Calculating Risk Impact

The preferred output metric from customers in the Operations organization is cost.

Measuring risk in the form of cost shows how much cost in a given time period was incurred due to risk in the process. A risk-less process will incur no additional cost to the business besides the strict cost of producing the product. The real time operations metrics listed above can be combined to give an assessment of risk. Figure 5-3 shows how the original organization structure separated operations metrics from the risk assessment, but integration of metrics through a combined calculation will provide benefits to both processes.

The challenge of combining the metrics was in the units of each metric being differ-ent. Initially the team considered the possibility of collecting historical impact data, and generating a training set similar to what was done for creation of the Ops-BOM.

However, this kind of measurement had never been done before, and attempts to cre-ate training values were heavily biased by the methods used to retroactively calculcre-ate historical values. Therefore, further efforts were spent working with members in cost, quality, and management to understand the dollar impact of different events to the

Figure 5-3: Information flow diagram of current system and the data driven risk anal-ysis process. Cost, quality, and delivery metrics are currently considered separately, and not integrated into MRL assessment process. Metrics are described in different units and hard to compare resulting in high variability risk assessment results. The proposed solution is to integrate collection and analysis of operations metrics with each other and the MRL assessment to drive consistency and quantitative validation in the risk assessment process.

Pratt & Whitney business.

CoPQ was already in dollar value and by itself represents an incurred price that the company paid due to the quality risk in the manufacturing process. This value could be simply summed into the total risk value without having to undergo unit conversion. Business costs were traced with the cost team regarding processing of QNLIs. The total labor cost, processing cost, and final closeout cost was calculated per QNLI and aggregated into a proprietary conversion factor, 𝐶𝑞.

Cost is also already represented in dollar value, and is one of the other reasons for representing risk as an incurred cost. Target cost is subtracted from the actual cost because it is not considered a risk by the definition provided in this thesis. Actual cost contributes positively to risk, as this ideally is cancelled out by the target cost.

Perhaps the most difficult metric to integrate is on time delivery. Initial discussions focused around using delivery as a normalizing percentage. For example, if total impact without considering delivery is $100, then a 50% rate of OTD should result in a final total impact double the original at $200. Conceptually this means that the incurred risk of a process would approach infinity as the OTD% approached zero as shown in Figure 5-4.

Using delivery as a normalization tool produces unrealistic results close to the boundaries of the relationship. As OTD approach zero, total risk could face a mul-tiplication factor of 100 if OTD for a process were 1%. Initial uses of normalization found several parts showed an unreasonably high risk value due to the inflationary effects of simple normalization. From interviews with cost and manufacturing teams, cost of delivery tended to fluctuate around the actual cost of the part. Parts that were more expensive to produce also were the costliest to the manufacturing process when delayed. Intuitively, parts are more expensive because of the need for a lot of processing steps and value added through the manufacturing chain. These are also the parts that are critical to deliver to internal customers on time. Rather than nor-malizing the risk against OTD, the team implemented a delivery cost scaled against actual cost to the OTD, shown in Equation 5.1.

Figure 5-4: Inverse relationship between risk and OTD with delivery as a normaliza-tion variable. As OTD approaches 0, Risk approaches infinity. Simple normalizanormaliza-tion of OTD yields extreme results at boundary values.

𝐷𝑒𝑙𝑖𝑣𝑒𝑟𝑦𝐶𝑜𝑠𝑡 = 𝐴𝑐𝑡𝑢𝑎𝑙𝐶𝑜𝑠𝑡(1 − 𝑂𝑇 𝐷) (5.1)

Scaling delivery cost by actual cost means that at low OTD, the cost incurred due to delinquent delivery approaches doubling the entire cost of the part. However, if the OTD is close to 1, the delivery cost incurred is negligible.

Combining quality, cost, and delivery into a single model for calculating risk is shown in the following overall equation:

𝑅𝑖𝑠𝑘𝐼𝑚𝑝𝑎𝑐𝑡 = 𝐶𝑜𝑃 𝑄+𝐶_𝑞*𝑄𝑁 𝐿𝐼+𝐴𝑐𝑡𝑢𝑎𝑙𝐶𝑜𝑠𝑡+𝐴𝑐𝑡𝑢𝑎𝑙𝐶𝑜𝑠𝑡*(1−𝑂𝑇 𝐷)−𝑇 𝑎𝑟𝑔𝑒𝑡𝐶𝑜𝑠𝑡 (5.2) The risk impact equation is a combination of the quality, cost, and delivery metrics identified and connected to the risk dashboard. The central dashboard contains the raw input data, and also logic to calculate risk for each part in the engine given Equation 5.2. Working directly with quantitative values in a versatile unit such as cost allows for easy aggregation of risk in any grouping. To determine risk impact of a module consisting of several parts, the total risk is simply the sum of the individual part risks. This allows for program level aggregation so that upper management can compare risk across programs to drive high level funding decisions.