast tab. As results are entered, the test date and time and ‘by’ is recorded. Values entered outside the previously recorded target parameters will result in that item lot’s quality control status being set to fail and possibly the entire quality control activity failing. Once all the individual results have been entered, the user can indicate completion based on the result entered, the system determines if the quality control activity status should be pass or fail. The user can overwrite this determination. The caveat is that none of the failed quality control tests were set to must pass. Based on system set-up, the lot status code may default limiting the available transactions possible for that items’ lot.
Columbus’s process data collection allows the processor to gather information on a number of different processes that do not directly deal with the receipt or creation of items. For instance, recording the temperature of the fridge or logging the temperature of an oven based on the frequency during a specific production run. Other examples, capturing key vendor metrics upon receipt or assuring customer quality based on shipments master records and events are linked together through the assignment of data collection elements. For the data collection sheet, the status can be changed to complete which finishes the business process. Alerts can be sent to role centers as we can see here. This notifies those key decision-makers of an alert requiring attention. Once the alert is satisfied, it can be closed. History is maintained on all alerts. A data collection element can be set with a frequency to reoccur hourly for example. If missed, a second alert can be generated notifying a group that the hourly result was not recorded.