Supply Chain Modelling And Management Essay For SND Company In Australia.

Using the following questions to guide your project report:

1. What is the lowest cost for SND? (Remember to include all the related cost components in your model.)
2. Where should SND set up its subsidiary DMS? Does the location make sense?
3. How is the international sea transport managed?
4. How is the domestic road transport managed?
5. What can you conclude from you model?
6. How sensitive is your solution to some of the parameters, such as unit cost at different places, transport cost, lead time?
7. How sensitive is your solution to the fluctuations of demand?
8. Let’s we assume that for domestic transport, coastal shipping can be used at one tenth of the road transport cost. Will this have some significant impacts on the solution (you can ignore the lead time for domestic sea transport)?
9. We haven’t considered the demand uncertainty, lead time variability, etc. In other words, our model is a static one. Please discuss, based on your understanding of the course, the applicability of network optimization models to supply chain management.

Production Cost of SND

Due to the improvement in the connectivity and the communication, the business operations are becoming more interconnected to each other. The different components of the production process of business are taking place in different regions of the world. While this allows the organisation to enjoy some of the conveniences, it also leads to complexity. This requires the management of the company to continuously monitor the supply chain modelling. One of the interesting point regarding the supply chain modelling and management is that it takes into account the changes in the external environment of the business as well. As a result, it is easier for the businesses to keep a track on the changing logistics costs related to the changes in the demand for the products and the activity of the other players of the market (Christopher, 2016). The supply chain modelling and management also provides the organisation with flexibility and resilience in order to deal with any kind of adverse situation regarding the operation. Hence, in the modern world with interconnected business operations established in different parts of the world, it is important to analyse the supply chain models.

This study is related to supply chain management problem pertaining to a company based in Australia. The SND Company is currently facing supply chain issues where it is supplied from OVS, a foreign organisation. The cost of supplying the products to SND is till the ports of each of the cities. However, the business of the SND faces a lot of volatility due to the changes in the demand from the side of the customers of the market. This also leads the organisation to exchange some of the supplied goods among the different cities of the country. For the transpiration among the different cities of the country, the company uses land transportation. DMS is the subsidiary of SND which has some of the productive capacity and provides support to SND in case of peak demands.  

Table 1: The demand and the unit production cost for SND

(Source: Stadtler, 2015)

The above figure shows the cost of SND per unit in different cities of the country. As per the information of the table, DMS Adelaide has the lowest cost of production compared to the other locations of the country (Schönsleben, 2016). However, if per unit production is taken in to account, per unit cost of both Melbourne and Sydney is less due to the high demand that it has for the products. The different components of the cost are presented below:

Transportation Costs

Table 2: The cost of production in different locations of the country

(Source: Monczka et al.2015)

The above table shows the cost of operation at different DMS of the company. It is cheaper to produce in DMS Adelaide as the overall cost is around $450 which is less compared to other locations. It is important to note that, the DMS Sydney has the production cost of $515 which is the highest among all the locations. DMS are the subsidiary production units if SND which will be used to source the products in case of high demand in the market. However, one of the pitfalls of producing in the DMS is that it can only produce 500 units maximum in each of the location and hence the management of SND needs to use a combination of different locations based on the cost of production and the demand for the products in the market. Hugos (2018) stated that, the logistic modelling allows the management of the organisation to choose different locations based on the different types of demand in the market.

Table 3: The road transportation cost of SND

(Source: Wang et al. 2016)

The above table depicts that total road transportation cost to the company is $2162491.47.  This is related to the total demand worth of $950. It is important to note that transportation to Melbourne from Adelaide and Brisbane is high whereas other costs are low.

Sea transportation costs

Table: The sea transportation cost for different cities

(Source: )

The table above shows the cost of transporting the products in different cities of the country. The cost is maximum for the case of Adelaide and minimum for Perth. However, it is important to note that the lead time for each of these cities are also different from each other. While the lead time in case of Adelaide is 30 days the lead time for the case of Perth is 18. Perth also have the lowest $/LCL unit value as well.

Table 4: The transport cost per unit between the different cities

(Source: Stevens & Johnson, 2016))

The table above points out different transpiration cost from one city to another which SND may use in the wake of high demand. While there is no cost of product movement within the city, there is a different cost associated with transportation among different cities. However, in terms of the average cost, Melbourne is the right place for the subsidiary given its high demand and average transportation cost to different other locations of DMS. This location makes sense as it has high demand for the products (Ho et al. 2015).

Sensitivity Analysis

Table 5: demand and the cost at different seaports

(Source: Touboulic, & Walker, 2015)

The above table shows the lead time, purchase cost, pipeline stock cost and the total cost from OVS. In terms of the lead time, Perth is the lowest however; its total cost is $418221. In terms of the management, the shipments will be made direct to each of the seaports and production capacity of each of the units needs to be increased so that, products can be supplied to other parts of the country in order to extract the benefits from the shipment (Grant, Wong & Trautrims, 2017). The total cost of shipment in different international ports will be $1959041 which may vary depending on the demand for the products in the market.

Table 6: The cost for domestic transport

(Source: Ross, 2016)

The domestic transports are done through the containers of 20’ and 40’ which further can hold 200 and 100 units of the products respectively. Given the DMS subsidiary in Adelaide, the transportation cost is lowest for Perth and highest for Sydney. As per the demand and the total purchase cost is also shown in the above table (Chin, Tat & Sulaiman, 2015). The domestic transportation is done through the comparison of demand and the total purchase cost for each of the locations .

Conclusion of the model

The model concludes that it is preferable to use the services of DMS rather than the services of the OVS. This is due to the fact that, the DMS locations are different big cities of the country and the road transportation cost is reasonable. Although, each of the units of production is can produce only 500 units, the organisation needs to source supplies from different locations based on the cost and the demand. Apart from that, other advantage of using the service of DMS includes the operational cost which the organisation can remove during the time of low demand as these DMS units will then operate autonomously to manage their own cost of operation.

The solution is sensitive to the changes in different parameters pertaining to the transportations of the goods in the locations. For example, the lead time, changes of which can alter the cost of operation of the company and the sourcing decision from different DMS of the organisations (Alftan et al. 2015). The lead time changes creates problem for the shipment timelines and hence the cost of transpiration and hence the production cost of the organisation goes up. The use of solver in the model has ensured to deal with the changes parameters such as the changes in the unit cost and the lead time for each of the shipments.

Conclusion

The solution of the model is also sensitive to the changes in the demands in the market as well. The different demand level in the market will require different levels of sourcing from DMS of the organisation (Maloni et al. 2017). The changes in the demand will also impacts on the cost of production and the transportation cost as well. The use of solver in the model has allowed the cells to change depending on the values of the other cells (Hohenstein et al. 2015). The changes in demand can be put in the model and solver will adjust to provide the accurate cost and production detail from each of the DMS as per the demand.

If the domestic transport and coastal transport can be used in 10% of the road transport, then the solution would change. The use of solver in the model has allowed the result to change with the changes in the parameters. In that case, it will be preferable for the SND to get the supplies from the OVS and distribute in different DMS locations as per the need and the market demand for the product. In addition to that abrupt changes in the demand can also be managed through this as well.

The network optimisation is a concept through which the businesses optimise the supply chain connections in order to minimise the operational cost of the business. In this solution, the lead time and the demand variability have not been accounted and hence optimisation in this case is static. Dynamic optimisation allows the organisation to include the changes in the parameters which in turn allows the management to take action based the result. This type of optimisation allows minimising the cost of operation given the output level guided by the market demand. In order to trace the change in demand and its impacts on operation, dynamic optimisation is important.

Reference

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Chin, T. A., Tat, H. H., & Sulaiman, Z. (2015). Green supply chain management, environmental collaboration, and sustainability performance. Procedia CIRP, 26, 695-699.

Christopher, M. (2016). Logistics & supply chain management. Pearson UK.

Grant, D. B., Wong, C. Y., & Trautrims, A. (2017). Sustainable logistics and supply chain management: principles and practices for sustainable operations and management. Kogan Page Publishers.

Ho, W., Zheng, T., Yildiz, H., & Talluri, S. (2015). Supply chain risk management: a literature review. International Journal of Production Research, 53(16), 5031-5069.

Hohenstein, N. O., Feisel, E., Hartmann, E., & Giunipero, L. (2015). Research on the phenomenon of supply chain resilience: a systematic review and paths for further investigation. International Journal of Physical Distribution & Logistics Management, 45(1/2), 90-117.

Hugos, M. H. (2018). Essentials of supply chain management. John Wiley & Sons.

Maloni, M. J., Campbell, S. M., Gligor, D. M., Scherrer, C. R., & Boyd, E. M. (2017). Exploring the effects of workforce level on supply chain job satisfaction and industry commitment. The International Journal of Logistics Management, 28(4), 1294-1318.

Monczka, R. M., Handfield, R. B., Giunipero, L. C., & Patterson, J. L. (2015). Purchasing and supply chain management. Cengage Learning.

Ross, D. F. (2016). Introduction to e-supply chain management: engaging technology to build market-winning business partnerships. CRC Press.

Schönsleben, P. (2016). Integral logistics management: operations and supply chain management within and across companies. CRC Press.

Stadtler, H. (2015). Supply chain management: An overview. In Supply chain management and advanced planning (pp. 3-28). Springer, Berlin, Heidelberg.

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Touboulic, A., & Walker, H. (2015). Theories in sustainable supply chain management: a structured literature review. International Journal of Physical Distribution & Logistics Management, 45(1/2), 16-42.

Wang, G., Gunasekaran, A., Ngai, E. W., & Papadopoulos, T. (2016). Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International Journal of Production Economics, 176, 98-110.