NetLogo User Community Models
## WHAT IS IT?
Supply chain resilience has gained more importance in recent years due to the trend for e-commerce. The industry has a tendency to sell products in low quantity, with high demand variation. To increase the resilience of the supply chain, demand forecasting is necessary. The model compares two approaches towards forecasting. One case focuses on forecasting only on individual distributor's history and another case considers the possibility to share information and forecast on all distributors' histories.
## HOW IT WORKS
In the beginining of every tick, household selects a distribution function and determines the ratio of order. The ratio informs the household to increase or lower their order. Than the ratio is multiplied by variation of product quantity (This approach is used to simulate different size of households). Every beginning of tick household sends an order to the distributor. The distributor fulfills the order the same tick and the stock level is updated. If the stock level is negative, the backlog is evaluated. Than distributors must order products from suppliers, however there is 1 tick delay for product delivery from supplier. Therefore, the demand is forecasted in advanced and supplier delivery are made in advanced. If there is a backlog, it is being added to the order to the supplier during the next tick, to increase the stock level.
## HOW TO USE IT
You can set the distributors quantity and household quantity. A limit is set of how much agents you can have to decrease the limit of hardware.
Share_information is used to change the forecasting approach. Forecast on individual company's history (off), forecast on group companies' history (on).
Market_type = oligopoly. Is used to simulate a small market where only few distributors exists, in this case the households always places orders.
Safety_level is used to determine the necessary stock level, which must be held to have a possibility to deal with drastic demand increase. The main idea of the forecasting approach is to have as little stock as possible. The level is set to a percentege of previous 7 day demand history.
mean_treshold. Due to the random distributions and product quantity, sometimes the households all order the maximum amount at these times the demand drastically spikes, which disrupts the results of the model. Therefore, there is set a limit of how many times the demand and forecasting results can increase when comparing with the mean demand.
consumer_loyalty. If consumer loyalty is turned on, consumers selects only 1 distributor in the setup stage and always stays loyal to it. Of the consumer loyalty is turned off, every tick the consumers selects different distributor.
## THINGS TO NOTICE
Real companies' sales histories are fitted on distributions with R package "tdistrplus".
The forecasting is done with a machine learning approach, Extreme learning machine (ELM). The high variation in demand variation is also necessary to limit the ELM ability to adapt to the variation precisely. The ELM algorithm has quick neural hidden layer computation capabilities, however in temrs of precision is weak. This particular algorithm has been chosen due to processing power limitations.
When using the behaviour space it is important to set parallel running process to 1, because the r code is used to suite linear programming and not parallel.
## THINGS TO TRY
Distributor_N and Clients_N can help to simulate different market size (e.g. Belgium vs USA).
Share_information helps to compare individual and group demand forecasting in the same context.
Safty_level is used to help limit the stock level. When the safety_level changes its important to evaluate the backlog level.
Market_type compares market type with many sellers (perfect competition) vs few sellers (oligopoly).
consumer_loyalty. This parameter simulates loyalty discounts and benefits vs random consumer behavior depending on lower price, availability or other criteria.
## EXTENDING THE MODEL
The model can be extended by introducing more product type variation, evaluating the costs of warehousing. The machine learning approach could also be used not only to make predictions, but also choose the safety_level and other variables on its own.
## NETLOGO FEATURES
I have used extension R to implement machine learning. NetLogo does not have built in futures.
## RELATED MODELS
## CREDITS AND REFERENCES
Extension R: https://ccl.northwestern.edu/netlogo/docs/r.html
Prediction method is not accurate, but has a very fast hidden layer evaluation, which is suitable for simulations with less computation power: http://www.ntu.edu.sg/home/egbhuang/
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