An Introduction to Oracle Manufacturing Operations Center (MOC)
Does your manufacturing intelligence system support proactive monitoring for quick decision support? Lots of data gets collected on your shop-floor – but how should it be organized to assess the performance of a machine, a line, a plant, or a fleet of plants? What will it take you to make the process more a science than an art? Oracle has launched a new product - Manufacturing Operations Center - to answer some of these questions.
A real-time manufacturing intelligence system can equip shop-floor supervisors, plant managers, and VPs with the tools to see the operations at the right level and obtain valuable insights. Some examples of such insights are:
- Can production losses from existing equipment be reduced to increase the effective capacity, and therefore postpone new equipment purchases?
- Which machines are showing abnormal temperature and vibration trends and need to be immediately put under preventive maintenance to avoid costly scrap?
- When should you move production from Equipment-A (or Plant-A) to Equipment-B (or Plant-B)?
- What is the backlog on the shop-floor – and which customer orders should be prioritized for manufacturing?
The primary challenge in any manufacturing intelligence solution is the sheer variety of data standards that need to be handled. Each plant can have equipment based on a different technology with different control parameters expressed in different communication protocols. Integrating information from all equipment to get a holistic view of the shop-floor becomes a complex task. However, as more and more companies try Lean and Six-sigma initiatives - accurate, current, and holistic measurements of equipment and plant parameters become critical to identify and eliminate waste in all its forms.
Oracle’s Manufacturing Operations Center (MOC) is a manufacturing intelligence solution aimed at measuring what needs to be managed. It can be implemented as a stand-alone product, or in conjunction with Oracle E-Business Suite for which it has prepackaged adaptors. It collects real-time, high-resolution data from shop-floor systems (for example: SCADA, PLCs, DCS, quality systems, MES, counters, sensors, and data historians) and contextualizes it for analysis. Its architectural elements are:
- A persistent data model that supports extensible attributes for capturing customer-specific process parameters.
- A contextualization rule engine that adds business context (work order #, shift, product, etc.) to the data collected from the shop-floor.
- Configurable role-based dashboards based on Oracle Business Intelligence Enterprise Edition (OBIEE)
MOC supports advanced graphics and drill-downs for more than 50 KPIs and more than 90 measures. These KPIs and measures are organized under the MOC Catalog that has the following categories:
- Manufacturing Asset Performance: To analyze Overall Equipment Efficiency (OEE) and production loss
- Batch Analyzer: To analyze batch production variances (quantity, material usage, resource usage) and cycle time
- Schedule Adherence: To analyze production performance and production slippage
- Agility Responsiveness: To analyze flexibility ratio at equipment level
- Plant Maintenance: To analyze equipment downtime measures
- Quality: To analyze first pass yield, scrap, and rework
- Service Levels: To analyze manufacturing performance related to schedule, requested, and promise dates for pegged sales orders
- Equipment Downtime Analysis: To analyze equipment downtime by downtime reasons
- Equipment Scrap Analysis: To analyze scrap quantity by scrap reasons
- Equipment Attributes Data – Actual: To compare actual attributes data with specifications
Role-based dashboards can be configured from these measures to foster focused monitoring. The seeded Plant Manager Dashboard has tools to track the following metrics:
- Asset performance (OEE) by plant, department, and equipment: OEE is a product of machine availability, machine performance, and first-pass yield. It summarizes the current state of the plant and helps in benchmarking operations against other companies and divisions.
- Batch performance: Measures work order quantity variance, PPM trend, batch cycle time trend, and service level performance
- Production performance: Measures production schedule performance by department and equipment
The first measure – OEE – helps plant managers dig deeper into the reasons for poor equipment availability, equipment performance, and product quality. Figure 1 shows how these losses (shown as B, C, and D) can reduce the total capacity (A) of equipment to effective capacity (E).
Figure 1: Understanding Overall Equipment Effectiveness (OEE)
Plant managers can view the departments with low OEE, and drill down to individual equipment to find out which loss is dragging the effective capacity down. Similar drill down is possible with other metrics in the Plant Manager Dashboard.
MOC uses ISA-95 standard for defining equipment hierarchies. Shop-floor data is collected as tags using 3rd party OPC servers (from companies like Kepware, ILS, and Matrikon) and other 3rd party solutions. With Kepware’s KepServer, for example, a Channel is a group of shop-floor equipment, and each data collection point (pressure, temperature, vibrations, etc.) for an equipment is defined as a tag. Each tag is mapped to a database table-field in MOC. Data on each tag can be collected at a user-defined frequency.
MOC requires Oracle Warehouse Builder (OWB) 10.2.0.4 and OBIEE 10.1.3.4. It uses OWB as an ETL tool to extract data from source systems. OBIEE is used to generate configurable role-based dashboards, ad-hoc reports, and alerts. The reports can be easily downloaded to Microsoft Excel and PowerPoint. A profile option in MOC points to the machine and port where OBIEE is installed.
The latest release (12.1.1.01) of MOC comes with EBS process manufacturing integration, enhanced EBS discrete manufacturing integration, production performance reporting, and production quality monitoring.
MOC is built for quick deployment and adoption. It is advisable to start MOC deployment with a controlled scope (a cell or a line in a plant) and expand gradually to cover the production landscape.