ASSESSING THE VALUE AND FEASIBILITY OF ALTERNATIVE CANE SUPPLY SCHEDULING IN THE SEZELA MILL SUPPLY AREA

Transcription

1 SASEX ASSESSING THE VALUE AND FEASIBILITY OF ALTERNATIVE CANE SUPPLY SCHEDULING IN THE SEZELA MILL SUPPLY AREA Report to South African Sugar Association Experiment Station Mount Edgecombe South Africa Le Gal Pierre-Yves Calvinho Olivier Meyer Eddie Lyne Peter CIRAD/TERA num 4/4 February 24

2 ASSESSING THE VALUE AND FEASIBILITY OF ALTERNATIVE CANE SUPPLY SCHEDULING IN THE SEZELA MILL SUPPLY AREA Report to South African Sugar Association Experiment Station Mount Edgecombe Le Gal Pierre-Yves*, Calvinho Olivier**, Meyer Eddie***, Lyne Peter*** February 24 * CIRAD (France) and University of Natal (South Africa) ** Master Student, Institut National Agronomique Paris-Grignon (France) *** SASEX (South Africa)

3 CONTENTS Executive Summary.... BACKGROUND PROBLEM STATEMENT METHODS Conceptual background Data collection and analysis RESULTS Assessing the stability of quality curves At the mill area level Between Coastal and Inland zones Refining scenario basis Constant circumstances of the harvest season Harvest capacities Hauling capacities Mill capacity Simulating alternative supply scenarios DISCUSSION Field level Farm level Mill level Mill area level The way forward CONCLUSION Acknowledgements Bibliography ii

4 List of Tables Table : Annual LOMS and cane production in the Sezela mill area... 4 Table 2: Sample of interviewed growers according to the selection criteria... 9 Table 3: Sample of interviewed hauliers... Table 4: Average weighed %RV at the mill level (998-22)... 2 Table 5: Occurrence of RV peak according to zone and year... 4 Table 6: Values of cane production used in delivery scenarios... 4 Table 7: Distribution of deliveries at the beginning and end of the season (%DRD)... 7 Table 8: Ratio of potential DRD increase according to grower type and location... 2 Table 9: Potential ratio of capacity for grower/haulier... 2 Table : Hourly industrial capacities for fibre, brix and non-sucrose List of Figures Figure : %RV curves for three climatic zones (2 season)... 6 Figure 2: Conceptual framework of mill supply modelling... 8 Figure 3: Transported tonnage per haulier in Figure 4: Weighed weekly %RV for Coastal, Inland and mill areas... 2 Figure 5: Inter-annual coefficient of variation of weekly %RV (998-22)... 3 Figure 6: Difference in %RV between Inland and Coastal zones per year... 5 Figure 7: Distribution of annual cane tonnage according to production zone... 5 Figure 8: Total weekly delivery curves from 2 to Figure 9: Weekly delivery curves per zone (2)... 7 Figure : Relationship between current DRD and capacity increase ratio... 2 Figure : Delivery curves according to scenarios Figure 2: RV gains between alternative and reference scenarios according to year Figure 3: Delivery/crush capacity ratios according to scenarios and years Figure 4: Proportion of weeks with deliveries exceeding QD capacity Figure 5: Weekly deliveries/harvest-haulage capacity ratios according to scenarios and years List of Maps Map : Zoning of the Sezela mill area... 6 iii

5 ASSESSING THE VALUE AND FEASIBILITY OF ALTERNATIVE CANE SUPPLY SCHEDULING IN THE SEZELA MILL SUPPLY AREA Executive Summary In the South African sugar industry sugarcane is traditionally delivered to the various mills uniformly over the milling season and across all supply areas. This type of delivery schedule does not always exploit the different cane quality patterns that exist in certain mill supply areas. These regional differences in recoverable value of sugar patterns (RV) are primarily due to soil and climate differences and resulting differences in agronomic practices. A study was conducted from April to August 22 in the Sezela mill area to investigate the potential for improving mill area profitability by modifying cane supply and harvest scheduling to account for sub-region cane quality trends. Production and delivery data from mill weighbridge and cane quality databases was analysed for 2 and 2 to determine cane quality trends as well as the capacity and variability of cane deliveries through the season. This was done to investigate how sugar production could be maximised by changing the structure of cane deliveries from the fields to the mill, bearing in mind the existing harvest and transport systems as well as mill capacities (Guilleman et al., 22; Guilleman et al., 23). This first study showed that potential gains ranging from to 5% of the current annual sugar production might be achieved by re-arranging cane supply scheduling according to homogeneous quality-based sub-areas. In order to achieve these potential gains the required re-scheduling of cane deliveries would have to take cognizance of capacity constraints in the supply chain, including on-farm harvest and cane handling operations, cane transport and mill processing. Considering the results of the first study, the miller and grower representatives were interested in investigating more precisely the practicalities of implementing changes in cane supply delivery patterns. A second study was therefore conducted from April to August 23 in order to design and simulate realistic alternatives. This study analysed (i) the supply curve characteristics from 2 to 22, (ii) the relevance of the quality-based zoning according to climatic variability (998-22) and (iii) the available capacities along the supply chain (growers, hauliers and mill). New scenarios were then simulated and discussed with the growers and the miller. The methodology used for the second study was similar to the first study. Cane quality data for the 2 to 22 seasons was accessed directly from the mill database, while the 998 and 999 data were supplied by SASA. An analysis of grower, haulier and mill capacities was carried out using data captured by the weighbridge and mill records. Interviews were conducted using a sample of the stakeholders (growers, hauliers and miller) in order to assess both existing and spare cane harvesting, transport and crushing capacities. Once all this new information was assimilated new cane delivery scenarios were designed and simulated using the spreadsheet programme developed during the first study. The results showed that, at the mill area level, cane quality (measured in %RV) varies according to a bell-shaped curve during the season. However, each year has its own particular profile. The 998 and 999 seasons showed similar curves. On the other hand, the 2 and 2 seasons were low quality years characterized by low %RV and high variability. Nevertheless, cane quality was relatively stable between week 25 and 42, with inter-annual variability increases both at the beginning and at the end of the season. However, the %RV

6 peak occurred later in the Coastal zone, except for 22. Inter-annual variability of Coastal cane quality was higher when compared with the Inland zone. The instability of the Coastal quality may be due to the effects of Eldana saccharina infestation. Furthermore, the Inland cane quality was higher than the Coastal zone throughout the season. These results confirm the potential of scheduling harvesting operations according to quality-based sub-areas. An increase in sugar production may be expected by delaying harvesting in the Coastal zone, starting the season with Inland cane and ending earlier due to weather induced cane quality variations at the end of the season. The season would be completed with Coastal cane. In refining alternative cane delivery scenarios, cognizance must be taken of variability of total cane production, length of milling season, as well as weekly cane deliveries from one season to another. Furthermore, harvesting, infield transport, transloading and road haulage capacities and other issues such as labour availability and cash flow must be taken into account. Finally, assessing the mill capacity is critical when designing new cane supply scenarios with the ability to cater for cane quality variations. The results indicated that commercial growers could easily sustain a 5% increase in cane deliveries if harvest windows are shortened. However, there may be some growers who will experience temporary cash flow problems. On the other hand, the survey showed that grower/hauliers were capable of increasing their deliveries by as much as 3% on average. Considering the total crop, crush capacity and the quality dependent crush capacity, the mill capacity was pegged not to exceed 7 tons per week in a 38-week milling season when evaluating new scenarios. In total, five alternative cane supply scenarios were simulated. The results show that best RV gains are obtained when the Coastal zone deliveries are delayed by four weeks at the beginning of the season and the Inland zone cane deliveries stop nine weeks before the end of the season. However, this scenario is somewhat risky as it uses the crush capacity and is more sensitive to the cane quality variations. The next best RV gains were obtained where the Inland production area is increased together with a shorter Inland harvest window. Potential gains ranged from to 3%. The two studies conducted in 22 and 23 investigated mainly the value of modifying cane supply scheduling in the Sezela mill area. Both studies showed the potential profitability that could be reached by simulating various scenarios of harvest window reduction applied to a four-zone partition of the mill supply area. Stakeholders agreed at the end of the second study that this part was now completed, but new issues were raised for further investigation. It has been decided to investigate some of these issues in relation to the proposed changes in cane delivery patterns. The first research project will be conducted by a French student between March and October 24. The aim of the project is: To assess various cane payment systems in relation to re-arrangement of mill cane supply The second research project will be carried out as an MScEng project with supervision by SASEX researchers in collaboration with the University of KwaZulu-Natal and the Department of Transport. The aim of the project is: Improvement of transport logistics in areas where cane supply is optimised The two new studies are closely related to the two previous studies and will add value to any future changes to cane supply chain proposals, cane handling logistics and cane payment systems employed. 2

7 . Background The South African sugar industry consists of 5 mills operated by four companies located in Kwazulu-Natal and Mpumalanga. An average of 22 millions tons of cane is produced annually, mainly by 7 large-scale growers, who account for 72% of the production, as well as 2 miller-cum-planter estates (3%) and 5 registered small-scale growers (5%). The South African Sugar Association (SASA) embodies the partnership between millers and growers. SASA provides specialist services to the sugar industry and is responsible for distribution of the proceeds from sugar and molasses sales to cane growers and millers. Negotiations between millers and growers regarding the general organization of the industry, such as the cane payment system, are conducted under the SASA umbrella. Since its liberalization in 998, the South African sugar industry is facing a new economic environment. The national market remains protected by means of import tariffs and minimum prices, but over half of SA s total sugar production is exported at a price that fluctuates according to the global market. So the industry needs to improve its efficiency in order to remain competitive internationally, especially in a context of strong local currency and variable rainfalls. Profitability gains can be sought at different levels of the industry (cane production, sugar extraction and products marketing), which is the aim of most research programs conducted in sugarcane production (varieties, fertilisation, irrigation, harvest techniques), milling and refining technology (sugar extraction methods) and economy (market awareness). But there is also potential to improve profitability at the mill area level by looking at cane supply management. This process involves interactions between numerous stakeholders: farmers and harvesting contractors who manage harvest, hauliers who transport cane to the mill, and millers who organize supplies in order to regulate the mill operation. Therefore improving cane supply management requires a system analysis and specific methodology in order to provide useful information to both growers and millers (Muchow et al., 2). Various ways of improvement can be considered, ranging from changes in harvest and transport techniques to new rules of delivery allocation (Gaucher et al., 997). A first study was conducted from April to August 22 in the Sezela mill area to investigate how sugar production could be maximized by changing the structure of cane deliveries from the fields to the mill, bearing in mind the existing harvest and transport systems as well as mill capacities (Guilleman et al., 22; Guilleman et al., 23). This study showed that potential gains ranging from to 5% of the current annual sugar production might be achieved by rearranging cane supply scheduling according to homogeneous quality-based sub-areas. Considering these valuable results, the Sezela mill was keen on investigating more precisely the practicalities of implementing such scenarios. A second study was then conducted from April to August 23 to design and simulate realistic alternatives. It analysed (i) the supply curve shapes from 2 to 22, (ii) the relevance of the quality-based zoning according to climatic variability (998-22) and (iii) the available capacities along the chain (growers, hauliers and mill). New scenarios were then simulated and discussed with the growers and the miller (Calvinho, 23). 3

8 2. PROBLEM STATEMENT The Sezela mill is supplied with sugarcane by a large variety of growers including its millercum-planter estate (3%), 8 large-scale farmers (72%) and 5 small-scale farmers (%). The remaining 5%, called diversion cane, is supplied by two other Illovo mills (Eston and Umzimkulu) which operate at full capacity. Between 998 and 22, an average of 2.7 million tons of cane were crushed per year, with quite large variations from one year to another (Table ). Table : Annual LOMS and cane production in the Sezela mill area Average Starting week Closing week LOMS* Tons/annum Tons/week *Length Of Milling Season Depending on the year, the crushing season starts from mid-march to mid-april and ends before Christmas (999 and 22) or in January the following year. The mean weekly crushed tonnages range from 47 to tons. These performances show the flexibility of the mill capacity but are lower than the theoretical capacity (66 t/week, corresponding to 2 5 t over 38 weeks). Indeed, the mill capacity is adjusted according to specific circumstances (strikes in 997 during 5 weeks, high production level in 2 and 22), while the LOMS remains above 37 weeks. This value is considered as economically optimal (Moor and Wyne, 2) and is contractually agreed by both miller and growers. It enables the mill to operate during a minimum season duration in order to secure the bagasse supply of its downstream plant where co-products such as furfurol are produced. The mill supply management involves up to four operators : growers who grow their cane, often cutting and sometimes transporting it as well (called grower/haulier: grw/hlr in this study), harvest contractors who cut and load the cane on behalf of the growers (usual arrangement for the small scale growers), hauliers dedicated to cane transport (called haulier/haulier: hlr/hlr), and the mill. In 22, 35 hauliers were operational in the Sezela area; 22 hauled more than, t/year (7 hlr/hlr and 5 grw/hlr). The Mill Group Board (MGB) is responsible for the season planning and the cane supply coordination between these operators. Before the season starts growers estimate their production and they receive in return a DRD (Daily Rateable Delivery) from the MGB. The calculation takes into account the total cane production for the mill area, the mill capacity and the planned LOMS. DRDs are uniform throughout the season and throughout the mill area. Monthly and daily adjustments are made during the season in case of unforeseen events or better estimation of the remaining production. The mill manages its supply directly with the hauliers rather than See Guilleman et al. (22) for details regarding the way cane supply is managed from the growers to the mill. 4

9 the growers. Each haulier gets a DRD which aggregates his growers DRDs, and has to coordinate his daily supply in order to fulfil it. Cane quality is a crucial parameter for the cane supply chain as it impacts on sugar production and stakeholders profitability. From the miller s point of view, cane quality refers to sucrose content and the possibility of extracting this sucrose. The higher the fibre and non-sucrose contents, the more difficult sucrose extraction becomes. High fibre will retain sucrose during the diffusion process, while high non-sucrose will prevent an efficient crystallisation. The sugar industry has defined an indicator to assess the potential amount of sugar extracted from cane and to link the farmers payment to the quality of cane they deliver. The Recoverable Value (%RV) rate is derived from the Estimated Recoverable Crystal (ERC) formula (Murray, 22; Moor, 22), and includes the sucrose, non-sucrose and fibre contents of the cane. The %RV measured in cane consignments by the Cane Testing Service (CTS) is dependent on many factors, including climate, production techniques and harvest/transport conditions. Variations of these factors within a mill supply area might result in sub-regional patterns in sugarcane production and RV distribution. As the current system is based on uniform delivery allocation across all regions throughout the milling season, it might be profitable to investigate alternative supply systems based on these variations of quality in time and space. The Sezela mill was chosen for this study because of (i) its potential diversity of cane quality within its supply area, (ii) interest in the potential of the approach at Sezela and (iii) the availability of data captured at the weighbridge since 2 regarding production and cane quality. The study conducted in 22 showed that: the mill area could be split into five sub-areas. Three of them are based on variations of quality curve (Inland, Hinterland and Coastal; Figure and Map ). In that case it was assumed that climatic factors were critical to explain that variability within the mill area. The small scale growers were aggregated in a fourth zone, to take into account their specific harvest decision-making processes. Diversion cane was grouped in a fifth zone as the mill has no real capacity to control this cane. gains of between to 5% of total tonnage RV (around R million) could be obtained by differently allocating DRD to the various zones. The main idea was to take advantage of the better quality of Inland and Hinterland zones at the beginning of the season by giving them supply priority at that stage, while the Coastal zone harvest was delayed. Scenarios were simulated with a spreadsheet application to calculate and compare RV production, while the LOMS, harvest windows and DRD per zone varied. These results were reported back to both growers and miller, who asked for further investigations to (i) assess the inter-annual stability of quality variations between sub-areas and (ii) to design supply scenarios which better take into account the circumstances of the Sezela mill area, particularly the capacities along the chain. This second study was conducted in 23 in that respect. 5

10 RV % 5% 4% 3% 2% % % 9% 8% 7% 6% 5% weeks Coastal Hinterland Inland Figure : %RV curves for three climatic zones (2 season) Map : Zoning of the Sezela mill area 6

11 3. METHODS 3. Conceptual background The methodology used for this second study is similar to that used for the former study 2. The objective is to provide both miller and growers with information regarding the impact of new modes of cane supply management on the profitability of the mill area, as well as the functioning of the supply chain from the fields to the mill. This objective is achieved by using simulation models which provide a simplified representation of the supply chain and enable the user to quickly compare a large range of scenarios by changing some of the parameters included in the model. Manipulating these components and results will enhance stakeholders common knowledge and capacity to find agreed solutions (Hatchuel and Molet, 986). Two types of modelling techniques are found in the literature to address changes in delivery allocation rules. An Australian team has developed an optimisation model at the mill area level that maximizes the sugar yield and net revenue of the chain by defining suitable harvest dates and crop cycle length for a range of production units (Higgins, 999; Higgins et al., 998; Higgins and Muchow, 23). Through participation by the whole industry, this model includes cultivation, harvest, transport and transformation costs. It provides an optimal solution for harvest scheduling, given the structure and capacity of the supply chain. This model seems well suited when the mill and its suppliers are closely integrated, as is the case in Australia. It would be difficult to apply in the South African context because of the relative stakeholders autonomy and the large diversity of the grower population within a mill supply area. The simulation approach developed in La Réunion (Gaucher, 22) seems more suitable for the SA scenario, as it enables the assessment of alternative supply scenarios based on a simplified representation of the various operators in the supply chain, with their constraints and relationships. Changes in structure and capacities are simulated, and their consequences in terms of sugar production quantified at the mill area level. Balance between delivery performances and mill crushing capacity can be investigated and discussed according to various hypotheses of supply chain structure and planning/operating rules (Gaucher et al., 23). The model is made up of two sub-modules, the first for supply planning and operating and the second for cane processing (Figure 2). It is underlined by a three-level representation of the supply area, including mill, intermediate operators (IO: transloading centres, hauliers) and production units (PUs). It simulates a crushing season on a weekly step basis, according to the cane flow paths between these three levels, their given characteristics (particularly their capacities) and some planning and operating rules. Based on this conceptual model a computer program called MAGI was developed (Le Gal et al., 23). As the software was not operational for this study, the spreadsheet application developed in 22 was used again in 23. MAGI will be available in 24 as a generic tool adapted for both the La Réunion and South African sugar industries. 2 See Guilleman et al. (22) for more details. 7

12 Agro-industrial structure Name and location of Production Units (PU) Type and name of Intermediate Operators (IO) Links between PU, IO and the mill (structure of cane flows) PU Characteristics: Cane area Cane yield Harvest capacities IO Charactersitcs: Transfer capacities Mill Characteristics: Hourly crushing capacity Daily work duration Working days per week Maintenance duration Breakdown rate Step () Length of Milling Season Opening and closing dates Delivery scheduling Planning and operation rules Delivery allocation Latest closing date Hazard management rules Weekly delivery / PU Step (2) Weekly quality curve / PU Weekly mill process efficiency Amount of sugar produced weekly and annually Figure 2: Conceptual framework of mill supply modelling 3.2 Data collection and analysis Our approach was directed at three objectives: (i) assessing the stability of the quality differential between zones over a larger range of years, (ii) analysing supply curves and capacities along the chain to refine the scenario design and (iii) designing and comparing new scenarios. Following the miller and growers suggestion, it was decided to merge the Hinterland and Inland zones, as their quality curves were very similar (see Figure ). The first objective was addressed by analysing the cane quality data (sucrose, fibre, nonsucrose, RV) captured at the weighbridge for 6% of all cane consignments, from 998 to 22. Data collected between 2 and 22 were processed directly from the mill database, while 998 and 999 data were supplied by SASA as they were not archived by the mill. The mill database provided the daily tonnage delivered during the 2, 2 and 22 seasons as well. 8

13 The analysis of both grower, haulier and mill capacities was carried out (i) by processing the delivery data included in the database (growers daily deliveries, truck payloads, mill crushing tonnage) and (ii) by interviewing a sample of stakeholders to assess existing extra capacities that could be used in cases where harvest windows are reduced. These samples were selected as follows: Growers Twenty-five large scale growers uniformly split over the two zones were interviewed (Table 2). The selection was based on (i) the total annual tonnage, (ii) the variability of weekly deliveries during the season (estimated with the coefficient of variation for each grower), (iii) the farm location, (iv) the amount of delivery days during the season, and (v) the farm age (growers who purchased their farms since 999). It was assumed that these criteria would be linked with the farm flexibility regarding harvest capacities. The final selection took into account farm accessibility as well, which explains why some cases are not represented in the sample. Table 2: Sample of interviewed growers according to the selection criteria Total tonnage (22) <25 t 25-5 t 5-5 t 5-9 t 36 t Total Coefficient of variation of weekly deliveries (22) -2% 2-5% 5-8% Total Coastal Inland Coastal Inland Coastal Inland 2 2 Coastal Inland Coastal Inland Coastal Inland Coastal Inland Coastal Inland Coastal Inland Coastal 2 a dont b Inland a a Coastal Inland 3 dont a 3 dont b Coastal Inland 3 Coastal 4 dont b Inland c 8 9 Coastal Inland a + a,b Coastal Inland Coastal a Inland 2 3 Coastal Inland Coastal Inland a growers delivering on less than 3 days in 22 b growers who purchased their farm since 999 c grower owning large areas both in Coastal and Inland Numbers in bold = number of growers selected The following items were addressed: total area, cane area, other activities (potentially competing with cane production for work and equipment), harvest and transport equipment and techniques, current DRD, maximum DRD achievable with the current harvest system (without extra equipment), impacts of a shorter cutting season on cane yields, labour availability and cash flow. The small scale growers (annual production less than 2 t) were not directly interviewed, as they are very numerous and their contribution does not weigh much in the total supply to the mill. Moreover, their harvest is carried out mainly by small scale contractors. Instead, one staff member from the mill division in charge of providing them with technical support was 9

14 interviewed to estimate their constraints and potentialities regarding harvest management (see also Le Gal and Requis (22), for a study conducted in the Amatikulu mill area). Hauliers Only hauliers transporting a significant annual tonnage of cane were interviewed, as it was assumed that they should be more flexible, able to provide extra capacities and to react to grower requests if necessary. Twenty-two hauliers out of 33 transporting cane in 22 in the Sezela area were in that category as they exceeded tons hauled during the season (Figure 3). Tons hauled by haulier per zone in Tons cane SSG 4 Inland 3 Coastal Diversion Contractor code Arrows indicate hlr/hlr. Haulier 36 transports diversion cane only. Figure 3: Transported tonnage per haulier in 22 Fifteen hauliers were then selected according to their business area (Figure 3) and their type (hlr/hlr versus gwr/hlr), as it was assumed that extra capacities as well as geographical flexibility were linked with these two criteria (Table 3). The following items were addressed: equipment, other hauling activities, current DRD, potential maximum DRD achievable without investment in new equipment, and truck management rules in case of DRD increase. Table 3: Sample of interviewed hauliers Type Coastal Inland Grw/hlr 7 out of 8 4 out of 6 Hlr/hlr 4 out of 6 Mill The factors impacting on mill capacity were investigated by interviewing some staff members from the process division. The objective was to estimate capacity flexibility, taking into

15 account both the cane quality crushed during the season, the cane supply management and constraints such as diversion cane. Once all the quantitative and qualitative information was recorded and processed, new scenarios were designed and simulated using the spreadsheet programme. Their comparison against a reference scenario representing the current system was carried out (i) between years for a specific scenario and (ii) between scenarios for a particular year. Results were presented to and discussed with miller and grower representatives during two meetings. 4. RESULTS 4. Assessing the stability of quality curves Cane quality is partly dependent on climatic circumstances, particularly temperature and water balance (Fauconnier, 99; Singels and Bezuidenhout, 22). So quality curves may vary from one area to another and from one year to another. As alternative delivery scenarios were based on quality curve differences between Coastal and Inland zones, especially at the beginning of the season, it was important to evaluate the stability of these differences over a larger range of years, i.e. from 998 to At the mill area level At the mill area level cane quality (measured in %RV) varies according to a bell-shape curve during the season (Figure 4). However, each year shows a particular profile regarding the average %RV (Table 4), the quality variability from one week to another, the maximum RV peak and its symmetry around this peak. The 998 and 999 seasons show quite similar curves. Their mean %RV is above the average, with low variation throughout the season. The curves are asymmetric, with a peak around the week 32 (which can be considered as the usual peak week in Sezela). The second half of the season shows better but more irregular quality than the first half. 2 and 2 are low quality years characterized by a low %RV and high variability, particularly in 2. 2 shows a very asymmetric curve shape, with a slow start, a late peak (week 36) and a much better finish, while 2 is almost symmetric. 22 is atypical: its mean %RV is very high and very regular; the curve reaches an early peak and is almost flat throughout the season. Nevertheless, a general trend of variation appears from one year to another. Cane quality is stable from week 25 to week 42, with inter-annual variability increases both at the beginning and the end of the season (Figure 5). Deliveries should thus be concentrated during the middle of the season while quality is also maximum. However, this option is subject to capacities along the chain and a 37-week LOMS. A compromise is to vary the supply between Inland and Coastal throughout the season.

16 %RV Calendar week Inland Coastal Mill area %RV Calendar week Inland Coastal Mill area %RV Calendar week Inland Coastal Mill area %RV Calendar week Inland Coastal Mill area %RV Calendar week Inland Coastal Mill area Figure 4: Weighted weekly %RV for Coastal, Inland and mill areas (998-22) Table 4: Average weighted %RV at the mill level (998-22) Year Total Average %RV CV (%)

17 25 2 Coefficient of variation (%) Calendar week Mill area Inland Coastal Figure 5: Inter-annual coefficient of variation of weekly %RV (998-22) 4..2 Between Coastal and Inland zones The scenarios simulated in 22 are based on the main assumption that Inland cane shows better quality than Coastal cane at the beginning of the season. 2 and 2 data were used to validate this hypothesis, which needs to be justified over a longer period because of the inter-annual variability of quality curves. This analysis takes into account (i) the curve shape throughout the season, (ii) the peak weeks for each zone and year, (iii) the inter-annual stability of weekly %RV for each zone and (iv) the inter-annual stability of the quality variation between the two zones. In general Inland and Coastal quality curves follow the same profile as the mill area zone (Figure 4), but: The %RV peak occurs later in Coastal, except in 22 (Table 5); Inter-annual variability of Coastal quality is higher than the Inland zone at the beginning of the season. This explains the quality uncertainty at the mill area level at that stage, as 6% of the total cane production comes from the Coastal zone. The factors explaining this instability within the Coastal zone have not been clearly identified. It could be due to (i) a higher variability of individual quality curves, which would require defining more accurate sub-areas, (ii) a higher inter-annual climatic variability (temperature and rainfall) or (iii) the impact of Eldana saccharina infestation. This insect is found mainly in Coastal areas because of appropriate climatic conditions (Goebel and Way, 23). Its damage increases in carryover cane, which is harvested around 4-6 months in Coastal areas because of the mill closure in summer. On average, Inland quality is higher than Coastal throughout the season. The gap varies by around % during the first half of the season and decreases once the RV peak is reached (week 32) (Figure 6). This mean profile varies from one year to another, essentially 3

18 according to the gap value at the beginning of the season: high to very high in 999, 2 and 2, smaller in 998 and 22. These results confirm the potentiality of scheduling harvest according to quality-based subareas. An increase in sugar production may be expected by delaying harvest of Coastal cane and starting with Inland cane. Then (i) the impact of the uncertain Coastal cane quality would be reduced, (ii) the entire mill supply area would take advantage of the better Inland cane quality at the beginning of the season and (iii) Coastal cane would be harvested closer to its RV peak. But these alternative delivery schedules can be implemented only if extra capacities exist along the supply chain, as they lead to reduced harvest duration for each zone. Table 5: Occurrence of RV peak according to zone and year Coastal Inland Refining scenario basis After the exploratory investigations conducted in 22 the stakeholders demand focused on the practicalities of implementing a supply system based on quality-based delivery allocation. Two issues were addressed in 23 in this respect: (i) What are the values and constraints to be considered regarding the constant circumstances of the harvest season at the mill area level? (ii) What are the potentialities in term of extra capacities along the supply chain? 4.2. Constant circumstances of the harvest season Three kinds of circumstances were considered as constant for every scenario: total annual cane production and distribution between zones, season duration and timing, and profile of delivery curves. These parameters were specified by analysing delivery data between 998 and 22 and discussing it with the miller and grower representatives. Cane production Total cane production is quite variable from one year to another at Sezela, and consequently the season duration is variable as well (Table ). For simplification reasons it was decided to keep a value close to the average for the period. So the LOMS could also remain constant for every scenario (see below). As the distribution of production between the four zones included in the scenarios (Coastal, Inland, SSG and Diversion) has been stable from 2 and 22 (Figure 7), the following production values were considered for every scenario simulated in 23 (Table 6). Table 6: Values of cane production used in delivery scenarios Zone Diversion Coastal Inland SSG Total Tons of cane %

19 %RV %RV Calendar week Calendar week %RV %RV Calendar week Calendar week %RV %RV Average (common years) Calendar week Calendar week Figure 6: Difference in %RV between Inland and Coastal zones per year Diversion 4% SSG 2% Diversion 8% SSG % Diversion 6% SSG % Inland 22% Coastal 62% Inland 23% Coastal 58% Inland 24% Coastal 6% Figure 7: Distribution of annual cane tonnage according to production zone 5

20 LOMS and season timing It was decided to keep the LOMS at its agreed length (38 weeks) and to stop the mill before Christmas, although the 22 simulations showed valuable RV gains when the mill closure was delayed until after Christmas. Indeed, the delivery curve is better synchronized with the maximum of the quality curve in that case. Both miller and growers considered that the constraints created by such delay were greater than the expected RV gains. Summer rainfall and higher temperatures make harvest conditions more difficult and hazardous. The cutters, most of whom come from the Eastern Cape, go back to their communities for Christmas and are reluctant to come back afterwards, especially if they have to work in warm conditions. Moreover, the cane cut during that period takes less advantage of the favourable growing conditions prevailing from January to March. Delivery curves Scenarios simulated in 22 considered that deliveries were up to DRDs from the very first week of the season. This assumption was checked by analysing the real delivery curves from 2 to 22. Moving averages over four weeks were considered 3 instead of weekly deliveries in order to better underline the curve trend. Total weekly delivery curves show a similar pattern from 2 to 22 (Figure 8). Deliveries increase gradually during the first 6-8 weeks of the season, then they reach a plateau that they follow roughly until the last 3-5 weeks. This general profile is also observed for the four delivery zones (Figure 9). This means that (i) growers fulfil their DRD during most of the season (coefficients of variation vary from 5 to 3% according to the year and the impact of unforeseen events), (ii) they take around three weeks to reach their DRD at the beginning of the season and (ii) they gradually stop their deliveries over the last two weeks. Based on these observations, the values in Table 7 were used in the 23 scenarios Harvest capacities Harvest capacities are determined at the farm level when growers manage the cutting, loading and hauling tasks themselves from the fields to the loading zones. At Sezela this organization concerns mainly large farms producing over 7 tons per season. Smaller commercial farms and small scale growers usually contract the task to harvesting contractors, who are generally growers as well. Commercial harvesting contractors manage their own DRD plus the growers DRD they contract with 4. Five contractors who manage DRDs ranging from 2 to 36 tons were interviewed during the study. 3 The moving average for week i is equal to the average of weeks i-3 to week i. 4 Small scale contractors harvest SSG fields on behalf of section committees. They haul cane only to the loading zone and do not supply directly the mill. 6

21 tons cane Calendar week Figure 8: Total weekly delivery curves from 2 to Tons cane Calendar week Diversion Coastal Inland 3 SSG 4 Figure 9: Weekly delivery curves per zone (2) Table 7: Distribution of deliveries at the beginning and end of the season (%DRD) Week Week 2 Week 3 Week n- Last week 2% 5% 75% 5% 2% 7

22 A large variety of harvesting systems can be found in the Sezela mill area. Cane is generally burnt, then cut by hand. Cutting green cane is more time consuming, but is expanding for environmental reasons. Each grower matches the size of his cutter team to his DRD and his cutting technique. For instance, one of them employs 25 cutters for a 9 ton DRD. Cutter efficiency is highly variable according to the individual and cutting conditions. Permanent staff are usually not involved in the cutting task. Cutting, loading and transport are intrinsically linked and selected according to various factors: the kind of payment system used (weight or rope), the field accessibility for machinery (slope essentially), the distance between fields and loading zones, the ratio between three capacities: cutting, loading and transport to the loading zone and loading into 3 ton trucks (hilos). Two main systems were pinpointed during the survey: Cutters lay cane sticks in windrows which are then loaded by a 3-wheel grab loader into a trailer. This loose cane is then brought to the loading zone where it is transloaded to a hilo with a 3-wheel loader. In this case, cutter payment is based on the length of cane rows cut. Cutters collect cane into stacks that are chained and loaded onto specialized trailers. These bundles are hauled to the loading zone where they are unloaded and weighed with a crane. The crane then loads the bundles into hilos and the chains are brought back to the fields. In this case the cutter is paid according to the weight of the bundle he cut. Investigations focused on the factors impacting on capacity increase in case of reduced harvest windows, i.e. labour availability, loading and transport equipment and work organization. Each grower was then asked to estimate his own ratio of capacity increase (potential DRD / current DRD). Labour availability Of the growers interviewed, 95% use their cutters for 8 h/day, which is considered the normal working time. To increase cutting capacity on a regular basis means employing extra cutters. Growers face two constraints in that respect: Cash flow: The mill pays the growers at the end of each month following the deliveries. So growers need to carefully plan their cash flow at the beginning of the season, as they are paid only two months after the first deliveries. This planning process will be even more crucial when increasing the number of cutters. Cutter availability: It could be difficult to find extra cutters if harvest windows are reduced, as each cutter would get less money for the season. This problem could be solved on the farms where both cane and timber are grown, by cutters starting with the cane harvest and switching to timber during the year. Loading and transport machinery Of the growers interviewed, 95% declared that they could increase their DRD with their current equipment, either because it is under-used (less than 2 h/day) or it is not used at all. For instance, one farm owns two tractors used 5-6 h/day, two 3-wheel loaders used 2 h/day and one extra loader. This equipment was bought when the sugar industry economy was 8

23 favourable for the growers. These extra capacities give them more flexibility while facing unforeseen events and sudden DRD increases. Farms combining cane and timber production may also increase their DRDs by transferring equipment from one activity to the other, as the machinery is similar in both cases except for the trailers. This possibility is mainly found in the Inland zone, where 8 growers out of manage a cane-timber farming system. Work organization Growers are split by the mill into three groups, depending on their delivery time slot: 2 h deliveries (6-8 h, 8-24 h and 24h deliveries) (6-6). This organization aims at regulating the cane deliveries at the millyard throughout the day. Only one grower in our sample would be ready to switch from a 2 h slot to a 24 h slot. This kind of switch would need to be carefully planned at the mill area level to keep the regularity of the truck flow. Growers include in their work organization some safety margins in case of a sudden increase in DRD, either because of a badly controlled fire or a break in harvest (for whatever reason) which needs to be caught up on in the future. These stops may be due to local events (rainfall, machinery breakdown, cutter strike) or affect the entire mill supply area (mill breakdown). Growers may also try to take advantage of the open DRDs that the mill sometimes offers (five days in 2, but none in 22). Growers use various ways of increasing their DRD: delivering on Sundays; using mutual aid from neighbours who divert their cutters and machinery for a short period and are compensated later on; transferring capacities from timber harvest in the case of a combined farming system; using permanent staff to complement the hired workers. Any structural increase in DRD would have to integrate these safety margins which enable harvest systems to react to unforeseen events. Potential increase in harvest capacities The potential capacity of the growers interviewed could be almost doubled compared with current DRDs (Table 8). Values are quite similar once an outlier value is excluded. The ratio variation is not explained by the total delivered tonnage (Figure ), as extra capacities are generally available over 7 tons of cane. Nevertheless the ratio increases with smaller DRDs, i.e. a grower may need to purchase a second vehicle to meet his DRD, but may not be able to fully utilise the second vehicle. Mean DRD increase ratios are equivalent for growers and harvesting contractors. The Inland ratio is higher than the Coastal, probably because of (i) the transfer of timber equipment to cane harvest and (ii) structural extra capacities required to face more hazardous climatic circumstances. These values were used to evaluate the feasibility of scenarios where harvest windows were reduced. 9

24 Table 8: Ratio of potential DRD increase according to grower type and location General Inland Coastal Growers Harvesting contractors Average CV (%) Note: An outlier value of 4. was excluded from the calculations 3 Increase ratio 2,5 2, current DRD (t/day) Figure : Relationship between current DRD and capacity increase ratio Flexibility of harvest capacity was considered only for commercial growers. Small scale growers depend on small scale harvest contractors, whose technical efficiency is quite unreliable (Le Gal and Requis, 22). Based on this observation it did not seem realistic to increase their current DRDs, so the delivery curve of the SSG zone remained similar whatever the scenario. A similar decision was made for the diversion cane. It comes from Eston growers who cannot deliver to their mill because of its crushing capacity saturation. Diverted tonnage is estimated before the season starts, assuming that cane will be delivered on a 24 h/day, 7 days/week basis. Sezela mill uses this cane to fulfil its crushing capacity, but cannot really manage the scheduling Hauling capacities The 35 hauliers operating in the Sezela area show a large diversity of hauled tonnage, from 6 to 49 tons during the 22 season (Figure 3). Only 22 of the 35 hauled more than tons. Seven of them are transport companies (hlr/hlr), while 5 are growers hauling their own cane and eventually cane produced by to 5 neighbours (grw/hlr). 2

25 Extra hauling capacities are based on under-used vehicles, capacity transfer from timber to cane production (only trucks as trailers are specific to each product), mutual aid in case of sudden DRD increase or investment in new equipment. The latter concerns essentially hlr/hlr. The grw/hlr group is characterized by a low geographical flexibility and regular relationship with their customers. They show significant extra capacities (Table 9), mainly because of (i) safety margins required to deal with DRD variations, and (ii) threshold effects regarding the ratio between transport capacities and DRDs. These extra capacities are greater for Inland grw/hlr, as they were for the harvesting operations, and probably for the same reasons. By comparison hlr/hlr have a greater geographical flexibility, except for one case attached to Inland (Figure 3). They have a market-oriented strategy which makes them invest in new equipment when the transport demand increases. But they seem to maintain their vehicle fleet size even if they lose market shares. Their extra capacity ratios are quite low (average.25) with their current fleet size, but could easily be increased by purchasing new vehicles. The hlr/hlr flexibility regarding both capacity and customers reduce constraints on cane transport in case of harvest window reduction. Nevertheless, an increase in vehicles could impact on delays at the millyard and reduce transport efficiency. In any case, logistics issues should be addressed in order to optimise the relationship between the tonnage to be hauled, the fleet size and the selected supply organization at the mill area level. Table 9: Potential ratio of capacity increase for grower/haulier General Inland Coastal n 4 6 Average CV (%) Mill capacity Assessing mill capacity correctly is of course critical when designing new supply scenarios. The model takes into account weekly capacity, as the purpose of this study is to focus on strategic changes rather than daily operation. This weekly capacity is calculated as follows: CW i = CH i x D where i = week number CW i = weekly capacity (t/wk) CH i = hourly capacity (t/h) D = weekly duration of operation (h/wk) Hourly capacity The milling process follows a number of stages in order to produce raw sugar from cane, i.e. crushing, diffusion, clarification, evaporation, crystallisation and centrifugation. Its efficiency evaluation is based on three ratios: 2