Factors commonly studied in Control Design are: error generation loss due to undetected errors costs and effectiveness of controls My study is to explore the impact of the process structure the error generation and functioning of control, and ultimately, to the objective to risks management of BP. Next I will explain why. The topology affects Error Propagation because if errors created early in the process , it gets spread to all the downstream tasks, A potential loss is carried by each downstream task when a task is performed on the wrong information whereas if errors created towards the end of the process , the impact of the errors would be smaller.
Process Structure also affects Controls functioning Coz the merging node typically 1 have information coming from multiple sources, thus, it would be easier for controls to detect and correct errors at the merging node, by having more info. available. 2. Process errors of the same type at the same time, will more effective
Loss as a function of errors By applying control, we can calculate the risk reduction and formulation the control allocation problem: Given a budget, find the optimal set of x_is that maximizes the net benefit of control This is a convex optimization problem, can be solved optimally using Langranging multiplier approach. I will not show you the mathematics to solving it. Instead, I show you the solution of a simplified version to derive some insights from the pattern shown in the solutions, Which are applicable also to the generalized case.
Control is used to mitigate errors and the error associated risks. We abstract the characteristics of different controls, consider controls as classifiers that detect and correct errors. The total available control resources are considered as a fix pool, which is normalized as “1”. This pool of resources can be divided into any smaller portions and allocate to different task locations. Control allocation factor, $x_i$ represent the portion of control resources allocated to task I, We assume that the portion of control allocated to task I determines the effectiveness and cost of the control. The effectiveness of control in our model is the probability of a control catching an error, given errors exists. We assume diminishing effectiveness as allocation increases. Thus effectiveness function of is modeled as a concave power function of allocation. The parameter g_i represents the maximum effectiveness that the control can achieve at task i. Cost function of is modeled as a convex power function of allocation.
test numerically to go beyond the limiting assumptions of the analytical model. In particular, what is the effect of variation in topological structure? Variation in correlations between errors and controls? Skew in the distribution of costs of errors
Goal To test the effect of various factors of a process on the optimal control solution. test numerically to go beyond the limiting assumptions of the analytical model. In particular, what is the effect of variation in topological structure? Variation in correlations between errors and controls? Skew in the distribution of costs of errors
Objective of case study is to show Model is applicable to real scenarios Parameters of the model is obtainable from real data
We collected data about their Order Fulfillment Process from a pharmacy near Pittsburgh. The Data consists of 15 tasks, 13 internal tasks, 46 errors that occur in different tasks, costs per error per type, frequencies of error occurrences, cost factors of controls.
1) Enter order information, ==error generated, error propagation 4) Prove prescription, == cost of errors, error propagation 9) Prepare and send claims to an insurance company or 10) to the responsible party , == cost of fixing errors 13) Update ledgers , == cost of presence of errors
With moderate control, risks are reduced significantly
I have proposed a methodology for BP design that brings a holistic view of error impacts in BP, Accounts for process structure in error generation, propagation and mitigation Strikes a balance among the risk factors and provides optimal control design framework to mitigate risk exposure Lending itself to implementation within process modeling workbenches offered by leading software vendors