Innovative Production Machines and Systems Conference

Dear Okfalisa,

Thanks for Your interest for our paper.

It has become essential to measure and monitor the effectiveness of supply chain processes in taking intelligent decisions.

We have created the SCOR Metamodel (the segment of the model is presented in the paper) which enables creation of any supply chain configuration and it is the basis for further modelling.

We have used standard metric as defined in the SCOR model, but also a custom metrics can be added.
Metrics (KPIs) can be defined at the different tiers (strategic, tactical, operational…), for the particular process, particular node (location), and can be tied to a particular performance attribute (for example supply chain costs) and even to the particular strategic goals.

The benefits of using the standard metrics (such as SCOR) are twofold:
1. Standardized way for defining the metric elements enables easier and seamless integration and collaborative performance measurement at the global supply chain level. KPI can be defined at each company and than exposed through the Internet (using the web services, XML, and distributed DBMS). Depending on the supply chain organization structure and ICT capacities, other models are possible (for example, company can just expose the data that can be used by a centralized BI system – usually owned by a main supply chain company).
By using the Data Warehouse and other BI technologies, data from distributed and heterogeneous sources can be extracted, cleaned, and loaded, thus enabling further reporting, analysis, and KPI calculation that can be ultimately delivered via Web portals, management dashboards and balanced scorecards.
2. This enables establishing benchmarking for performance comparison and uncovers best business practices for gaining competitive advantage. There are already commercial supply chain benchmarking databases available (for example, SCORMark, etc.)

KPIs can range from very simple measurements to very complex, cross correlated analytic results.
Metrics form different tiers (levels) are related. For example, Level 1 – Perfect order Fulfillment metric is calculated with the formula:
[Total Perfect Orders] / [Total Number of Orders] x 100%
The Perfect Order Fulfillment calculation is based on the performance of each Level 2 component of the
order line to be calculated: % of Orders Delivered in Full, Delivery Performance to Customer Commit Date, Documentation Accuracy, and Perfect Condition.

Each KPI in our Metamodel defines up to four expressions for some performance metric:
• The actual value.
• The goal value.
• The status. A normalized value between -1 and 1 that provides the status of actual vs. goal (-1 is ‘very bad’, 1 is ‘very good’).
• The trend. A normalized value between -1 and 1 that provides the trend over time (-1 is ‘getting a lot worse’, 1 is ‘getting a lot better’).

Concrete realization of the KPIs depends on the particular OLAP server software. We have used MDX language for the defining the KPIs. These KPI metadata can be embedded into Web portals and delivered to different front-ends (web browsers, web services, mobile devices, etc.).

When choosing the right metrics, the main leading elements are strategic goals, processes involved (our model database contain metric related to particular processes), priorities (business policy), available data, etc.

Hope You will find this information helpful.

Best regards,

Nenad