Supply Chain modelling is used in industry since last three or four decades. Considering the broad spectrum of supply chain, no model can capture all aspects of supply chain processes. To compromise the dilemma between model complexity and reality, a model builder should define the scope of the supply chain model in such a way that it is reflective of key real-world dimensions, yet too complicated to solve. In this blog I’ve briefly explained supply chain modelling approaches and characteristics (variables) which might be of interest to supply chain analysts.
Supply Chain Modelling- Approaches
To incorporate the 4 major decisions areas of location, production, inventories and transportation, Ganeshan and Harrison (1996) suggested that supply chain modelling process can be approach in 3 ways. These approaches were network design, ‘rough cut’ methods and simulation based methods.
The network design determines the location of production, stocking and sourcing facilities and the paths of the materials flowing through them. These are usually large models and normally carried out at the beginning of the design of a new supply chain. Network design methods have the advantage of enabling the system to determine the location, production, inventory and transportation strategies far into the future. There is however some weaknesses in that the model is large and time consuming to develop. The decisions that can be made using these models are more strategic than operational in nature.
Rough Cut method
Rough cut model has been widely used in industry. Most of these types of model take on an inventory management perspective and are useful for operational decisions such as rough cut capacity planning. The thrust of the rough cut model is the development of inventory control policies, considering several levels together. These models are known as ‘multi-level’ inventory control models.
There are several limitations to this type of model. Firstly, most of the studies of this type tend to ignore the production side at cell level and supplier capacities which is a main part of the supply chain. The other limitation is that this model focuses on inventory systems only and ignores the effect of transportation. For that reason many professionals thinks this method is a bit restricted due to the theoretical nature of the model. Most of the creators of rough cut models restrict themselves to certain well known forms of demand or lead time.
The third kind of supply chain modelling normally used by supply chain management involves simulation. This type of supply chain modelling is dynamic and is especially useful in studying the dynamic characteristic of a supply chain. Simulations are sometimes objected to as “real life is not like that” but nevertheless do provide some useful insights on supply chain operations within the limitations of the models (Towill, 1993).
Supply chain modelling and network design is also a great career options. You can find article of my friend Baidhurya “5 reasons why supply chain modeling is a great career option” useful to get started with supply chain modelling.
Supply Chain Modelling Characteristics (Variables):
Hokey and Gengui (2002) have explained “supply chain modelling characteristics generally expressed as a function of one or more of these decision variables. Thought not exhaustive, the following illustrates these variables:
Location: This type of variable involves determining where plants, warehouses (or distribution centers (DCs)), consolidation points, and source of supply should be located.
Allocation: This type of variable determines which warehouses or DCs, plants and consolidation points should serve which customers, market segments and suppliers.
Network structuring: This type of variable involves centralization or decentralization of a distribution networks and determine which combination of suppliers, plants, warehouses, and consolidation points should be phased-out. This type of variable may also involve the exact timing of expansion or elimination of manufacturing or distribution facilities.
Number of facilities and equipment: This type of variable determines how many plants, warehouses and consolidation points are needed to meet the needs of customers and market segments. This type of variable may also determine how many lift trucks are required for material handling.
Number of stages: This variable determines the number of stages that will comprise a supply chain. This variable may involve either increasing or decreasing the level of horizontal supply chain integration by combining or separating stages.
Service sequence: This variable determines delivery or pickup routes and schedules of vehicles serving customers or suppliers.
Volume: This variable includes the optimal purchasing volume, production and shipping volume of each node (e.g. supplier, a manufacturer and distributors) of a supply chain
Inventory level: This variable determines the optimal amount of every raw materials, part, and work in process, finished product and stock-keeping unit to be stored at each supply chain stage.
Size of workforce: This variable determines the number of truck driver drivers or order pictures need for the system growth”.
In general Supply chain modelling is used to test alternative supply chain decisions, evaluate performance and analysis weakness of the supply chain by supply chain analyst or managers. Significant efforts have been put into the creation of an ideal supply chain model so that accurate design on improvements in performance can be made.
In this various factors have to be considered. This involves selecting either the top down approach of working from a comprehensive to a detailed model or from a strategic to an operational mode. Next to be considered, are the modelling methods used to date, namely, network design, ‘rough cut’ and simulation which is suitable to the problem scenario you are looking at.
So if you like this article and find it useful, or have any other feedback please do leave in the comments.
Ganeshan R., Harrison T. P., 1996. An Introduction to Supply Chain Management. Department of Management Science and Information systems, Penn State University., USA.
Hokey, M ; Zoub, Gengui Z. (2002),” Supply chain modeling: past, present and future”. Computers & Industrial Engineering, Volume 43, Issues 1–2, 1 July 2002, Pages 231–249
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