# Monitoring the Performance of Petrochemical Organizations in Saudi Arabia Using Data Envelopment Analysis

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## Abstract

**:**

## 1. Introduction

## 2. Data Envelopment Analysis (DEA)

#### 2.1. DEA Models

#### 2.2. Methodological Extension of DEA

#### 2.3. Tie-Breaking Models for Super Efficiency

#### 2.4. Multidimensional Scaling (MDS)

## 3. Data Analysis

#### 3.1. Data Preparation and Selecting I/O Variables

#### 3.2. Super-Efficiency Model (SEM)

## 4. Results and Discussions

**Model 1**uses all of the 5 input and 5 output variables, where it is observed that 9 out of the 10 petrochemical companies have scored technical efficiency levels of 100% at the DEA frontier, which does not help to discriminate the performances. On the other hand, the super-efficiency computation managed to significantly separate them from one another, noting that the scores in % overflow the scale of 0–100 due to the calculations provided through the super-efficiency models used. However, the results in Table 2 are normalized to fit them in the scale of 0–100%. The Saudi Arabian Fertilizer Company (SAFCO) (Jubail, Saudi Arabia) and Saudi Arabian Basic Industries (SABIC) (Riyadh, Saudi Arabia) achieved 100.0% technical efficiency and super-efficiency with normalized scores. The Yanbu National Petrochemical Company (YANSAB) (Yanbu, Saudi Arabia) and the Sahara Petrochemical Company (SPCO) (Jubail, Saudi Arabia) come next with super-efficiency scores of 73.52% and 50.08%, respectively, followed by PETROCHEM (Jubail, Saudi Arabia) with score of 22.03% and the Saudi International Petrochemical Company (SIPCHEM) (Jubail, Saudi Arabia) with score of 21.82%. The remaining 4 companies performed more or less the same. The Saudi Industrial Investment Group (SIIG) (Riyadh, Saudi Arabia) achieved technical efficiency of 85% and super-efficiencies of 8.5% only. DMUs away from the DEA frontier are expected to show low super-efficiency scores, as clearly indicated in [51]. In

**Model 1**, all input variables, including depreciation, were considered to be controlled. When it was considered as an uncontrolled input variable, the performance efficiency scores of YANSAB and SPCO also rose to 100.00%. Thus, it is very clear that under controlled input scenarios, the discrimination of DMUs becomes vague, as a large number of DMUs tends to cluster around the nearest ones.

**Models 2, 3, 4,**and

**5**were constructed by changing the input/output combinations. In all of the model combinations, SAFCO was found to exhibit the highest super-efficiency scores, ranging from 23.74% to 100.00%. The technical and super-efficiency scores of SABIC in

**Model 4**were found to be 47.10% and 4.71% only, respectively. In

**Models 2**and

**5**, the performance of SABIC and YANSAB were found to be very close. In

**Model 5**, SABIC achieved an efficiency of 100.00%. and YANSAB performed at 20.89%, compared to 73.52% in

**Model 1**. The performance of The Rabigh Refining and Petrochemical Company (PETRORABIGH) (Rabigh, Saudi Arabia) and SIIG remain very low in all the models, and the National Industrialization Company (NIC) (Riyadh, Saudi Arabia) was found to be slightly above them. Meanwhile, the performance of SIPCHEM, PETROCHEM, and the Saudi Kayan Petrochemical Company (KAYAN) (Jubail, Saudi Arabia) were found to be very close together in all models, though SIPCHEM is a little bit ahead of the other two, with super-efficiency scores ranging from 3.56% to 21.82%. It was found that at low technical efficiency levels, when DMUs are far away from the DEA frontier, both the technical and super-efficiency scores tend to be the same or closer. For example, in the case SIIG, technical efficiency is lowest at 1.52% in

**Model 4**, and the maximum is 8.5% in

**Model 1**and

**Model 5**. It may be noted that the super-efficiency scores of SIIG before normalization are the same as those of the technical efficiency scores. The estimated level of improvement that can be achieved for DMUs based on the I/O configuration is tabulated in Table 3; about 35.3% improvement is possible in the gross profit margin %. Average price, an input variable, has a potential of 11.57%. Depreciation records the minimum possibility of improvement, with only 0.83%. Efficiency plots for the 10 companies, with “Gross Profit Margin %”, and “Average price” indicated on the x- and y-axes of the plots, respectively, are given in Figure 1a,b. Such efficiency plots from the software demonstrate the relative positioning of the companies with respect to the variables chosen. SAFCO, with a gross profit margin of 64.70%, sits at the top of the list for efficiency and hence is placed at the top right-hand corner of the plot.

## 5. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Table 1.**Petrochemicals Input and Output Data for 2013, as on 31/12/2013 [50].

Companies | Inputs | Outputs | ||||||||
---|---|---|---|---|---|---|---|---|---|---|

Avg. Price | General Admin Expenses | Depreciation & Amortization | Total Assets | Owners’ Equity | Gross Profit | Book Value | P/B | Gross Profit Margin (%) | Net Cash Flow | |

SAFCO | 151 | 81140 | 373936 | 9459857 | 8268786 | 2878615 | 25 | 6 | 68 | 3272122 |

YANSAB | 58 | 231851 | 1080097 | 4607895 | 15043331 | 3224790 | 27 | 3 | 34 | 4138027 |

SIPCHEM | 23 | 136535 | 558071 | 16688750 | 5793223 | 1298580 | 16 | 2 | 32 | 1739805 |

SABIC | 97 | 12759672 | 14283312 | 339070569 | 156271417 | 55344363 | 52 | 2 | 29 | 56421004 |

NIC | 28 | 915657 | 1462237 | 47270232 | 12006133 | 4837264 | 18 | 2 | 27 | 3962872 |

SPCO | 16 | 22324 | 211170 | 8678361 | 5794433 | 432159 | 13 | 2 | 18 | 426972 |

PETROCHEM | 22 | 250516 | 819114 | 21005725 | 4120810 | 726515 | 9 | 3 | 16 | 788636 |

SIIG | 26 | 261541 | 819419 | 25374069 | 6331913 | 726515 | 14 | 2 | 16 | 783016 |

KAYAN | 12 | 366843 | 2291716 | 46217826 | 14093625 | 602333 | 9 | 2 | 6 | 2562576 |

PETRORABIGH | 17 | 695240 | 2196598 | 45593819 | 8917457 | 461093 | 10 | 2 | 1 | 2593015 |

SABIC – Saudi Arabian basic Industries; SAFCO - Saudi Arabian Fertilizer Company; YANSAB - Yanbu National Petrochemical Company; SPCO - Sahara Petrochemical Co.; PETROCHEM – Petrochem Middle East (PME); | SIPCHEM - Saudi International Petrochemical Company; KAYAN - Saudi Kayan Petrochemical Company; NIC - National Industrialization Company; PETRORABHIGH - Rabigh Refining & Petrochemical Co., SIIG - Saudi Industrial Investment Group |

Models | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | |||||
---|---|---|---|---|---|---|---|---|---|---|

Minimise Inputs | Gen. Admin Exp., Total Assets, Owner’s Equity, Depreciation, Avrg. Price | Gen. Admin exp., Total Assets, Depreciation | Gen. Admin exp., Total Assets, Owner’s equity | Gen. Admin Exp., Total Assets, Depreciation | Owner’s Equity, Depreciation, Avrg. Price | |||||

Outputs | Gross profit, Net Cash Flow, P/B, Book Value, Gross Profit Margin | Net Cash flow, P/B | Gross Profit, Net Cash Flow, | Net Cash Flow, P/B | Book value, Gross Profit Margin | |||||

Company | Tech Eff. (%) | Sup. Eff (%) | Tech. Eff.(%) | Sup. Eff (%) | Tech. Eff (%) | Sup. Eff (%) | Tech. Eff (%) | Sup. Eff (%) | Tech. Eff (%) | Sup. Eff (%) |

SAFCO | 100.00 | 100.00 | 100.00 | 100.00 | 100.00 | 23.74 | 100.00 | 28.56 | 100.00 | 100.00 |

SABIC | 100.00 | 100.00 | 100.00 | 100.00 | 96.60 | 9.66 | 47.10 | 4.71 | 100.00 | 100.00 |

YANSAB | 100.00 | 73.52 | 100.00 | 32.18 | 100.00 | 25.96 | 100.00 | 25.96 | 100.00 | 20.80 |

SPCO | 100.00 | 50.00 | 100.00 | 36.35 | 54.60 | 5.46 | 100.00 | 12.12 | 100.00 | 25.24 |

PETROCHEM | 100.00 | 22.03 | 40.30 | 4.03 | 49.50 | 4.59 | 22.80 | 2.28 | 100.00 | 14.06 |

SIPCHEM | 100.00 | 21.82 | 53.60 | 5.36 | 75.90 | 7.59 | 35.60 | 3.56 | 100.00 | 19.70 |

KAYAN | 100.00 | 15.36 | 19.60 | 1.96 | 45.90 | 4.59 | 17.30 | 1.73 | 100.00 | 13.33 |

NIC | 100.00 | 12.92 | 37.90 | 3.79 | 100.00 | 11.57 | 31.00 | 3.10 | 99.50 | 9.95 |

PETRORABIGH | 100.00 | 11.60 | 19.00 | 1.90 | 73.50 | 7.35 | 15.40 | 1.54 | 88.20 | 8.82 |

SIIG | 85.00 | 8.50 | 33.30 | 3.33 | 32.10 | 3.21 | 15.20 | 1.52 | 85.00 | 8.50 |

Optimization Mode | Var. returns | Var. returns | Const. returns | Const. returns | Var. returns | |||||

Min. inputs | Min. inputs | Min. inputs | Min. inputs | Min. inputs |

Input/Output Variables | Improvement % |
---|---|

Average Price | 11.57 |

General Admin Expenses | 10.38 |

Total Assets | 7.75 |

Owners’ Equity | 2.83 |

Depreciation | 0.83 |

Profit Margin | 35.30 |

Gross Profit | 14.59 |

Net Cash Flow | 10.61 |

P/B | 3.39 |

Book value | 2.75 |

MDS Plot | Company | Super-Efficiency (%) |
---|---|---|

Quadrant I and II | SAFCO | 100.0 |

SABIC | 100.0 | |

YANSAB | 73.5 | |

SPCO | 50.1 | |

Quadrant III | SIPCHEM | 21.8 |

PETROCHEM | 22.0 | |

KAYAN | 15.4 | |

Quadrant IV | NIC | 12.9 |

PETRORABIGH | 11.6 | |

SIIG | 8.5 |

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

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**MDPI and ACS Style**

Alidrisi, H.; Aydin, M.E.; Bafail, A.O.; Abdulal, R.; Karuvatt, S.A. Monitoring the Performance of Petrochemical Organizations in Saudi Arabia Using Data Envelopment Analysis. *Mathematics* **2019**, *7*, 519.
https://doi.org/10.3390/math7060519

**AMA Style**

Alidrisi H, Aydin ME, Bafail AO, Abdulal R, Karuvatt SA. Monitoring the Performance of Petrochemical Organizations in Saudi Arabia Using Data Envelopment Analysis. *Mathematics*. 2019; 7(6):519.
https://doi.org/10.3390/math7060519

**Chicago/Turabian Style**

Alidrisi, Hisham, Mehmet Emin Aydin, Abdullah Omer Bafail, Reda Abdulal, and Shoukath Ali Karuvatt. 2019. "Monitoring the Performance of Petrochemical Organizations in Saudi Arabia Using Data Envelopment Analysis" *Mathematics* 7, no. 6: 519.
https://doi.org/10.3390/math7060519