REPLENISHMENT PLANNING AND SECONDARY PETROLEUM DISTRIBUTION OPTIMISATION AT Z ENERGY

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1 REPLENISHMENT PLANNING AND SECONDARY PETROLEUM DISTRIBUTION OPTIMISATION AT Z ENERGY Dr. Warren R. Becraft 1, Mr. Dom Kalasih 2 and Mr Stephen Brooks 2 1 Aspen Technology 371 Beach Road, #23-08 KeyPoint, Singapore Warren.Becraft@aspentech.com 2 Z Energy Limited 3 Queen's Wharf, Wellington 6140, New Zealand Dom.Kalasih@z.co.nz Steve.Brooks@z.co.nz ABSTRACT In today s complex and dynamic downstream environment, the secondary petroleum distribution team at Z Energy is dependent on instant and accurate information to make decisions about supply, inventory, customer demands, and scheduling. To improve, automate and optimise the largely manual workflow and relatively large time lag in the current process for forecasting fuel stock replenishments and scheduling shipments to its service stations and truck stops, Z Energy selected Aspen Fleet Optimiser (AFO) from Aspen Technology. AFO was implemented as a key component towards Z Energy s objective to deliver maximised margins by best managing its order-to-cash business process workflow. AFO consists of Automated Stock Replenishment, utilising advanced demand forecasting and replenishment algorithms, and Resource Scheduling Optimisation, utilising advanced scheduling optimisation algorithms and heuristics. These tools enable Z Energy to financially and operationally optimise each component of the overall logistics business process. AFO delivers value to Z Energy by forecasting product demand for each site, enabling inventory control to minimise runouts and haulbacks while reducing excess inventory, and optimising transportation schedules to minimise distribution costs providing lower cost per volume delivered. The key improvement is unlocked by shortening lead time between analysis of tank inventory levels and truck dispatch, consequently having greater certainty that scheduled loads are completed successfully. An equally important driver to Z Energy implementing AFO is that optimising the road transport bulk delivery task will deliver better health, safety and sustainability results by reducing the size of the task and thereby reduce risk exposure in those areas. INTRODUCTION Z Energy distributes over 1.8 billion litres of fuel by truck, over 8.1 million kilometres across New Zealand, in 72,000 trips each year (Z Energy Ltd., 2012). Each of these fuel deliveries needs to be optimised to provide a lowest cost per volume delivered, as well as to minimise supply chain disruptions to the end clients, the Z Energy retail petroleum stations, truck stops and commercial fuels clients. Optimising the efficiency of its logistics activity is also a key enabler to Z Energy achieving its ambitious sustainability goals which includes reducing the distance it travels to deliver fuel by an average of 15% for every litre of fuel delivered. In 2011, Z Energy initiated a project to improve, automate and optimise the process of secondary fuels distribution.

2 Key high-level objectives for the Z Energy project to implement a system that forecast fuel stock replenishment and assign shipments to customer fuel tanks were identified. These objectives included the ability to analyse, plan and forecast tank replenishment more simply and accurately, to be able to optimise delivery drop size, and to provide suitable reference information for optimal management of resources. The benefits identified through achievement of these goals included reducing the delivery distances travelled, reducing time to complete the replenishment plan, reducing re-work, and reducing the number of retains and stock outs. Additional benefits included reducing delivery costs and reducing working capital tied up in excessive inventories at customer sites. Z Energy s Secondary Petroleum Distribution Network Z Energy supplies 21 different products (motor spirits, diesel, aviation, kerosene, fuel oil and chemicals) to approximately 800 sites comprising retail and commercial customers. As shown in Figure 1 deliveries are drawn from 13 terminals across the North and South Islands of New Zealand. Deliveries are made to 208 petroleum stations, 94 truck stops and 50 airfields, as well as several hundred commercial customers with annual volumes and storage ranging from several thousand to tens of millions of litres. Prior to this project, the existing Z Energy workflow for forecasting stock replenishments and scheduling of deliveries was largely a manual process. Due to the complexity of the task, it proved difficult for Z Energy schedulers to accurately and consistently develop forecasts of each individual client s stock replenishment requirements and dispatch the appropriate transportation resource for the delivery in a timely manner. Marsden Point Refinery Wellington Dispatch Centre Terminals North Island - Marsden Point - Wynyard Wharf - Wiri - Mt Maunganui - Napier - New Plymouth - Hutt City Terminals South Island - Nelson - Woolston - Lyttleton - Timaru - Dunedin - Bluff Fig. 1: Z Energy Petroleum Distribution Network The consequences of this complex problem and manual workflow were resultant supply chain disruptions involving tank stock-outs at client sites and carry-overs involving product returned to the gantry or delivered to a secondary location due to insufficient ullage at the client site to accept the full delivery. These supply chain disruptions caused unnecessary economic costs as well as increased safety risks, which led Z Energy to initiate the project to optimise the secondary distribution business. 2

3 aspenone Solution Aspen Fleet Optimiser Aspen Fleet Optimiser (AFO) is Aspen Technology s secondary petroleum distribution solution, supporting supply chain best practices by integrating Automatic Stock Replenishment (ASR) and Resource Scheduling Optimisation (RSO) (Aspen Technology, 2011). Utilised by fuels distribution companies around the world, AFO has proven its value to companies such as Sinopec which optimises fuel deliveries to over 20,000 petrol stations, from over 300 oil depots, with over 2,000 delivery vehicles, every day (Hu, 2011). AFO has proven itself to be a valuable solution even in the face of extreme circumstances adversely affecting fuel distribution. In October of 2010, AFO enabled Total to quickly reschedule fuel deliveries and better manage its fuel orders, delivery fleet and fuel depots during the French trade union strikes (Ikhmir, 2011). When trade activists blockaded Total s refinery and depots, AFO provided a way for Total to prioritise fuel deliveries, ensuring no runouts for critical customers, and quickly adapt to changing sales trends for better forecasts during volatile sales activity. SECONDARY PETROLEUM DISTRIBUTION ISSUES In the petroleum fuels distribution business, the distribution of fuel from the source oil refinery to the fuel depots or terminals is known as primary distribution. Secondary distribution involves the delivery of fuel from the fuel depot or terminal to the end client, usually a retail petroleum station, truck stop, airfield, or other commercial customer, typically by truck. Two key objectives in the secondary distribution of petroleum fuels are determining when to replenish the fuel stocks at a client site, and the selection of which transportation resource to make the delivery, preferably at the lowest cost per volume delivered possible. Knowing when to replenish fuel stocks involves having up to date knowledge of current inventory positions, accurate forecasts of future sales or demands, and the appropriate calculation of the expected delivery window when the client can accept the replenishment and when it will be absolutely necessary to receive the replenishment in order to avoid running out of fuel. Automated Stock Replenishment Vendor Managed Inventory (VMI) is a supply chain best practice that is increasingly being adopted by organisations responsible for secondary petroleum distribution, such as Z Energy. Supplier controlled inventories enable the fuel supplier to better proactively manage replenishment planning across all client sites, providing forward visibility into the timing of upcoming replenishments, and enabling optimisation of transportation resource usage and resource balancing, instead of reactively dispatching fuel orders at the last minute as they are called in by clients. Automated stock replenishment converts a manual, error-prone process into an optimal fuel replenishment plan that reduces supply chain disruptions and associated costs. The key to achieving the benefits of automated stock replenishment is in the calculation of, and adherence to, individual product replenishment delivery windows for each client. In the example illustrated in Figure 2, calculation of the timing of the required fuel stock replenishment at the Z Energy client site is dependent on a number of variables including site tank capacity, current inventory and forecasted sales, delivering truck capacity or replenishment order size, and retain/runout buffers, among other factors. 3

4 Truck Capacity = 20 kl kl Cumulative Forecasted Sales 20 Delivery Window 0 00:00 08:00 16:00 00:00 08:00 16:00 00:00 Tank Capacity = 40 kl Retain Point Fig. 1: Calculation of Replenishment Delivery Window Runout Point The runout point is the time at which the client site s fuel sales reduces the tank s inventory to the pump stop level, beyond which saleable product cannot be pumped out and the station experiences a stock out. Z Energy stations will experience a loss of sales if a stock out occurs. The potential economic loss may extend beyond the actual lost sales during the stock out period, as the loss of goodwill or customer loyalty may cause further losses. The predicted runout point is calculated from the client site s inventory and sales data, and forecasted future sales. The forecasted sales are generated through the use of AFO s advanced forecasting algorithm which takes into account sales trends, average daily sales, sales segments, and special conditions. Sales segments break down the average daily sales into specified periods within the operating day for a more accurate determination of predicted sales, and thus inventory positions, throughout the day. In Figure 1, approximately half of daily sales occur between the hours of 8:00am and 4:00pm, with one-third occurring between 4:00pm and midnight, and the remaining occurring during the night shift from midnight to 8:00am. Based on a full tank inventory at midnight of the first day, the predicted runout point in the example would occur at approximately 10:40am on the second day, if predicted sales go according to forecast. The forecasting algorithm must also take into account day-of-week variations and special causes affecting sales, such as holidays, storms, promotions, competitor actions, and other local events, when calculating the runout point from the current inventory position. The retain point is the earliest time when the full load of the delivering truck, or truck compartment, will fit into the site s tank. If the truck arrives before the retain point, the full load cannot be dropped into the site s tank and will be retained on the truck. The retained load must then be either hauled back to the terminal, or diverted to an alternative client site to deliver the load. In either case, unnecessary expense is incurred due to the retained load, in addition to the safety risk involved by transporting the fuel product the extra distance to an alternative site. 4

5 Runout and retain buffers may be utilised to provide a safety margin in the replenishment timing. A runout buffer, moving the end of the delivery window earlier than the predicted runout point, would ensure that the replenishment delivery is made in time, even in the event of an unexpected surge in sales or potential delays in delivery. A retain buffer, moving the start of the delivery window later in time, would be used to ensure that sufficient sales have occurred to provide adequate ullage in the client site tank to prevent a retain situation. The scheduled delivery window is then adjusted by the respective buffer amounts to account for sales and transit time variability. Another key to improving secondary distribution planning and achieving further benefits is through proportional replenishment, i.e. replenishment of different fuel product stocks in proportion to individual product sales at a specific client site, as illustrated in Figure 3. Since most fuel delivery trucks contain multiple compartments, different fuel products can be delivered to client sites in differing amounts in a single delivery. The traditional method of stock replenishment keeps all product tanks full. This results in excessive deadstock sitting in client tanks that does not sell as fast, locking up working capital unproductively and increasing safety risks by maintaining an excessive amount of inventory of low-turnover fuel products. Proportional replenishment enables all products to reach the runout point at the same time, allowing for longer delivery windows and more flexibility in replenishment scheduling. Regular 70% of Sales Premium 20% of Sales Diesel 10% of Sales All Products Run Out at the Same Time Replenish in Proportion to Sales Regular Premium Diesel Maximises Delivery Window and Minimises Low-turnover Stock Inventory Fig. 3: Proportional Stock Replenishment In automatically determining the replenishment plan for a particular client, the runout point of the first product is calculated, the optimal proportional replenishment determined, and then a transportation resource configuration is selected that most closely matches the optimal shipment quantities. The retain point for the actual replenishment order is then calculated and the automated stock replenishment process is complete. The process is repeated to calculate future orders to provide better visibility into the replenishment plans for a selected number of time periods into the future. Not all Z Energy clients are under vendor managed inventory schemes, so manual fuel orders placed directly from non-vmi clients must also be accommodated, in addition to the automate stock replenishment orders generated by AFO for Z Energy s VMI clients. Manual stock replenishment orders are consolidated with the ASR-generated orders in the AFO replenishment manager and passed to the resource scheduling optimiser for 5

6 assignment to transportation resources and generation of the dispatch schedule for each shift. Resource Scheduling Optimisation Once the replenishment planning has been completed, the decisions must be made as to which trucks to use to deliver the replenishments to the stations and the timing of those replenishments. A feasible truck schedule can be produced manually, but an optimisation solution is required to consider all possible combinations of compartments, trucks, replenishment orders, costs and constraints such as truck, driver or site restrictions. Manually, a dispatcher may be able to consider a few alternatives in placing loads on trucks and scheduling deliveries, but the economic optimal schedule won t be able to be generated without an optimisation solution. Resource scheduling optimisation considers which shipments must be dispatched on a given shift in order to avoid a client runout, i.e. a must-go shipment, and which shipments can be dispatched early where client runout is forecasted to occur on a future shift, i.e. a can-go shipment. Can-go shipments provide flexibility to the optimisation algorithm to smooth out demand volatility for transports, maximise transport utilisation and minimise variable resource costs. Unnecessary costs are avoided by limiting the number of extra trucks that must be contracted for peak shifts and better utilisation of existing truck resources during shifts with lesser demands, as can be seen in Figure 4. Instead of requiring the hiring of additional transportation resources in four out of the six shifts being scheduled, only one shift requires additional trucks after converting to a must-go/can-go view of loads and developing a dispatch schedule using the Resource Scheduling Optimiser. Additional Required Traditional View Must-Go/Can-Go Optimised Trucks Available Shift 1 Shift 2 Shift 3 Shift 4 Shift 5 Shift 6 Shift 1 Shift 2 Shift 3 Shift 4 Shift 5 Shift 6 Shift 1 Shift 2 Shift 3 Shift 4 Shift 5 Shift 6 All Loads Must-Go Can-Go Must-Go Can-Go Fig. 4: Transportation Resource Optimisation Load Balancing The Resource Scheduling Optimiser factors in the must-go/can-go shipments, available transport capacity, all possible load configurations, possible combined or split shipments, product availability and cost at terminals, and other variable transportation costs, in order to produce an optimised, lowest-cost-per-volume-delivered, dispatch schedule. The dispatch schedule produced maximises available transportation resource utilisation and minimises unnecessary additional expenses incurred by hiring additional transportation resources for overflow work during peak periods. In addition to the load and compartment sizing constraints, other constraints to be managed by RSO in generating a schedule include weight-by-axel, compartment loading and unloading orders, product-compartment, truck-site, driver and other constraints. RSO produces an optimisation scorecard for each dispatch schedule created, providing detailed cost information to Z Energy s dispatchers, allowing them to compare different scenarios and responses to secondary distribution issues. Manual overrides of optimised dispatch schedules are allowed and may be necessary in certain situations to respond to 6

7 special circumstances. The optimisation scorecard allows Z Energy dispatchers to see the bottom line results of these changes. BENEFITS OF OPTIMISING Z ENERGY S SECONDARY DISTRIBUTION Z Energy has achieved both quantitative and qualitative benefits through the implementation of Aspen Fleet Optimiser. In the few months that AFO has been in place at Z Energy haulback/carry-over volumes have halved, and further reductions in unnecessary travel are expected. Stock out performance is also better than ever. This is the consequence of Z Energy dispatchers being able to more accurately forecast product demand for each site, enabling better inventory control to minimise runouts and haulbacks while reducing excess inventories. They are also able to optimise transportation schedules to minimise distribution costs, providing the lowest cost per volume delivered dispatch plan. The key improvement is unlocked by shortening the lead time between the analysis of tank inventory levels and the truck dispatch, consequently having greater certainty that scheduled loads are completed successfully. An equally important benefit to Z Energy implementing AFO is that optimising the road transport bulk delivery task delivers better health, safety and sustainability results by reducing the size of the task, and thereby reducing risk exposure in those areas. REFERENCES Aspen Technology, Incorporated. Aspen Fleet Optimizer Workflow Guide. Burlington, MA, Hu, Xiaogang. SINOPEC Secondary Distribution Optimization in Aspen Retail. AspenTech Global Conference OPTIMIZE 2011, Washington, D.C., May Ikhmir, Aaziz. Aspen Fleet Optimizer: Managing Supply Chain Disruptions during Labor Strikes in October AspenTech Global Conference OPTIMIZE 2011, Washington, D.C., May Z Energy Limited. Z Annual Review Wellington, New Zealand,