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METHODOLOGY The weights c; represents the price (the value or shadow cost) of

Figure 1 METHODOLOGY The weights c; represents the price (the value or shadow cost) of

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one unit of input Xi, 4245, yk = 1, 2, ..., 8, and hj represents the price (or the value of contribution) of one unit of
one unit of input Xi, 4245, yk = 1, 2, ..., 8, and hj represents the price (or the value of contribution) of one unit of
Here, the input inequality constraint (13) is replaced by a strictly retaining the input level of DMUO unchanged at “0° Vi. To achieve this we propose a strictly output-oriented model with zero input slacks as follows,
Here, the input inequality constraint (13) is replaced by a strictly retaining the input level of DMUO unchanged at “0° Vi. To achieve this we propose a strictly output-oriented model with zero input slacks as follows,
* GLCs, Note: USD 1.00 = RM 3.38, Source: Malaysian Business, 16th October 2009. Table 1. Contd.
* GLCs, Note: USD 1.00 = RM 3.38, Source: Malaysian Business, 16th October 2009. Table 1. Contd.
Table 2. Descriptive statistics
Table 2. Descriptive statistics
In our analysis only the top six performers operate under CRS, and are 100% scale efficient, thereby implying the existence of multiple most productive scale size. The remainder DMUs all exhibit DRS. This suggests that most MB100 companies not only show serious’ scale inefficiency, but also operate in a region of DRS. No company is operating in an IRS region. Hence it may be concluded that one reason for the inefficiency of these companies comes from its scale size. It is operating under DRS leading to the fact that any increase in input results in less than proportionate increase in outputs or improvement in performance. Apart from the inefficiencies that could arise in the conversion process, another reason for the inefficiencies of the opera efficie efficie opera opera inefficient units could be attributed to the scale of ions. DMUs that do not operate at the most nt (or productive) scale size cannot be _ fully nt. The inefficiency may arise because it is ing under DRS or IRS. A DMU is said to be ing under DRS if changing all inputs by the same proportion results in a smaller proportional change in outpu DMU s. IRS may also be defined similarly. Whether a is operating under IRS or DRS can be determined by observing its TE and PTE efficiency scores, such that
In our analysis only the top six performers operate under CRS, and are 100% scale efficient, thereby implying the existence of multiple most productive scale size. The remainder DMUs all exhibit DRS. This suggests that most MB100 companies not only show serious’ scale inefficiency, but also operate in a region of DRS. No company is operating in an IRS region. Hence it may be concluded that one reason for the inefficiency of these companies comes from its scale size. It is operating under DRS leading to the fact that any increase in input results in less than proportionate increase in outputs or improvement in performance. Apart from the inefficiencies that could arise in the conversion process, another reason for the inefficiencies of the opera efficie efficie opera opera inefficient units could be attributed to the scale of ions. DMUs that do not operate at the most nt (or productive) scale size cannot be _ fully nt. The inefficiency may arise because it is ing under DRS or IRS. A DMU is said to be ing under DRS if changing all inputs by the same proportion results in a smaller proportional change in outpu DMU s. IRS may also be defined similarly. Whether a is operating under IRS or DRS can be determined by observing its TE and PTE efficiency scores, such that
Table 3. Contd. Analyzing non performing DMUs
Table 3. Contd. Analyzing non performing DMUs
Table 4. Distribution of DMUs according to efficiency scores. The top six performers which scored 100% in all three measures (PTE, TE and SE) are ranked according to the number of peer counts, that is, the number of times it is a peer to the inefficient units since the peer count number can be consider as a measure of the extent to which the performance of an efficient company can be a useful benchmark for the inefficient ones (Mostafa, 2007).
Table 4. Distribution of DMUs according to efficiency scores. The top six performers which scored 100% in all three measures (PTE, TE and SE) are ranked according to the number of peer counts, that is, the number of times it is a peer to the inefficient units since the peer count number can be consider as a measure of the extent to which the performance of an efficient company can be a useful benchmark for the inefficient ones (Mostafa, 2007).
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