SUGARCANE HARVEST AND TRANSPORT MANAGEMENT: A PROVEN WHOLE-OF-SYSTEMS APPROACH THAT DELIVERS LEAST COST AND MAXIMUM PRODUCTIVITY By G DINES 1, S M c RAE 2, C HENDERSON 2 1 NSW Sugar Milling Cooperative Ltd, NSW 2 Agtrix Pty Ltd, NSW chrish@agtrix.com KEYWORDS: Cane Transport, GPS Tracking, Cane Supply, Cane Bin, Harvest Management, Cane Road Transport. Abstract NSW SUGAR MILLING, A cooperative sugarcane processing group in Northern NSW, Australia, harvests and transports up to 2.5 Mt of sugarcane each year to its three factories (Harwood, Broadwater and Condong) using only 21 harvesters and 28 trucks in total. Cane is delivered to each factory every six minutes to maintain maximum processing capacity. There is little or no queuing of trucks at the mill, and few interruptions from the field side operations. This paper describes the progress made since the technology and processes were last reported in 1999. Traditionally a significant proportion of sugarcane processing costs are invested in harvesting and transportation. By contrast, the current system s operational costs are very low. This paper describes how high levels of operational automation, a reliable system-wide communications system and comprehensive visualisation of real time activity allow a small number of staff to successfully manage a very effective just in time system. This innovative system represents a significant change to traditional approaches in harvest and transport management and illustrates a proven path using a whole-of-systems approach for other processors to follow. Introduction The harvesting and transportation operations for a sugarcane processing factory are an extremely dynamic space. Small margins are made on processing large volumes of raw materials, in an environment characterised by unplanned events including adverse weather and machinery breakdown. No harvest plan survives first contact with the crop in its environment. Flexible, rugged, reliable and responsive systems are required to manage receivals of sugarcane in volumes satisfying optimum crushing capacity on a continuous basis. In many ways management of the sugarcane supply chain is as much art as it is planning and procedures. However, in its innovative adaptation of technology over the last 15 years, NSW Sugar Milling is demonstrating world s best practice, simplifying use of resources and reducing use of capital, while reliably collecting and delivering cane to its factories in a just in time operation. Continuous innovation has substantially improved the efficiency of harvesting and transport operations since systems were last reported (Dines et al., 1999). Increasingly automated systems and improved communication and visualisation technologies mean fewer human resources are required to monitor real time operations. Staff can monitor operations from any location using mobile technology while real-time feedback to field operators enables significant productivity improvements. Systems are being increasingly centralised across the three factories improving 1
flexibility and providing management with access to strategic data in real-time, assisting them in improved decision making. Some current business risks for NSW Sugar Milling include: greater difficulties in sourcing experienced, well trained staff volatile global sugar prices volatile currency exchange rates fuel energy costs impacts of climate change including costs of emission offsets (vehicle fuel for example) and increased weather variability. Improving harvesting and transportation systems therefore contributes to reduced business risk by lowering capital and operational costs, reducing the need for highly trained numbers of human resources, reduces emissions by reducing the number of trucks, haul-outs, harvesters and other vehicles, and by optimising routes and minimising empty runs. Improving the reliability of cane supply, thereby minimising process stops due to the lack of sugarcane, improves smooth operation of the process leading to fewer maintenance problems and reduced costs. Commercial confidentiality restrictions, the variable take up of whole crop harvesting, significant weather events and impacts involving the commissioning of co-generation facilities (at two of the three mills) prevents exposure of data after 2006, except in a limited way. Financial impacts 1 The harvesting and transportation of sugarcane to a sugar factory represents a significant proportion of costs in the processing and manufacture of sugar, accounting for around 1/3 of all onfarm costs (Hassuani et al., 2005; Salassi and Barker, 2008). Reducing costs in harvesting and transport is therefore a significant business driver for NSW Sugar Milling. Current harvesting costs amount to $9.89 per tonne cane in traditional sugarcane factories and $8.25 in new factories in Brazil (Hassuani et al., 2005). Average harvesting costs across four regions (10 factories) in Australia in 2000 were reported as $6.04 per tonne sugarcane (Higgins and Muchow, 2003), and $9.01 per tonne sugarcane in 2010 in a central region harvest area (Oliveira and Balieiro, 2010). Harvesting costs in 2006 in Louisiana, USA have been reported at $6.45 per tonne cane (Salassi and Barker, 2008), while harvesting costs in South Africa in 2000 were reported between $2.80 and $3.81 per tonne sugarcane (Meyer et al., 2000). In contrast, the indicative per tonne cane harvesting costs in 2006 in NSW were $5.63. Sugarcane transportation costs per tonne sugarcane are reported as $3.43 in traditional factories and $3.05 in Brazil in 2011 (Xavier, personal communication, 2011). Transport costs at Sezela mill in South Africa in 2006 were reported as $4.47 per tonne sugarcane (Giles, 2006). Productivity indicators Productivity data for harvesting and transport operations in season 2006 are represented below in Table 1. Harvesting groups on average used a single harvester and three haul-outs with four operators. Harvesting groups were reduced from 28 in 2006 to 22 by 2010. A just in time 23 tonne cane delivery arrives at each factory, on average, every six minutes. 1 Real $1 = $0.5441 AUD, USD $1 = $1 AUD, ZAR R1 = $0.17 AUD 2
Table 1 Productivity data for harvest season 2006. 2006 Harwood Broadwater Condong Totals/Averages Crop (tonnes harvested sugarcane) 823 535 1 133 968 676 671 2 643 174 Harvesters 8 12 8 28 Harvest tonnes sugarcane per harvester 104 067 94 497 84 584 94 383 No. of prime movers 11 15 8 33 Total haulage per truck 77 087 75 598 90 223 80 969 Factory crush rate per day (tcd) 4110 4881 3288 12 280 Average no. of deliveries of cane per hour 9 12 8 10 Average no. of deliveries per truck per day 17 14 19 17 Impact on factory operations by problems originating from the cane supply and transport operations was minor in 2006, averaging 2.1% of all stops (Table 2). Presented another way, it is six minutes 35 seconds of stops on average each day at each factory in the group. Table 2 Source of mill stops various sources in 2006 as a percentage of all stops. 2006 Harwoodd Broadwater Condong Average Cane supply originated mill stops 3.1 2.2 0.8 2.1 Weather originated mill stops 61.9 56.4 47.7 55.3 Internal originated mill stops 35.0 41.4 51.5 42.6 Totals 100.0 100.0 100.0 100.0 Cane harvesting A typical harvesting group operates one, sometimes two harvesters, and three or four sixtonne haul-outs to remove harvested cane from the harvester to the local trans-loading pad. Haul outs are gradually being converted to carry 10 tonne loads. While the harvester driver is usually the group supervisor, one of the haul-outs will have a 7 or 10 inch ruggedised touch screen installed (Figure 1). This haul-out driver electronically consigns full bins at each trans-loading pad (Figure 2), via telemetry, to the cane receivals information system. The cane receivals information system passes the need for a trip to the trip scheduling application FREDD for a pickup to be scheduled. The harvester and haul-out drivers communicate by radio. Haul-out drivers who fill bins, and who do not have touch screens, advise their harvester group colleague by radio who consigns for them. Each consignment identifies bin number and pad location, cane ownership and block information, and other attributes including green/burnt cane, but is actioned by simply entering the bin number. Information connected to the bin has been pre-entered already. At the start of every day farm numbers, block numbers, harvester groups and pad identification are recorded using the touch screen. Thereafter, this information is automatically attached to each bin consigned. The harvester GPS continually indicates its location, recording the identity of the block currently being harvested, and auto-filling the consignment form, ensuring consignment data is updated dynamically and accurately and simplifying the work of the haul-out driver. 3
Fig. 1 Touch screen and software screens in the haul-out cab. It is essential that the consignment information reach the cane receivals system in a timely manner, as this determines when a trip will be scheduled for pickup. To improve the reliability of the consignment system, as the coverage and uptime of the Australian NextG network has improved, the communications between the client and server have been adapted to work from radiobased telemetry to the mobile network. All harvesters are tracked using GPS integrated into an AgDat remote touch screen in the cab, which records and transmits the tracks of the harvester as it moves up and down the block. Integrated data loggers record machine productivity information and ensure accurate tracking of harvester operations when actually cutting cane. Fig. 2 Photographs showing trans-loading operations: harvesting, cane transfer from haul-out to bin and bin pickup. A harvest status management and monitoring application CHOMP receives harvester tracking and cutting information via the cane receivals system and AgDat interpolator. Crop estimates and block status information including cut, fallow, standover, and crop available for each block is visualised and reported, assisting cane supply officers to provide accurate and reliable estimates of crop remaining to be cut, and where it is. Information regarding block level crop age and variety is also provided. Harvesting groups earn an incentive when haul-out drivers fill each bin with between 21 and 23 tonnes cane net, the target bin weight. Penalties occur for bins loaded below 20 tonnes and above 23 tonnes cane net. No incentive is provided for bins filled between 20 and 21 tonnes cane net. Telemetry also feeds information about factory operations, including current crush rate, back to each harvesting group in real time, via a web based application called SHIRT (Supply and Harvester Information In Real Time). SHIRT was introduced progressively in 2007 to replace the previous radio-based telemetry system (Rose et al., 2009). 4
Dines G et al. Proc Aust Soc Sugar Cane Technol Vol 34 2012 Tared weights, and cane quality information ncluding mud (soil) weights, recorded when cane is received at the factory, are reported back to each harvesting group within 30 minutes. Harvesting groups monitor and modify their behaviour to ensuree maximumm productivity and best opportunity to maximise target bin weight incentives. Cane transport NSW Sugar Milling replaced older steel bins in 2006 and 2007 with larger and lighter aluminiumm bins (Doolan and Lamb, 2009). The newer bins tare 3.74 tonnes net and have a maximum carrying capacity of 89 m 3 of mechanically harvested sugarcane. A prime mover with a single full bin of mechanically harvested sugar cane should ideally arrive at the factory weighingg 43 tonnes gross. All bins are identified both with passive RFID tags and a handwritten painted number. Prime movers are two axle B double multi lift vehicles. Unmanned trans-loading pads are located in optimum locations servicing the cane harvest catchment area (Prestwidge et al., 2006). Each pad is located and mapped using GPS and GIS technology. Each pad has a virtual boundary, enabling records regarding truck arrival and departuree times to be recorded. Waiting or unloading times for each transaction at the pad is collected and monitored. An average drop off and pickup time for transactions by a prime mover at a pad is seven minutes. The average distance between a block and trans-loading pad is approximately 800 metres (Prestwidge et al., 2006). Prime movers are tracked using integrated GPS/modem m devices. Information on each vehicle location and activity is visualised by a web-based application (Figure 3) in each factory s control office (Figure 4), or by cane officers or management at any time. Green vehicles carry a full bin of cane and are returning to a factory to unload. Blue vehicles are outward bound on a trip to pick up a previously electronically consigned full bin. Vehicles coloured red are reporting an invalid GPS signal, possibly as a result of being out of range or suffering a fault. Black coloured vehicles are recorded not on a trip or otherwise occupied. This also occurs when a vehiclee enters the factory and finishes its trip, before a new trip has been allocated to it. Estimated time of arrival (ETA) information is attached to each vehicle. Fig. 3 Screen capture vehicle tracking, web based visualisation. 5
A transport scheduling application, FREDD, allocates trips for cane pickup from pads to available vehicles based on configurable settings. Current strategies for dynamic automatic decision making include: meeting required crushing rates, maximising average bin weights, variable road conditions including incremental weather events, road repairs and traffic density. Other objectives can include: minimising cut to crush time, maintaining harvester group equity, zone equity and maximising vehicle utilisation. Trips are allocated when full bins are consigned by harvesting groups. FREDD will allocate a trip to the next available vehicle by providing a printed docket to the driver at the weighbridge. Vehicle ETAs are calculated on a central server, where all vehicle GPS locations are collected. FREDD then generates a trip, updates locations, and monitors each vehicle on a trip automatically. Generally, FREDD operates unmanned at the three factories, with minimal human intervention. Fig. 4 Factory control desk monitoring harvest and transport operations. The average trip distance, factory to trans-loading pad and back to the factory, is 27 km across the factory group. Because each full bin is consigned at the pad the factory is aware of the number of full bins, their location and available cane before it arrives at the factory. This includes cane in transit. ETA information for each trip is available to the factory enabling projected available volumes of cane to be estimated at any time. This is matched to planned crushing capacity. FREDD monitors projected volumes versus planned crushing capacity and distributes new trips to ensure the factory is fed with cane to maximum planned capacity at all times. Short notice interruptions in supply due to unforseen events generate a slowdown in factory real time crushing rates ensuring the opportunity for a mill stop is minimised. NSW regulatory authorities require drivers to take breaks at regular intervals in order to maximise safety and health of the driver, and other road vehicles. During these breaks drivers are not allowed to drive a vehicle. A fatigue management module attached to FREDD monitors driver activity and provides recommendations regarding best times for driver breaks. The module monitors levels of activity across the supply chain during these decisions. Drivers self manage fatigue breaks. FREDD advises the control officer when a driver is due to return from a break and only includes him in scheduling decisions when he has been returned to the system. 6
Cane receivals A virtual boundary, using GPS location and GIS mapping technology, indicates when tracked prime movers with full bins arrive at the factory. In addition, RFID tags on both each prime mover and bin record truck and bin identities, together with a date/time stamp, as the truck drives slowly past an appropriately mounted passive RFID reader just inside the factory gate. Vehicles move to the automated unmanned weighbridge where gross weights are recorded. Trucks proceed to the tip where cane is unloaded. Unloading is controlled by the prime mover driver following indicators provided by signals from the factory DCS. When tipping is completed the prime mover and empty bin proceed once more to the weighbridge for taring. RFID tags are verified at the weighbridge using a passive RFID reader when both gross and net weights are recorded. Delivery information including source of the cane is provided to the driver on a cab level mounted screen. Upon completion of the net weighing task a printer, located at cab height on the weighbridge, provides the next trip to the driver. Trip docket information included pad number and location. Safety information provided on the printer docket includes a list of vehicles on trips that share the route to be taken on the new trip, and information about power lines that might be on or near the trans-loading pad. Safety information reduces the business risk associated with accidents or collisions, and improves driver safety and confidence. The driver proceeds immediately on his new trip. Productivity data collected includes arrival and departure times, waiting times at each location for each prime mover and bin. ETA times are compared with actual arrival times. ETA times for every route are updated dynamically with actual trip times ensuring accuracy is maintained. If road repairs for example delay arrivals on a particular route the new times will update ETAs every three trips automatically. FREDD adjusts its scheduling automatically to account for variations in ETA. System-wide impacts Across the group s three factories, harvesting and transport management, weighbridge and receivals operations are managed by one group manager, three cane supply officers (one based at each factory) and two control officers on shift in each factory (total six), a total of 10 staff. Control officers are also responsible for a variety of laboratory sample handling and analysis tasks in addition to monitoring supply chain activity. As a result, control offices are collocated with the factory laboratories. High levels of harvester, prime mover and bin productivity maximise use of capital and minimise operational costs. System application software and hardware are monitored continuously and incremental continuous improvement is practised to improve operations. Tracking, scheduling and recording of filter mud truck activities is an example of a recent development project. Tracking the distribution of filter mud to and within blocks ensures accurate monitoring of application of nutrients in the filter mud to the block, thereby more accurately determining nutrient load in total and potentially reducing input costs for growers. Tracking and scheduling also assists in ensuring effective management of logistics costs and efficient operations. An ongoing focus on centralisation aims to minimise distributed database and application maintenance tasks while standardising file formats and reports as far as possible. Improved data provision back to field operators in real time, or near real time, has significantly aided field productivity. Improved communication across the supply chain reduces friction and aids in building flexibility and reducing concerns and misunderstandings at all levels. 7
Harvesting groups receive live feedback indicating bin weight and quality performance, and can see when factories slow down or stop. Feedback on machine activities contributes to more effective harvester maintenance and operation. The introduction of SHIRT for example has resulted in fewer complaints related to bin supply and equity due to improved transparency and better information (Rose et al., 2009). Feedback also contributes to low levels of transport regulatory authority infringements. All movements of vehicles are tracked and archived. Location information including speed, distances travelled, dates and times has been used to protect transport drivers when complaints have been made or when accidents occur, unless the driver is shown to be at fault. As a result of integrated systems and centralised databases, management obtains accurate and timely reports of operational activity and performance when required. Small problems that develop are quickly exposed and dealt with before larger issues emerge. Drivers are largely self-managed, and operate safely as a result of accurate trip information and knowledge indicating the presence of other vehicles on their route. This contributes to minimising chain of responsibility liabilities for NSW Sugar Milling, reduces business risk and improves driver safety. Integration of transport scheduling with factory operations allows for adjustment of the crushing rate to minimise mill stops if interruptions in cane supply occur. Equally, if factory operations are interrupted the communications system provides information to harvesting groups ensuring large quantities of harvested cane do not build up on pads affecting cane quality. Intertransfer of cane occurs if, following a mill stop, significant delays in factory operations occur. FREDD manages pickup and delivery of the cane from the factory that has stopped, seamlessly integrating it with the normal supply of the second factory. Conclusion NSW Sugar Milling operates a world class harvest and supply chain. Sugarcane is harvested in an efficient and timely manner to dynamically match optimal factory crushing performance. The supply chain operates at least cost, with very low levels of human resource and with significant use of automated operating and visualisation technologies. Developed system reporting functions provides valuable operational and performance data to management in real time. Continuous improvement practices are ensuring ongoing incremental advances in effective system operation. Further advances are underway including filter mud tracking and recording and improvements in centralised data management and reporting. Acknowledgements The authors wish to thank the staff of NSW Sugar Milling, the staff of Agtrix Pty Ltd and the staff of various harvesting groups in NSW for their input and advice. REFERENCES Dines G, Petersen GJ, Worth RT (1999) Productivity advances in a road transport cane delivery system. Proceedings of Australian Society of Sugar Cane Technologists 21, 468 473. Doolan GT, Lamb BW (2009) Whole crop cane transportation: Introduction and maintenance of lightweight high volume aluminium bins. Proceedings of Australian Society of Sugar Cane Technologists 31, 556 567. Giles R (2006) An optimisation study of the sugarcane transport fleet. Proceedings of South African Sugar Technologists' Association 79, (CD-ROM) 13 pp. Hassuani SJ, Leal MRLV, Macedo IDC (2005) Biomass power generation: Sugar cane bagasse and trash. Report of Programa das Nacoes Unidas para o Desenvolvimento (PNUD). 217 p. 8
Higgins AJ, Muchow RC (2003) Assessing the potential benefits of alternative cane supply arrangements in the Australian sugar industry. Agricultural Systems 76, 623 638. Meyer E, Domleo K, Bliss J, Maher GW (2000) Assessing the viability of a fully mechanised harvesting operation for a large sugarcane estate. Proceedings of South African Sugar Technologists' Association 73, 74 78. Oliveira AL, Balieiro SF (2010) Comparative study between the Australian and Brazilian agriculture practices. Sugarcane, sugar and ethanol production costs in Brazil. http://pecege.dyndns.org:8080/?sc=dn (accessed 6 December 2011). Prestwidge D, Lamb B, Higgins A, Sandell G, Beatie R (2006) Optimising the number and location of new cane delivery pads in the NSW sugar region. Proceedings of Australian Society of Sugar Cane Technologists 28, 1 10. Rose P, McRae S, Codina G (2009) SHIRT Real time supply chain information for harvester managers. Proceedings of Australian Society of Sugar Cane Technologists 31, 365 371. Salassi ME, Barker FG (2008) Reducing harvest costs through coordinated sugarcane harvest and transport operations in Louisiana. Association of Sugar Cane Technologists 28, 32 41. 9