Efficiency of Sugar Industry in Sudan


Book Description

The primary aim of this paper to assess the output loss due to inefficient management of Sugar industry in Sudan. An industrial firm is scale inefficient if there is under utilization of production inputs. In this paper we employed nonparametric Data Envelopment Analysis (DEA) to estimate scale efficiency of the major sugar producers in Sudan: Kenana sugar company and Sudan sugar company (SSC) manufacturers: Sennar, Assalaya, New Halfa, and Al-Genied. The finding of the paper indicate Kenana and Al-Genied manufacturers exhibit constant return to scale, whereas the other three sugar manufacturers of SSC: Sennar, Assalaya, and New Halfa exhibit increasing return-to-scale. Increasing return to scale implies inefficient utilization of available input mix. The average output loss due to scale inefficiency for Sudan Sugar Company during the periods 2009, 2010, 2011, and 2012 are respectively 6%, 12%, 14%, and 16% of the benchmark company output level of Kenana. This result implies that for Sudan Sugar Company to increase its efficiency level, needs to manage cane production in Assalya, Sennar, and New Halfa projects on commercial basis, as is the case in Al-Genied, by renting the agriculture land with its infrastructure to private firms to produce sugar cane on commercial basis.




Technical Efficiency of Sugar Industry in Sudan


Book Description

This paper employs Stochastic Frontier Analysis to estimate technical efficiency of major sugar producers in Sudan: Kenana sugar company and Sudan sugar company which owns Sennar, Assalaya, New Halfa, and Al-Genied plants. The production function of sugar output employs two inputs: capital, and labor. The finding of the paper indicate technical inefficiency (distance from optimum production frontier) of Sudan sugar company on average is about 13 percent, implying average output loss of (11,294) tons of sugar per annum for each producer. Estimation results in the paper also indicate kenana sugar company is performing at the highest level of efficiency in the group with only 0.12 percent of inefficiency score. The average output loss due to such technical inefficiency for Kenana company is estimated at 407 tons of sugar per annum. The finding in the paper indicate a major source of inefficiency of Assalya and Al-Genied plants is over staff of employment (or labor productivity decline) as well as under utilization of available capital stock. However, the technical inefficiency of Senar and New Halfa is due to shortage of labor (increasing return to scale) and under utilization of capital stock in the case of New Halfa and over utilization of capital in the case of Senar. Such managerial deficiencies of Sudan sugar company plants may be can be mitigated by reverting to a management style that involve private sector partnership.







Sugar Industry in the Sudan


Book Description







The Sudan Sugar Industry


Book Description







The Sugar Industry of the Sudan


Book Description







Statistical Thinking


Book Description

How statistical thinking and methodology can help you make crucial business decisions Straightforward and insightful, Statistical Thinking: Improving Business Performance, Second Edition, prepares you for business leadership by developing your capacity to apply statistical thinking to improve business processes. Unique and compelling, this book shows you how to derive actionable conclusions from data analysis, solve real problems, and improve real processes. Here, you'll discover how to implement statistical thinking and methodology in your work to improve business performance. Explores why statistical thinking is necessary and helpful Provides case studies that illustrate how to integrate several statistical tools into the decision-making process Facilitates and encourages an experiential learning environment to enable you to apply material to actual problems With an in-depth discussion of JMP® software, the new edition of this important book focuses on skills to improve business processes, including collecting data appropriate for a specified purpose, recognizing limitations in existing data, and understanding the limitations of statistical analyses.