The Analytics Module provides Manufacturing Management with a tool to drill down into data at a single production site using the FACTIVITY MES and find ways to improve their manufacturing floor’s production process.  Using Microsoft’s Reporting Services functionality, these KPI (Key Performance Indicators) analytic reports go beyond the standard set of OEE measurements of Availability, Performance and Quality.

This Module expands upon the simple analysis of machine up and machine downtime. Machine availability is most often just not enough to identify the root cause of factory floor problems.  There is much more value in tracking other elements besides up and time down in terms of factory floor analysis. Factivity uses Microsoft’s SQL Reporting Services as a standard, yet simple to use tool to access and evaluate the data.

The module has groups of reports that include OEE analytics, labor analytics, job analytics, as well as, a facility to evaluate alarms capture from the shop floor data collection MES.

Using the power of the drill down in this pervasive and easy to use reporting tool, FACTIVITY helps manufacturers focus in on the root cause of problems captured in real-time.  For example, to complement the evaluation of OEE are the detail analysis of each of the components of OEE…Availability (Down Time), Quality (Scrap) and Performance.  Each of these “views” are user configurable by several sort criteria built into the report.  Sorting scrap, for example, by type of scrap, identification of the resource with the most/least scrap, the employee involved and all queries can be selected by user defined date ranges.

These analytics can be a great asset to management involved with lean manufacturing, as well as, an invaluable tool for Value Steam Mapping of a factory floor.

FACTIVITY Machine Monitoring Module goes beyond real-time displays on a variety of dashboards. Back-end analytics provide answers to the questions of how to improve on-time delivery and reduce waste. It provides the path to better manufacturing execution on your shop floor to see how well your floor assets are working.

• How often does the equipment go down when it should be running…Availability?
• Are your machines running at their rated speeds…Performance?
• Does the machine produce parts that must be scrapped…Quality?
• What combination of operator, machine and part are the most effective…or the least productive?

Questions to ask yourself:

Is there an indication currently that jobs that are released are waiting too long before getting into production?

Are jobs waiting longer than appropriate at work centers? How often do we run into bottleneck situations, where and when?

Do you measure bottlenecks by how long jobs wait at an operation before running or

Do you measure bottlenecks by the amount of “scheduled run time” for each job waiting at the operation (The total amount of scheduled work presently sitting in queue at the operation)?

What type of work stoppages have affected the On-Time Delivery, from which area and what caused them?

Analytics provided by FACTIVITY and Reporting Services are not the only way to get at valuable information from the detailed time-segmented FACTIVITY data files. In addition, this same information can be output directly to several preformatted Excel Spreadsheets with the intent on allowing management to analyze the data as needed in this ubiquitous analytical tool.

Multi-Plant Analytics Module

Beyond single site metrics FACTIVITY has a multiple plant Data Warehouse that can provide a consolidated view of all the FACTIVITY factories into a Microsoft SQL Server Data Base file.

Comparing the analytics among multi-operational facilities can be a valuable way to find the best approach to identifying areas of process improvements for the corporation.  Using this Multi-Plant Module, operations management can be provided the view into performance analytics from one facility to another for identification of best practices and/or common areas of needed improvements.  By consolidating the Analytics from each plant and housing them into one data warehouse, trend and observations between the facilities can become visible.