Development of Ops-BOM Rules

Each engine program generally contains between 7500 and 9000 parts. However, not each part is important to scrutinize when assessing aggregate risk of an engine program. For example, MRL assessments don’t necessarily need to be conducted for every fastener in an engine, but each set of fasteners has a line item in the global EBOM. The goal of the Ops-BOM process was the use attribute data about each part present in Pratt & Whitney BOM databases to determine what set of rules could reasonably result in a representative list of parts from an engine program.

Two main engine programs were chosen to test and validate the Ops-BOM pro-cess referred to in this thesis as Program K and Program F. Although several factors about each part were available to use in the decision model, the initial approach to rules development centered around emulating industry experts. A major module was chosen to develop an industry knowledge-based list of parts gathered from inter-views with stakeholders across the Operations organization. During the stakeholder list discussion, it became clear that the goal of this effort was to be able to identify assembly-ready parts. Assembly-ready parts can be conceptualized as the final

prod-uct of a manufacturing process before final assembly. The difficulty is determining which parts are considered assembly-ready, because there is no single BOM level or definition with which to filter a BOM. Using industry experts to comb through sec-tions of BOMs and determine assembly-ready parts gives us a way to computationally determine what data might have emulated the same result.

The initial emulation model used a hand-crafted list of assembly-ready parts along with part attribute data to construct a decision tree. Success of the decision tree output was measured by minimizing deviations from the hand-crafted list. Figures 5-1 and 5-2 show the initial decision trees generated from the hand crafted industry knowledge BOMs.

Examining the decision tree outputs, we can determine the deciding data factors that contributed to providing a solution as close as possible to the hand-crafted list of assembly-ready parts. Two important attributes were found to be "Item Type"

and "Part Type". The item type is a classifier describing the complexity of the part in question. The part type is a description the function of the part such as fastener, supporting structure, or turbine component. This thesis uses letter codes to anonymously represent different part types for intellectual property reasons. Program K resulted in a simple decision tree with only two deciding variables to determine whether a part enters the Ops-BOM or not. These two factors were part type as well as the item type of the next higher assembly. The Program F decision tree included a few more decision points, but only added one additional decision variable, the item type of the part itself.

The results of the initial decision tree analysis helped drive the discussion regarding the official set of rules to be used for the Ops-BOM. Although we could have attempted to use one of the decision trees, the ownership system for the Ops-BOM process at Pratt & Whitney is not heavily integrated with data systems. The goal of this phase was to develop a method of generating an Ops-BOM that displayed a limited list, but had visibility into the rest of the BOM. A primary focus of this effort was to align rule definitions with concrete data so that the process could be automated given accessibility to data.

Figure 5-1: Program K decision tree for generation of Ops-BOM. The manually sorted training set identified 24% of the EBOM parts as critical for risk assessment. The automated attribute-based sorting method described by this tree resulted in capturing 31% of the EBOM. Two key attributes were identified: Item-Type of Next Higher Assembly and Part Type.

The first branch in both trees had to do with the next higher assembly’s item type.

The different types of items are detail, assembly, and non-machined assembly (NMA).

Details tend to be smaller parts consisting of a single piece of sheet or machined metal.

Assemblies are generally groups of details or other assemblies that have been fastened together. For instance, several sheet metal details might join together to create an assembly bracket. Finally, NMAs are collections of assemblies and details that are not necessarily one single part. For example, a NMA could be a set of turbine blades necessary to build one engine. However, the blades are not joined together into a single physical assembly. Often, NMAs are groups of parts or large sections of the

Figure 5-2: Program F decision tree for genereation of Ops-BOMM. The manually sorted training set identified 18% of the EBOM parts as critical for risk assessment.

The automated attribute-based sorting method described by this tree resulting in capturing 19% of the EBOM. Three key attributes were identified: Item-Type of Next Higher Assembly, Item-Type of Subject Part, and Part-Type.

final engine. While details and assemblies are tracked closely at the manufacturing level, NMAs are mostly used for final assembly and tracking engine level metrics. The first decision for both trees was to eliminate any parts with next higher assemblies that are classified as assemblies. This suggests that the most significant rule for determining if a part is assembly-ready is if the parent assembly to the subject part is classified as NMA. Intuitively this makes sense because NMAs tend to be collections of parts ready to be put into the final frame of the engine. The parts that roll up into NMAs are single units, but are ready to serve their purpose in the greater assembly. These parts can be details and sub-assemblies, but once they enter NMA,

the fabrication is usually finished.

The second common point between the two trees in Figures 5-1 and 5-2, is the use of Part Type as a significant differentiator. Both trees created a rule to eliminate all parts of part type AN, AS, MS, and ST. These are all standard part types that refer to items not unique to Pratt & Whitney. These are generally fasteners, nuts, and other standard equipment that is not manufactured specifically for the engine.

Since these parts exist in every level of the BOM, simply checking whether NMA was the parent item type would not have worked.

While Program K only used these two rules, Program F created a few more branches associated with the item type of the subject part itself. An interesting point is that the item type is used twice, once to include NMA and detail items, and another time to exclude remaining NMA items. The team examined the training data to determine why the decision tree would be outputting these decisions, and found that in most cases, NMAs were not included in the Ops-BOM because they existed outside of the scope of manufacturing teams.

Aggregating the analysis results, the team developed a set of rules as follows:

0. Start with full EBOM

1. Remove all parts that do not roll up to NMA (non-machined assemblies) 2. Remove all NMAs

3. Remove all parts of type: AN, AS, MS, ST, NAS, and DS 4. Remove all duplicate part numbers

The final rule list was heavily influenced by the decision tree analysis of the man-ually generated BOM, but included some additional rules deemed necessary during additional deep dive into the data. “DS” was found to be another part type not necessary in the Ops-BOM, and while doing a final pass the team discovered line duplications in the raw data. Rules 3 and 4 were modified to reflect the update. The rules begin indexing at 0 because Rule 0 is the definition of the input. Rules 1-4 act to select parts for the Ops-Bom.