SML - Prototype of an Entirely Automated Cereal Processing - Engineering Assessment Answer

SML is a newly formed engineering start-up that has designed and built a prototype of an entirely automated cereal processing and mixing line that produces 3 types of breakfast cereal on a continuous basis, 8 hours per shift, 3 shifts per day, 7 days per week. SML also operates as a food manufacturer in its own right and it produces a high-quality muesli that it sells not to the main supermarket retailers, but to smaller health food outlets where customers pay a premium for high quality produce. Historically, orders arrive daily in sizes up to about 100 kg but can be as low as 50kg. It is a growing market and SML wants to be able to satisfy all orders and possibly create additional capacity to increase its production. You have been appointed by SML to help them understand the behaviour of this new prototype machine and to determine the best way to improve and operate it. Ultimately, SML wants to market and sell this machine to other food producers. To do so, they may also have to provide a certain level of customisation for different customer requirements and it would be very important to know what specific design parameters, operating characteristics and procedures affect its commercial performance.

The Prototype Cereal Processor

SML produces 3 different flavours, depending on the mix of the muesli: Original (flavour 1), Apple (flavour 2) and Hazelnut (flavour 3). Under the current operating rules (“the Base Case”), each time an order comes in for a new flavour, a flavour change is required and this takes EXPO(30) minutes to perform. If the next order is of the same flavour, then a flavour change is not required.
The line commences with the Primary Processing of a batch of raw material (rolled oats), of which the size (in kg) and type (flavour) will be determined by historical data (given in the attached Excel file), randomly assigned in the model based on the demand distributions that you will derive from this data. The Primary Processing takes X minutes per kg. Under the current standard operating procedure (SOP) a new order that arrives will be assigned automatically its weight of oats and will
enter the primary processing queue and wait for this activity to become free. This queue can hold a maximum of 5 orders on its own conveyor. When this level has been reached, SML will not accept a new order until the queue length drops below 5 again. In that case, the order will simply be refused, and that sale will subsequently be lost.

As soon as an order (batch) is complete it is removed from the processor and placed in a Separator where it is divided into 500-gram units. The Separation process requires Y minutes per unit. Each time a batch of product arrives at a processing step, the unit time to produce each product is generated from the input distributions. The Separator also has a queue and it can only hold two orders in the queue in addition to one order being processed (separated). If that queue fills to capacity, the process preceding it, in this case the Primary Processor, will finish processing its current order but will not release that order to the separator or commence processing a new order.
The individual units are then sent to one of three filling machines. Each filler has its own queue. The fillers are identical, though their processing rates are different. Under the current SOP, separated units are sent to the filler with the shortest queue. The fillers operate at rates of A, B and C minutes for fillers 1, 2 and 3 respectively. The machine is quite flexible in terms of allocating units to fillers and it can be programmed, for instance, to send units to a specific filler in batches of 1 or more as you chose. As mentioned, each filler has a queue and the maximum number of separated items waiting in each queue is currently limited to 40. Again, when the queue capacity of all the queues have been reached, the preceding process, in this case the separator, will pause processing until
queues begin to free up.
The filled products are subsequently grouped by the filler and released into batches of six for final packaging into cartons. It is vital to keep this batch of six belonging to the same order. Once a group of 6 items is available, that group is sent to a packer that packs 12 units (2 x 6) into a carton identified by the order number. This takes Z minutes (per individual unit). In most cases an order will not consist of a convenient multiple of 6 packages. In that case the remainder of the order is partially assembled at the filler and partially packed in a carton by the packer. After packing the carton, either fully or partially filled, it remains in a holding area where it waits to be assembled with other cartons belonging to the same order. When the order is complete, it is shipped to the customer, and it leaves the operation. The packer also has a finite input queue length of 20 groups of 6 units. Once this space is occupied, the fillers cannot release any more individual packs or groups until space is cleared again. Transfer times between steps are assumed negligible though only a certain number of products can queue for each process. If these queues fill up the previous process or operation must enter a blocked state.
Morning shift starts at 6am, afternoon shift at 2pm and night shift at 10pm. The maintenance team only works morning shift.
Scheduled maintenance (lubricating, dis-infecting and cleaning) is performed based on utilisation of each process and this data is given in the attached spreadsheet. This is carried out during the morning shift, when triggered by the level of production since the last scheduled maintenance performed. If a maintenance task is triggered during the afternoon or night shift, then production may continue, and maintenance will then commence at the earliest opportunity on the immediate morning shift following. The production process may resume once a signal has been received that maintenance and cleaning has been completed. Under the Base-case scenario, this maintenance is
carried out first thing in the morning. However, as part of your recommendations, you are free to carry out this maintenance at any time during the morning shift, in order to align this task with the current state of the process (e.g. the contents of the queues). There is a single maintenance technician who carries out this work. This is a high priority task because of the complexity and number of moving parts, as well as to minimise the risk of contamination and consumer risk. In addition to maintenance, failures and stoppages also occur. Each machine is also subject to 2 types of failure, each with its own failure distribution and repair distribution. When a failure occurs

in the separator, the entire order being processed (including waiting to be processed in one of the queues) is wasted, and the order needs to be regenerated from the start. Any of the other failures will not result in a scrapped order. The MTBF and maintenance intervals are counted in units processed. If a failure should happen on the afternoon or night shift, the affected process will not be attended to until the maintenance team arrives first thing in the morning.
1. Management wants to know what design elements and characteristics of this automated process cause bottlenecks under the current SOPs, so that these may be rectified for the next design iteration of the machine.
2. Management also wants to know how many orders may be lost under the current SOPs, and under any operational or design changes you would propose.
3. It is concerned that a lot of time is wasted by changing flavours each time a different order comes in and would like to know if there is a better system or rule for grouping orders together. For example, should orders go to an initial holding queue and be grouped by flavour so as to cut down on change-over times? If this rule is adopted, management does not want an order to stay in this hold queue for longer than two days.
4. Another issue that management would like to know is if orders as small as 30 kg can be economically produced in addition to the current demand profile, at what level of demand (orders/day) and what effect this would have on the productivity of the process.
5. Are the rules for assigning units to fillers appropriate? Should certain orders be allocated to certain fillers for example?
6. Should the operators be trained to perform their own repairs on afternoon and/or night shift? What difference would it make if the process can be repaired on all shifts? Can failures be anticipated and can this information be used to optimise performance?
The design intent of this machine is to keep the overall physical size as small as possible while maximising the output or productivity of this device. It will mean designing the hardware and the operating environment (rules) concurrently. It is possible to increase queue lengths (conveyor lengths) but doing so of course increases the size of the machine and this would have to be considered very carefully. Most important is to understand the relationship between design elements, operating procedures and the external environment, and how these may be ‘tuned’ to give the best results.

ASSIGNMENT
Part 1: This will be held in Week 4 and each team member must attend and speak at this event. A timeslot booking function will be enabled o Moodle for your team to book a 20min slot with your lecturer. Feedback will be given on the day.
(a) Prepare a flowchart of how the production system operates.
(b) Define the scope of the study and the desired outcome from the simulation study you intend to undertake.
(c) You will be given input data for key variables such as the product type (flavour), batch size, processing time (in minutes per unit), failure rates (TTF expressed as a function of units produced) and repair times (TTR), for each of the process modules as well as a maintenance plan for the process. Analyse this input data and prepare a histogram for each set of observations. Fit a theoretical distribution to your histograms and assess the goodness of fit in each case.
(d) Prepare an Excel spreadsheet containing important replication data, that you anticipate you may want to experiment with in Part 3.

(e) Finally, document your work as a professional progress report. This will form a part of your final submission.

(a) Establish the “base-case” model.
(b) Incorporate into your model the structure, variables and rules you will experiment with in your final submission. You will not have to produce any results for your improvements at this stage, but the model should be prepared.
(c) Develop a simulation model of the operation, using Arena and include key performance information on your screen (queues, state, units processed, orders wasted etc).
(d) Specify and establish all required data structures and arrays to be used by Arena to look up values while the model is running, from your Excel worksheet and enable these to be read automatically by Arena.
(e) Specify the reports and logs that the model will write out (to a text file) and explain how you will use these as part of the analysis.
(f) Verify and validate your model, its operations and the behaviour of key input and process variables, and that the model performs as intended. Demonstrate that you have analysed this data in Minitab.
(g) Document the work performed as part of this stage and prepare the second progress report.
Part 3: A Written Report
(a) Design a simulation experiment, specifying the number of replications, replication length, warm-up periods etc., in order to ‘optimise’ the performance of the process
in terms of:
(b) Rules that maximise throughput and yield
(c) Minimise lost time, waste, lost orders
(d) Process utilisation
(e) Machine reliability and availability
(f) Perform sensitivity analyses on key process variables. These will include variables that management may want to investigate further in relation to improved maintenance and operational strategies.
(g) Establish appropriate confidence intervals for your results.
(h) Prepare a final professional report of the entire simulation study.

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