TSHWANE UNIVERSITY OF TECHNOLOGY

THE RESOURCE ALLOCATION MODEL (RAM)

Version 1.3

DRAFT

15 JULY 2004

This document describes the development of a Resource Allocation Model (RAM) for TUT that should be used as an interactive planning tool for financial forecasts and determining annual budget allocations.

BACKGROUND

The National Plan for Higher Education urges institutions to improve the overall efficiency and effectiveness of the higher education system. This cuts across all programmes and activities of the institution, and in particular for this document the generation and utilization of financial resources.

Furthermore, government has developed a number of benchmarks and performance indicators that need to be adhered to. This has led to the development of a new funding framework for higher education, into which some of these benchmarks have been built as parameters. Funding is directly linked to the performance / non-performance in these items. It is actually the first time in history that government is going to steer the system in such a strong way, by reducing subsidy for weak performance.

The merger of the former three technikons into the Tshwane University of Technology (TUT) has placed a major responsibility on the management to develop the strategic plan / institutional operating plan of TUT in accordance to the Merger Guidelines and within clear financial parameters.

The daunting challenges captured in the abovementioned issues has prompted the development of a decision support system in the form of a Resource Allocation Model (RAM) that could be used as an interactive tool in developing long term financial sustainability models and short term budget modeling.

2.	PURPOSE OF THE DOCUMENT

The purpose of the document is to describe the developed Resource Allocation Model. The methodology used in developing this tool consisted of –

Use of the new funding framework as the point of departure;

Develop the model as an income-driven model, i.e. all allocations need to be derived at using the different income streams as the start;

Incorporate the different benchmarks and performance indicators from government, as well as those agreed upon in the strategic plan of TUT;

Use the history over the last few years to obtain a realistic picture where TUT currently stands and where it should be in 3 to 5 years from now;

The different parameters included in the model should be variables such that when they are adjusted the model will automatically be updated, showing clearly the impact of the adjustment made on the resource allocation to different faculties / directorates of the institution.

POINTS OF DEPARTURE

In the first version of RAM, provision has been made for the following type of adjustable benchmarks:

Determining the different income streams which includes the projected income from the new funding framework in different categories, student fees (class fees and levies), NSSPs, residence fees and other income.

Allocating percentages of income in the different streams to earmarked, distributable and incentive funds.

The percentage of income allocated for total staff remuneration.

The percentage of income allocated for staff remuneration in faculties (teaching and learning component).

The percentage of income allocated for staff remuneration in support services (clustered according to staff related, student related and space related).

Benchmark for the ratio Full Time Equivalent (FTE) students per FTE lecturer.

The percentage of funds available for operational costs and its distribution.

The percentage of funds available statutory requirements and its distribution.

The percentage of funds available for corporate accounts and its distribution.

The percentage of funds available for capital expenditure.

The percentage of funds available for strategic projects.

The annual inflation rate.

Growth caps on contact and distance students.

The remuneration package of a Senior Lector Equivalent (SLE).

4.	THE RESOURCE ALLOCATION MODEL

The main component in the model is the new funding framework. A brief illustration of the new funding framework will follow to explain the main categories and variables that contribute to the new framework.

4.1	New Funding Framework

Fundamental issues

A basic feature of the new framework is that it is a goal-oriented mechanism for distributing government grants to institutions in accordance with national priorities and approved institutional plans. The following key assumptions of the old formula fall away:

Government’s key responsibility is not that of contributing to institutional costs and student choices; and

Institutional competition cannot be the sole determinants of size and shape of the Higher Education (HE) system.

Integration of planning and funding

The following diagram set out the key aspects of how the new framework integrates planning and funding.

Diagram1

As indicated in diagram 1 government does not adopt a “hands off” stance as far as HE is concerned. The Minister of Education approves plans for the HE system and plans for individual institutions, and uses the funding framework to implement these plans.

Since the primary purposes of higher education are those of teaching and research, government funds institutions for the rendering of services related the production of graduates/diplomats and research findings. Government’s starting point for a HE budget is not a calculation of actual unit costs in the system. Government decides first of all what services it requires from the HE sector, what it can afford to spend on HE, and then allocates funds to HE in accordance with national needs and priorities.

The Minister divides the higher education budget on a 3-year rolling basis between its different components. This is illustrated in Diagram 2 below:

Diagram 2

` generated by generated by generated by generated by National budget for higher education institutions: 2004/05: R9.9 billion 2005/06: R10.6 billion 2006/07: R11.2 billion Earmarked grants 8% NSFAS 6% Institutional restructuring 5% Other; including interest & redemption on loans 1% & foundation programmes 1% Block grants 87% Teaching input grants 56% Teaching output grants 14% Research output grants 11% Institutional factor grants 6% Approved FTE student places Non-research graduates & diplomates Research masters & doctorates & publications Enrolment size & % disadvantaged students

(a)	Teaching input grants

A flow of the process in the calculation of a teaching input grant for an institution X can be represented in the following way:

Diagram 3: Calculation of teaching input grants

The funding grid and weighting factors approved by the Minister for 2004/5 to 2006/07 is set out in Tables 1 and 2.

Table 1: Funding Grid for Teaching Inputs

Funding Group	CESM categories included in funding group
1	07 education, 13 law, 14 librarianship, 20 psychology, 21 social services/public administration
2	04 business/commerce, 05 communication, 06 computer science, 12 languages, 18 philosophy/religion, 22 social sciences
3	02 architecture/planning, 08 engineering, 10 home economics, 11 industrial arts, 16 mathematical sciences, 19 physical education
4	01 agriculture, 03 fine and performing arts, 09 health sciences, 15 life and physical sciences

Table 2: Weighting Factors for Teaching Inputs: 2004/05 to 2006/07

FUNDING GROUP	UNDERGRADUATE	HONOURS	MASTERS	DOCTORAL
CONTACT	DISTANCE	CONTACT	DISTANCE	CONTACT	DISTANCE	CONTACT	DISTANCE
1	1.0	0.5	2.0	1.0	3.0	3.0	4.0	4.0
2	1.5	0.75	3.0	1.5	4.5	4.5	6.0	6.0
3	2.5	1.25	5.0	2.5	7.5	7.5	10.	10.
4	3.5	1.75	7.0	3.5	10.5	10.5	14.0	14.0

(b)	Teaching output grants

The various steps in the calculation of teaching output grants are summed up in Diagram 4 below:

Diagram 4: Calculation of teaching output grants

The following graduation benchmarks per qualification level are currently being implemented by the Minister of Education in calculating the normative totals.

Graduation Benchmarks

Undergraduate up to 3 years	Contact 22.5%	Distance 13.5%

Undergraduate 4 years and more	Contact 18%	Distance 9%

Honours	Contact 54%	Distance 27%

Post graduates up to masters	Contact 30%	Distance 22.5%

As per the teaching inputs, a weighting is also been applied to the graduates in determining the teaching output units (TOU). These weightings are displayed in Table 3.

Table 3: Weighting Factors for Teaching Outputs

2004/05
1st certificate and diplomas of 2 years or less	0.5
1st diplomas and bachelors degrees: 3 years	1.0
Professional 1st bachelor’s degree: 4 years and more	1.5
Postgraduate and post diploma diplomas	0.5
Postgraduate bachelors degree	1.0
Honours degrees/higher diplomas	0.5
Non-research masters degrees and diploma	0.5

(c)	Research output grants

Non- Research masters graduates are determined as an interim arrangement by the proportion of FTE enrolled research and non-research masters students in the enrolled funded credit HEMIS report, excluding experiential learning.

A weighting is also been applied to the research outputs in determining the Research Output Units (ROU). These weightings are displayed in Table 4.

Table 4: Weighting Factors for Research Outputs

2004/05
Publication units	1
Research masters graduates	1
Doctoral graduates	3

The ratios, 1.25 for universities and 0.5 for technikons, of weighted publication units per permanently appointed instructional/research staff is used to calculate the normative research outputs of an institution:

The various steps in the calculation of research output grants are summarized in Diagram 5 below:

Diagram 5: Calculation of Research Output Grants

(d)	Institutional factor grants

These grants generate additional amounts added to the teaching input grant and are based on:

% of African and coloured FTE students who are SA citizens and in contact programmes (Table 4)

Total number of FTE contact plus distance students (Table 5)

Table 4: Institutional factor grants for disadvantaged students: 2004/05 to 2006/07

Proportion of African + Coloured Students in relevant FTE student enrolment (SA citizens only)	Additional amount added Relevant teaching input grant
80% and above	10%
75%	8.75%
70%	7.5%
65%	6.25%
60%	5%
55%	3.75%
50%	2.5%
45%	1.25%
40% and below	0

Table 5: Institutional factor grants for institutional size

Total FTE student enrolment: contact plus	Additional amount added to the Teaching Input Grant
4 000 and less	15%
6 000	13.6%
8 000	12.1%
10 000	10.7%
12 000	9.3%
14 000	7.9%
16 000	6.4%
18 000	5.0%
20 000	3.6%
22 000	2.1%
25 000 and more	0

Migration Strategy

The triennium 2004/05 to 2006/07 will be the first in which the new funding framework is implemented. A migration strategy will be used to ensure that implementation of the new framework does not have the effect of destabilizing the higher education system. This migration strategy will remain in place until the end of 2006/07

In short, the migration strategy compares the outcome per institution in the new framework to that of the old formula. If a difference of more than 4% is visible, the old formula + 4% is used in determining the grant, otherwise the new formula is used. Currently Tshwane University of Technology falls outside the 4% range.

4.2	Purpose of the Resource Allocation Model

As we have seen in the new funding framework, the Minister of Education expects a Higher Education Institution to be primarily involved in the teaching of students. It would then be appropriate to allocated funds according to the number of students that an entity have.

One of the strengths of this proposed model is the way in which funds are distributed. Two sets of methodologies are used. First the model distributes funds to the academic faculties according to the number of Teaching Input Units (TIU) which they have generated and agreed upon benchmarks. Secondly the model allocates funds to the support services based on the average Rand value per TIU spent by the support services on salary, operational and capital expenditures over the past two years. This is not the ideal for the support services and should only form a departure base. Benchmarks must be determined, a gap analysis performed and a migration strategy developed through a scientific exercise.

The complexity of a subject has been taken into consideration when the weightings of teaching inputs were developed. TIU can therefore be used as a solid base in all the calculations since it will ensure a fair distribution of funds to the different faculties and directorates.

The model further identifies different income sources, determines the distributable income for a budget year and enables the institution to set expenditure targets according to national benchmarks. These targets have been set to be achieved over time and a user-friendly monitoring system will monitor the progress towards achieving the targets.

The model is an interactive model which allows the Financial Planning Task Team and the EMC to evaluate different scenarios immediately.

4.3	Data Source

The student and staff data used in the model originate from the HEMIS database. The finance data were extracted from the general ledger on the ITS system.

A consolidation of the former Technikon Pretoria, Technikon Northern Gauteng

and Technikon North West student, staff and finance data were used.

For demonstration purpose of the model, provisional data of previous years have been used. Readers should hence not make financial calculations based on the data used. Consolidation and finalization of all data is a high priority over the next few weeks.

5.	DIFFERENT COMPONENTS OF THE RESOURCE ALLOCATION MODEL

5.1	Income Sources

During this phase each income source is determined and divided between earmarked, distributable and incentive categories by allocating percentages to the three different categories. If a percentage is allocated to earmarked funds the type of fund should also be identified. Table 6 displays a possible income distribution.

Table 6

2005 % INCOME DISTRIBUTION	EARMARKED	DISTRIBUTABLE	INCENTIVE
GOVERNMENT SUBSIDY
Teaching Input	0.00%	100.00%	0.00%
Teaching Output Grant	0.00%	98.00%	2.00%
Teaching Output Development	70.00%	30.00%	0.00%
Research Output Grant	0.00%	99.00%	1.00%
Research Output Development	70.00%	30.00%	0.00%
Institutional Factors	50.00%	50.00%	0.00%
Interest & Assessment Rates	100.00%	0.00%	0.00%
Long term Liabilities	0.00%	100.00%	0.00%
Residence	100.00%	0.00%	0.00%
TUITION FEES
Class Fee	0.00%	100.00%	0.00%
Levy Fee	100.00%	0.00%	0.00%
Other Levies Fee	20.00%	80.00%	0.00%
Exam fees	0.00%	100.00%	0.00%
Other	100.00%	0.00%	0.00%
NON SUBSIDIZED PROGRAMMES	100.00%	0.00%	0.00%
RESIDENCES	100.00%	0.00%	0.00%
DONATIONS AND CONTRACTS	100.00%	0.00%	0.00%
INVESTMENT INCOME	0.00%	100.00%	0.00%
MISCELLANEOUS	0.00%	100.00%	0.00%

5.2	Parameters/Benchmarks

This is probably the main focus point of the model. Each parameter or value that is set will have an impact on the outcome. The following parameters/benchmarks can be adjusted during the planning phase:

Percentage of the distributable income to be spent on salaries. This is the first target to be achieved by the year 2009 which allows the institution to manage the different processes for achieving it.

Percentage of salary allocation to be allocated to the academic faculties.

Percentage of salary budget to be allocated to the support services. The support services are clustered together according to the type of service that they provide.

Percentage of the difference between distributable income and the salary allocation to be spent on operation expenditures.

Percentage of the difference between distributable income and the salary allocation to be spent on capital expenditures.

Percentage of the difference between distributable income and the salary allocation to be spent on corporate accounts.

Percentage of the difference between distributable income and the salary allocation to be spent on strategic projects.

Ratio between Full Time Equivalent student and staff. This particular ratio is well-known in the higher education sector and therefore the model uses this ratio in the determination of a new ratio TIU: SLE. The new ratio forms the basis in allocating the academic funds. The following formula was used in the determining the new ratio:

T= (B/A)*C where

A = 2003actual FTE ratio

B= 2009 envisage target FTE ratio

C= 2003 actual TIU: SLE ratio

The values have been extrapolated from the data in order to determine the number of TIU required in a particular year for one SLE salary package.

(i)	Expected inflation rate.

(j)	Student enrollment cap. This is necessary for two reasons: First due to caps that have been placed by the Minister of Education and secondly to control unnecessary growth.

(k)	R value of a Senior Lector Equivalent post.

Determine for each academic faculty a primary funding group and for each support directorate the support type (Student related, Staff related, Space related) (SSS)).

The following tables illustrate the above parameters/benchmarks that can be adjusted in order to find the best scenario.

Table 7: TSSS Salary Distribution

SALARY AS % OF BUDGET	ACADEMIC SALARY ALLOCATION	STUDENT SALARY ALLOCATION	STAFF SALARY ALLOCATION	SPACE SALARY ALLOCATION	TSSS
2003	68.3%	55.0%	13.4%	22.3%	9.3%	100.0%
2009 TARGET
BENCHMARK	62.00%	65.00%	9.20%	14.20%	11.60%	100.0%

A reduction in allocation does not necessarily constitute a lower Rand-value if the income can be increases

Table 8: Percentages Allocation in other categories

OPERATIONAL EXPENDITURE ALLOCATION	CAPITAL REPLACEMENT	CORPORATE ACCOUNTS ALLOCATION	STRATEGIC PROJECTS ALLOCATION	STUDENT : STAFF RATIO (FTE)	STUDENT GROWTH CAP	SLE VAUE
2003	81.7%	18.3%	37	Contact	2003	R341,125
2009 Target	2004	R361,000
BENCHMARK	-	-	-	-	25	Distance	2005	R383,288

5.3	Salary Allocations

The first allocation after the distributable income has been determined is the salary allocations utilizing the benchmarks outlined in 5.2.

TUITION (Teaching and Learning)

Since the ratio of TIU: SLE has already been determined a simple calculation for each academic faculty has been made by dividing their actual TIU by the benchmark TIU: SLE and multiply it by the Rand value of a SLE unit to derive the FIRST salary allocation for each academic faculty/satellite. The satellite campuses still need to be awarded an additional allocation due to the fact that they have many more administrative functions that they perform. This is still outstanding. Since this is a first allocation, the model compares the total amount of all the academic faculties and the satellite campuses salary allocations to the amount that was set aside during the parameters setting phase. An adjustment factor is then calculated and the option exists to apply this factor to all academic allocations or to change the original set of parameters to balance the two amounts.

Table 9 illustrates a possible allocation based on the methodology and currently displays dummy data:

Table 9 SALARY ALLOCATIONS FOR THE ACADEMIC ENVIRONMENT

GROUP	EMC MEMBER	EXPENDITURE	2003 ACTUAL	2004 BUDGET	2005 ALLOCATION	2005 FIRST PROV ALLOCATION	2005 SECOND PROV ALLOCATION
SALARY	SALARY	SALARY	SALARY	SALARY
4	Prof Sibara	Agriculture, Horticulture,	1000	1100	1200	1300	1200
4	Prof Van Staden	Arts	1000	1100	1200	1300	1200
2	Prof Sibara	Economic Sciences	1000	1100	1200	1300	1200
1	Prof Van Staden	Education	1000	1100	1200	1300	1200
3	Prof Durrheim	Engineering	1000	1100	1200	1300	1200
4	Prof Durrheim	Health Sciences	1000	1100	1200	1300	1200
2	Prof Van Staden	Information & Comm Technology	1000	1100	1200	1300	1200
2	Prof Sibara	Management Sciences	1000	1100	1200	1300	1200
4	Prof Durrheim	Natural Sciences	1000	1100	1200	1300	1200
1	Prof Van Staden	Social Development Studies	1000	1100	1200	1300	1200
1	Prof Sibara	Tourism, Hospitality and Leisu	1000	1100	1200	1300	1200
2	Prof Du Preez	Witbank	1000	1100	1200	1300	1200
2	Prof Du Preez	Nelspruit	1000	1100	1200	1300	1200
2	Prof Du Preez	Polokwane	1000	1100	1200	1300	1200
Tswane University Of Technology	14000	15500	16800	18200	16800

SUPPORT SERVICES

The model first calculates the average salary expenditure over the last two years per SSS cluster. This average salary expenditure is used to determine the average amount spent per TIU by each SSS cluster. This amount per cluster is then multiplied by the number of TIU in the budget year to derive the salary allocation for each SSS cluster. The average proportion of each support directorate within each SSS cluster over the past two years will determine a FIRST allocation of each support directorate’s portion of his SSS cluster salary allocation. Since this is a first allocation the model compares the total amount of all the SSS clusters salary allocations to the amount that was set aside during the parameters setting phase. An adjustment factor is then calculated and the option exists to apply this factor to all SSS cluster allocations or change the original set of parameters to balance the two amounts. Although this will not solve all the irregularities it will ensure that directorate’s with the same type of functions will be treated in the same manner.

Table 10 illustrates a possible allocation based on the methodology and currently displays dummy data:

Table 10: Salary allocations for the Support Services

SSS	EMC MEMBER	EXPENDITURE	2003 ACTUAL	2004 BUDGET	2005 ALLOCATION	2005 FIRST PROV ALLOCATION	2005 SECOND PROV ALLOCATION
SALARY	SALARY	SALARY	SALARY	SALARY
STUDENT	Mr N J vdM Stofberg	Administration **	1000	1100	1200	1300	1200
SPACE	Prof M Ralekhetho	Building And Estates	1000	1100	1200	1300	1200
STAFF	Dr D Tromp	Finances	1000	1100	1200	1300	1200
STAFF	Prof R L Ngcobo	Foundation	1000	1100	1200	1300	1200
STAFF	Mr M A Mashego	Human Resources	1000	1100	1200	1300	1200
STUDENT	Mr N J vdM Stofberg	Information	1000	1100	1200	1300	1200
STAFF	Prof M Ralekhetho	Information Technology Service	1000	1100	1200	1300	1200
STUDENT	Mr N J vdM Stofberg	International	1000	1100	1200	1300	1200
STUDENT	Prof N P du Preez	Library	1000	1100	1200	1300	1200
SPACE	Prof M Ralekhetho	Logistical Services	1000	1100	1200	1300	1200
STAFF	Prof R L Ngcobo	Marketing And Communication	1000	1100	1200	1300	1200
STAFF	Mr M A Mashego	Personnel Development	1000	1100	1200	1300	1200
STAFF	Prof M Ralekhetho	Publication And Design	1000	1100	1200	1300	1200
STAFF	Prof S J Molefe	Quality Management	1000	1100	1200	1300	1200
STUDENT	Mr N J vdM Stofberg	Recruiting	1000	1100	1200	1300	1200
STAFF	Prof R L Ngcobo	Emc & Senior Management	1000	1100	1200	1300	1200
STUDENT	Mr N J vdM Stofberg	Registrar (Academic)	1000	1100	1200	1300	1200
STAFF	Mr N J vdM Stofberg	Secretariate	1000	1100	1200	1300	1200
STAFF	Prof S G K Lenyai	Strategic Info and Planning	1000	1100	1200	1300	1200
STUDENT	Ms R Maphai	Student Affairs	1000	1100	1200	1300	1200
STUDENT	Prof N P du Preez	Telematics	1000	1100	1200	1300	1200
Prof R L Ngcobo	Unknown
Tswane University Of Technology	21000	23100	25200	27300	25200

5.4	Operational Allocations

TUITION (Teaching and Learning)

The model first calculates the average operational expenditure over the last two years per funding group. This average operational expenditure is used to determine the average amount spent on operational expenditure per TIU by each funding group. This amount per funding group is then multiplied by each academic faculty’s actual number of TIU to derive the FIRST operational allocation for each academic faculty and satellite campus. Since this is a first allocation the model compares the total amount of all the academic faculties, satellite campuses and support services operational allocations to the amount that was set aside during the parameters setting phase. An adjustment factor is then calculated and the option exists to apply this factor to all operational allocations or change the original set of parameters to balance the two amounts.

SUPPORT SERVICES

The model first calculates the average operational expenditure over the last two years per SSS cluster. This average operational expenditure is used to determine the average amount spent per TIU by each SSS cluster. This amount per cluster is then multiplied by the number of TIU in the budget year to derive the operational allocation for each SSS cluster. The average proportion of each support directorate within each SSS cluster over the past two years will determine a FIRST allocation of each support directorate’s portion of his SSS cluster operational allocation. Since this is a first allocation the model compares the total amount of all the SSS clusters operational allocations to the amount that was set aside during the parameters setting phase. An adjustment factor is then calculated and the option exists to apply this factor to all SSS cluster allocations or change the original set of parameters to balance the two amounts.

5.5	Capital Allocations

TUITION (Teaching and Learning)

The model first calculates the average capital expenditure over the last two years per funding group. This average capital expenditure is used to determine the average amount spent on capital expenditure per TIU by each funding group. This amount per funding group is then multiplied by each academic faculty’s actual number of TIU to derive the FIRST capital allocation for each academic faculty and satellite. Since this is a first allocation the model compares the total amount of all the academic faculties/satellite campuses and support services capital allocations to the amount that was set aside during the parameters setting phase. An adjustment factor is then calculated and the option exists to apply this factor to all capital allocations or change the original set of parameters to balance the two amounts.

SUPPORT SERVICES

The model first calculates the average capital expenditure over the last two years per SSS cluster. This average capital expenditure is used to determine the average amount spent per TIU by each SSS cluster. This amount per cluster is then multiplied by the number of TIU in the budget year to derive the capital allocation for each SSS cluster. The average proportion of capital expenditure of each support directorate within each SSS cluster over the past two years will determine a FIRST capital allocation of each support directorate’s portion of his SSS cluster capital allocation. Since this is a first allocation the model compares the total amount of all the SSS clusters and academic faculty’s capital allocations to the amount that was set aside during the parameters phase. An adjustment factor is then calculated and the option exists to apply this factor to all SSS cluster allocations or change the original set of parameters to balance the two amounts.

5.6	Corporate Accounts

For the interim phase of implementation, the model makes use of the corporate account expenditures in 2003 and 2004 made by the former Technikon Pretoria. Until the relevant data is available for all campuses this will be used as input parameter for the calculation of corporate accounts.

The model first calculates the amount spent on corporate accounts per SSS cluster. This amount has been used to determine the R value that was spent per TIU by each SSS cluster on corporate accounts. Since only corporate account expenditure has been used, the model included only the TIU of Technikon Pretoria. Inflation has been added to the average amount that was spent per TIU by each SSS cluster. Each cluster’s average amount has then been multiplied by the number of TIU for TUT to produce the FIRST corporate account allocation per SSS clusters. Since this is a first allocation the model compares the total amount of all the SSS clusters corporate accounts allocation to the amount that was set aside during the parameters setting phase. An adjustment factor is then calculated and the option exists to apply this factor to all SSS cluster allocations or change the original set of parameters to balance the two amounts.

5.7	Strategic Projects

Strategic Projects are being managed by the Executive Management Committee (EMC) on an annual basis. The model makes provision to allocate certain income amounts to be used as strategic projects in two ways. First an allocation can be made from the distributable income by setting a percentage to be allocated to strategic projects during the parameters setting phase and secondly by specify strategic projects when a portion or the total income source has been allocated to as an earmarked fund. Specific strategic projects could include the following:

Teaching Development

Research and Innovation development

Experiential learning.

5.8	Incentive Sub-System

The need arises to acknowledge excellence within different entities. The model currently makes provision to allocate additional revenue to an academic faculty/satellite campus if that academic faculty/satellite campus performed exceptionally. The criteria that has been used in evaluating an academic faculty/satellite is derived from the new funding framework and comprises of two elements:

Graduation Rate

The Number of student headcounts and student graduates in a particular year are being used to determine the graduation rate of an academic faculty/satellite campus. Let’s say an academic faculty/satellite campus enrolled 100 student headcounts and in the same year produced 20 graduates. Then the graduation rate for that academic faculty/satellite campus would be 20/100 = 20%. This is more or less the same calculation that exists in the new funding framework except that in the new funding framework this calculation is done on programme level group.

The next step is then to determine different intervals in which certain incentive awards could be made. Currently the model makes provision for 5 intervals: less than 15%, between 15 and 20%, between 20 and 25%, between 25 and 30% and greater than 30%. Each interval has a R value, which if an academic faculty/satellite campuses graduation rate falls with in that specific interval the TOU of that academic faculty/satellite campus has been multiplied with this incentive amount.

Research Output per Instructional/Research professional

The number of ROU and permanent appointed instructional/research professional staff in a particular year is being used to determine the number of ROU per instructional/research professional staff member.

The next step is then to determine different intervals in which certain incentive awards could be made. Currently the model makes provision for 5 intervals: less than 0.1, between 0.1 and 0.2, between 0.2 and 0.4, between 0.4 and 0.5 and greater than 0.5. Each interval has a R value, which if an academic faculty/satellite campuses number of ROU per instructional/research staff member falls with in that specific interval the ROU of that academic faculty/satellite campus has been multiplied with this incentive amount.

5.9	Reporting Sub-System

A number of interactive reports are available in the Resource Allocation Model to view the impact and compare different scenarios.

6.	SUMMARIZED RESOURCE ALLOCATION MODEL (Data for demonstration purposes only)

2005 INCOME DISTRIBUTION	EARMAR-KED RESERVE	EARMARKED	DISTRIBUTABLE	INCENTIVE	TOTAL
GOVERNMENT SUBSIDY	R 72,056,524	R 617,784,917	R 2,058,560	R 691,900,000
Teaching Input	R 0	R 463,666,475	R 0	R 463,666,475
Teaching Output Grant	R 0	R 94,106,281	R 1,920,536	R 96,026,817
Teaching Output Development	SP	R 30,470,504	R 13,058,788	R 0	R 43,529,292
Research Output Grant	R 0	R 13,664,327	R 138,024	R 13,802,350
Research Output Development	SP	R 11,194,703	R 4,797,730	R 0	R 15,992,432
Institutional Factors	CA	R 28,491,317	R 28,491,317	R 0	R 56,982,633
Interest & Assessment Rates	FIN	R 1,900,000	R 0	R 0	R 1,900,000
Long term Liabilities	R 0	R 0	R 0	R 0
Residence	R 0	R 0	R 0	R 0
TUITION FEES	R 54,085,444	R 311,962,954	R 0	R 366,048,398
Class Fee	R 0	R 309,401,051	R 0	R 309,401,051
Levy Fee	LEV	R 53,444,968	R 0	R 0	R 53,444,968
Other Levies Fee	FIN	R 640,476	R 2,561,903	R 0	R 3,202,379
Exam fees	R 0	R 0	R 0	R 0
Other	R 0	R 0	R 0	R 0
NON SUBSIDIZED PROGRAMMES	R 0	R 0	R 0	R 0
RESIDENCES	R 0	R 0	R 0	R 0
DONATIONS AND CONTRACTS	SP	R 200,000	R 0	R 0	R 200,000
INVESTMENT INCOME	R 0	R 3,000,000	R 0	R 3,000,000
MISCELLANEOUS	R 0	R 5,000,000	R 0	R 5,000,000
TOTAL INCOME	R 126,341,967	R 937,747,871	R 2,058,560	R 1,066,148,398

2005	DISTRIBUTABLE INCOME	ADJUST	TUITION	STUDENT	STAFF	SPACE	TOTAL
66.2%	SALARY	R 362,182,885	R 74,488,046	R 125,946,570	R 58,127,597	R 620,745,099
25.4%	OPERATIONAL	Y	R 78,311,889	R 24,910,526	R 66,004,684	R 68,524,979	R 237,752,079
3.4%	CAPITAL	R 18,912,975	R 5,227,494	R 5,346,516	R 2,213,293	R 31,700,277
5.1%	CORPORATE ACCOUNTS (CA)	R 47,550,416
0.0%	STRATEGIC PROJECTS (SP)	R 0
100.0%	TOTAL	R 459,407,749	R 104,626,066	R 197,297,770	R 128,865,870	R 937,747,871
48.99%	11.16%	21.04%	13.74%	100.00%
2005	TOTAL ALLOCATION	TUITION	STUDENT	STAFF	SPACE	TOTAL
58.22%	SALARY	R 362,182,885	R 74,488,046	R 125,946,570	R 58,127,597	R 620,745,099
22.30%	OPERATIONAL	R 78,311,889	R 24,910,526	R 66,004,684	R 68,524,979	R 237,752,079
2.97%	CAPITAL	R 18,912,975	R 5,227,494	R 5,346,516	R 2,213,293	R 31,700,277
7.13%	CORPORATE ACCOUNTS (CA)	R 6,345,870	R 30,938,391	R 38,757,471	R 76,041,732
3.93%	STRATEGIC PROJECTS (SP)	R 41,865,207
0.19%	INCENTIVE	R 2,058,560
5.25%	EARMARKED OTHER	R 55,985,444
100.00%	TOTAL	R 459,407,749	R 110,971,937	R 228,236,161	R 167,623,341	R 1,066,148,398
43.09%	10.41%	21.41%	15.72%	100.00%

7.	RECOMMENDATIONS

The Financial Planning Task Team is requested to approve and take note of the following recommendations/points of concern for the successful implementation of the Resource Allocation Model:

The Resource Allocation Model should be accepted as a tool for the planning of budget allocations and long-term financial strategies of TUT.

2.	An investigation into the effectiveness and efficiency with regard to the support services needs to be done. Part of this investigation should focus on individual budget allocations to the different support directorates through an Activity Based Costing methodology. A comparison study with other higher education institutions is also necessary, a gap analysis performed and a migration strategy developed through a scientific exercise should be done.

3.	Further investigations into the budget spending and allocation on Corporate Accounts and Strategic Projects need to be done.

4.	The model needs to be refined/expanded in the following areas:

Adjustment Module

A phase-in approach is necessary in order to adjust certain allocations that the model produces during the first few years.

Student Enrolment Management Module

There is an urgent need for a Student Enrolment Management Module, which is linked to the proposed PQM for TUT. It should cater for handling different scenarios, in particular geared at achieving a realistic student: staff ratio and the SET: Business: Humanities: ratios. The Department of Education has already started with an Enrolment Management Strategy and TUT will receive R12 million less in 2005 in government subsidy due to growth caps that were placed by the Minister of Education.

Setting of Benchmarks/Targets

The current benchmarks/targets should be tested with other higher education institutions, after which the EMC should set and accept the final benchmarks for TUT.

Develop a Web-based version of the Resource Allocation Model for easy interaction.

5.	Data sources/structures finalization

The following data sources/structures are critical:

Consolidated Programme and Qualification Mix

Academic Programme Topography;

Organizational structures;

Final 2003 HEMIS data submissions;

2005 Subsidy Sector data;

2003 and 2004 Financial Data Consolidation.

Document developed by –

Mr Thys Lourens and

Prof Peter van Eldik	Resource Allocation Model – Version 1.3/sv