Essays on Computational Analytics of Value Networks Assignment

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The paper "Computational Analytics of Value Networks" is an amazing example of a Business assignment. DEA analysis describes a performance measurement technique used to evaluate the performance of different decision-making units (DMU). It helps decision-makers too. Calculate an efficiency score for a firm. This helps determine whether a firm is efficient or has room to improve.   Determines how input needs to be added or how much output needs to be reduced to make a firm efficient. •       Identify the nature of returns of a firm to its scale to determine how the scale needs to change for the firm to reduce the average cost. •       Identify a set of benchmarks, which can be used to improve the rest of the firm’ s processes by comparing against the benchmarks. In our case, we are analyzing the productivity of 20 hospitals using the data provided.

A DEA analysis was conducted for the 20 hospitals using the DEAP analysis software. Each of the different scenarios is discussed below with the implications of each result used to advise on the managerial and operational areas for improvement as appropriate.

The following acronyms will be used for interpreting the data. •       VRS – Variable Returns to Scale •       TE – Technical Efficiency •       SE – Scale Efficiency   Case 1: Use DEA where the goal is to minimize inputs In the case of an output-oriented model, it is assumed that the hospital manager has more control over the inputs rather than the number of patients arriving at the hospital for acute or minor cases. Table 1 shows the calculation of benchmarking for peers of each hospital using input-oriented VRS efficiency for all twenty NSW Health hospitals. Table 1:  Results of DEA using DEAP for input – oriented VRS Model   -- Efficiency -- --Benchmarks-- DMU No.

(Hospital) Input-Oriented VRS Efficiency Peer 1 Weight (λ ) Peer 2 Weight (λ ) Peer 3 Weight (λ ) Peer 4 Weight (λ ) 1 0.885 12 0.103 15 0.897         2 0.360 12 0.167 15 0.833         3 0.770 15 0.810 6 0.190         4 0.626 15 0.378 6 0.622         5 1.000 6 1.000             6 1.000 6 1.000             7 0.561 12 0.049 6 0.319 15 0.632     8 1.000 8 1.000             9 1.000 9 1.000            


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