The objective of this assignment is to construct a MULTI The objective of this assignment is to construct a MULTI$-$METHOD MODEL $($combination of system dynamics and discrete event simulation model$)$ that accurately simulates the supply chain operations of a company. In order to accurately predict the inventory level of the supply chain model as well as the demand, potential users and users within the market place a combination of system dynamics and discrete event simulation modelling is combined. The reorder point as well as order quantity drastically impacts the inventory level fluctuation over time. Furthermore, a system dynamics model is constructed in order to accurately account for and simulate the market behaviour that the company operates in$.$ Both models will influence one another based on operations unfolding in each of them.

The market behaviours is influenced by a number of factors and the schematic illustration of the system dynamics simulation model is illustrated in Figure $1.$

Figure $1$: Schematic Process Flow of System Dynamics Model

The supply chain model will react to the market performance and the schematic illustration is illustrated in Figure $2.$

Figure $2$: Schematic Process Flow of Dynamic Event Model

Supply Chain Model

Various components from the Process Modelling Library were combined to form the total supply chain model. A Source generates the supply based on order quantity from the supplier, while a three Queue objects simulate the storage buffers between each Delay operation. Both the Delivery and the Production components illustrated in Figure $3$ are represented by the Delay operation. The input parameters applied to each component within the DEM setup is listed in Table $1$

Figure $3$: Anylogic Model Faculty View

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Table $1$: DEM Object Description & Input Parameters

Name Object Type Description

Supply Source The supply of good s from the supplier is simulated by this object. The original order quantity is defined by a property and is equal to $400.$

SupplierStock Queue The storage buffer of the supplier is represented by this queue. The storage capacity is infinite and the level is monitored by means of an on$-$screen dashboard.

Delivery Select Output The time that it takes to deliver the products.

The time that it takes to deliver goods is Triangular$(0\mathrm{.}5,1,1\mathrm{.}5)$ and the delivery quantity is equal to $200.$

RawMaterialStock Queue The storage buffer of the raw material is represented by this queue. The storage capacity is infinite and the level is monitored by means of an on$-$screen dashboard.

Production Select Output The time that it takes to produce the product from the raw material.

The time that it takes to deliver goods is Triangular$(0\mathrm{.}5,1,1\mathrm{.}5)$ and the production quantity is equal to $100.$

ProductStock Queue The storage buffer of the finished goods is represented by this queue. The storage capacity is infinite and the level is monitored by means of an on$-$screen dashboard. Whenever a sale occurs, a product is removed from this queue.

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Market Model

The market model consists of Flow, Stock, Dynamic Variables and Parameters that are linked to one another by links as illustrated in Figure $4.$

Figure $4$: Schematic Process Flow of market Model

The various components that make up the market model is listed and described in Table $2.$

Table $2$: Input Parameter and Dynamic Variable Description

Name Element Type Description

Potential Users Stock The total potential users which is equal to the TotalMarket parameter

Purchase Decisions Flow The rate of purchase effected by the SalesFromAd and AdEffect elements.

Demand Stock The demand generated for the product.

Users Stock The potential users that have already adopted the product or that adopt the product.

Discards Flow Product discards due to products reaching the life span.

SalesFromAd Dynamic Variable The rate of sales based on the effect that the advert has.

AdEffect Parameter The effect that the advert has on the potential users.

TotalMarket Parameter The total potential market population.

SalesFromWOM Dynamic Variable Sales induced due to word of mouth which is driven by the contact rate and effect of contact

ContactRate Parameter The rate at which contact is made with the users and potential users.

ContactEffect Parameter The effect that is made on the users and potential users.

ProductLifeTime Parameter The duration that the product lasts.

Model Integration

Once both models have been constructed, the two models have to be intergraded into one model with an event that triggers actions between the two models. The fully integrated model is illustrated in

Figure $5$: Fully Integrated Model

The Sales event triggers a sale of a product based on a condition where the demand is larger than one and the available product in stock is also larger than one. This event removes a product from the ProductStock object in the supply chain section of the model.

Furthermore, the Sales event also monitors the total inventory lev