RFQ Attachment Five - Applying the CPFR Model to the Marine Corps Supply Chain (Excerpt).pdf

The Pennsylvania State University

CMC Fellowship Program

The Mary Jean and Fran P. Smeal

College of Business Administration

APPLYING THE COLLABORATIVE PLANNING,

FORECASTING, AND REPLENISHMENT (CPFR ®) MODEL

TO THE MARINE CORPS SUPPLY CHAIN

By

Submitted in Partial Fulfillment

Of the Requirements for the United States Marine Corps

Logistics Fellowship Program developed ad-hoc reports in GCSS-MC Discovery Writer module in an attempt to mitigate this shortfall.

The degree of accuracy of these reports is often difficult to determine. It takes an in-depth knowledge of the underlying Oracle tables to understand where to pull the “boss files”. Most Marines do not have the training or knowledge of the Oracle ERP to determine which tables have the master data and which tables are feeding off the master data sets. Additionally, there is significant concern that when GCSS-MC transitions to the Oracle R12 environment, these ad-hoc management reports and tools will be unavailable as Discovery Writer is being replaced by a different business intelligence module and little has been communicated to the end-users with regard to its capabilities (Stewart, 2016).

GCSS-MC does provide visibility of the demand supported items at the retail consumer level. Thus, the enterprise now has visibility of approximately seven million items, valued at over $1.2 billion dollars, which was previously “unseen”.

Additionally, GCSS-MC includes a robust Advanced Planning Suite (APS) that provides the capability to forecast inventory demands and calculate replenishment orders in support of intermediate-level inventory management. The APS suite of applications (e.g. Oracle Demand Planning and Advanced

Supply Chain Planning) is a comprehensive planning solution that decides when and where supplies should be layered within the Marine Corps supply chain (GCSS-MC/LCM CPD, 2010). The intent is for APS to replace the decentralized, stand-alone Microsoft Access and Microsoft Excel files being used at the intermediate level activities.

The Oracle Demand Planning Module uses the sales orders and booking history to generate a forecast using any of the following forecast models: Linear Regression, Polynomial Regression, Exponential Fit, Logarithmic Fit, Asymptotic Fit Single Exponential Smoothing, Double Exponential Smoothing, Croston’s

Method, and Holt-Winters. At the beginning and end of each month, planners are provided an opportunity to review the demand plan, analyze any exceptions or anomalies, and lock-in any changes.

ODP then produces the forecast which planners and analyst access through the Advanced Supply Chain

Planning module. Planners and analysts can review the forecast, see the associated pegging of recommended sales orders to future demands, make any adjustments to the sales order quantity and release the sales orders associated with the forecast plan (Oracle APS, 2012). This would immediately send the sales orders to DAAS and the respective source of supply for fulfillment.

Initially, the intermediate level activities experienced significant challenges when trying to use APS. This was largely due to system complexity and a lack of understanding of how the enterprise resource planner worked. As the SMUs were used to operating independently of each other, there was a lack of coordination with regard to roles and responsibilities for generating the demand plan and reviewing it.

Also, Material Requirements Planning had historically been based on historical demands only, thus there was a lack of understanding of forecasting methods and procedures. These challenges were so significant that APS was taken off-line in 2012.

In July 2013, the Logistics Policy and Capabilities Branch (LPC) at I&L stood up an operational planning team (OPT) to analyze and develop solutions to improve the uses of APS. The APS OPT addressed system latency and database performance issues. They developed procedures to simplify the inventory planning process, as well as key forecasting metrics. Additionally, they assisted the GCSS-MC Program Office in documenting the roles and responsibilities of the various users, with “A Month in the Life of a LOGCOM

APS Administrator” and “A Day in the Life of a Planner using GCSS-MC Logistics Chain Planner-APS Roles” as a result (Bartos, Canady, Hunter 2015).

There are numerous benefits in using APS. It allows for centralized planning across the enterprise and decentralized execution. It provides the ability to process and manipulate large amounts of information quickly and efficiently, it reduces man hours (e.g. eliminates the need to individually key punch each sales order), and enhances the SMU’s capability to affect MEF Readiness. The process of reviewing