In general, a process utilised by the organisations for deriving and generating information based on massive data repositories for the purpose of delivery, packaging and procurement is known as supply chain analytics. In this aspect, the overall analysis pertaining to the supply chain acts as an important part of management of supply chain (SCM) (Riahi et al. 2021). It is worth mentioning that one of the most important aspects of using subversion analytics is for increasing the performance and forecasting techniques along with meeting the necessary demands of the consumers. The emphasis given to the predictive analytics is mainly based on demand signal archive and point of sale terminals which may be conducive for an organisation to emphasise on customer demand and contribute towards taking quicker decisions but into distribution and product cost savings (Aryal et al. 2018). Some of the main features of the supply chain analytics can help an individual for enhancing the overall decision-making ability in the supply chain the following ways:
Presenting the data: in the recent times, there is an increased need for dicing and slicing the data for a variety of reasons for the purpose of gaining a more in-depth insight for interpretation and analysis.
Processing the stream: The processing the stream is associated with the concept of processing the overall information for gaining insight pertaining to multiple data sources as provided by third-party data, weather forecasts, apps and IoT.
Social media integration: this is seen as proceeding with the improvement in market planning to analysing social media feeds and sentiment data.
Processing of natural language or NLP: The advent of NLP has led to use a various type of unstructured data which is hidden across data streams, new sources and records as organised and extracted in the desired forms.
Intelligence related to where things are: This method is applicable for optimising and understanding the delivery requirements through gaining useful insights pertaining to positioning info.
Using the digital counterpart of supply chain: this is used for the purpose of enhancing prescriptive and predictive analytics of organising the data into rubber supply chain model which can be exchanged across various types of users.
Graph databases: The graph databases refer to specific kind of databases which are associated with storing information in organised manner, thereby making it easy for recognising trends, tracing of a particular facility, increasing commodity and locating the links (Anitha and Patil 2018).
Based on the process of decision-making in supply chain, it can be helpful for a business to implement supply chain analytics for several reasons. Firstly, the supply chain analytics was implemented against logistics and sales preparation in accordance with maintaining the balance of manufacturer’s supply and estimating the demand for schedules created along with regularly sinking the operations of the business strategy (Brintrup et al. 2020). Therefore, the supply chain analytics is directly as denouncing the risk assessment for the purpose of forecasting threats and potential risks based on dynamics and patterns of supply chain. Similarly, supply chain analytics became popular for the purpose of increasing the position level of reviewing the customer data and planning in order to determine the variables which is associated with fall or rise of customer demand. Supply chain analytics further became popular for the purpose of consolidating the sources of different data and determine the inventory level, fulfilment issues, forecasting of demand and strengthening the processing of consolation of data. Thirdly, decision-making in supply chain was announced through finding possibilities for alternative sources of streamlining the procurement process, leaving the expenditure, coordinating the different departments and improving the contract talks as well. Lastly, the conduciveness of decision-making ability of the supply chain analytics is evident in form of improving the models which is associated with deciding the relevant volumes of inventory required for the purpose of adhering to the service target requirements and capital spending in order to increase overall working capital (Tipi 2021).
The usefulness of the four models of supply chain analytics has been further in more ridiculous follows:
Descriptive supply chain analytics:
It is worth noting that across various horizontals of supply chain function, descriptive statistics is applicable. Moreover, descriptive statistics not only assists in enhancing the overall decision-making process but also helping an organisation in adopting different strategies as well (Raman et al. 2018). Some of the main strategies being implemented with the use of the descriptive supply chain analytics can be seen with as follows:
Pruning of the product – Based on the use of volume variability matrix, the quadrants, ADI and the COV is able to contribute to “Good SKU-Bad SKU.” Moreover, descriptive supply chain analytics also helps in getting rid of the Bad SKUs which the company fails to deliver.
Creating manufacturing strategy – Moving forward with lean thinking is one of the main characteristics of descriptive supply chain analytics. Therefore, lean thinking allows for low variability and high-volume production processes. Based on the use of joint preparation or modular production strategy, the high availability or high-volume affects consumers in many ways. It is seen to limit the overall sales policy and losses for the variability of the goods used.
Maintaining safety inventories – Proceeding with adjusting the security stock amounts is identified as another important aspect of descriptive figures. In addition to this, the SKUs servicing allows for consumers to take priority over high variability products and large frequency products using high speed and high volume. Moreover, safety inventories is conducive for ensuring higher accessibility to descriptive figures and simplifying the overall process of protection of security stock amounts (Kamble, Gunasekaran and Gawankar 2020).
Enabling reporting warning – The means of using conditional reasoning to generate stock out warnings is stated in the reporting warnings. In this regard, it is seen as a good idea for the planet to proceed with sharing the relevant SKU in accordance with the online inventory and in-transit inventory in order to make the overall level of the consumers order higher than zero. Therefore, as good as the planners of it and learn about a particular SKU, it is simpler for concentrating and refining customer experience on in-transit inventory (Hallikas, Immonen and Brax2021).
Some of the main examples of descriptive analysis in the supply chain inside can be seen with creating sales and marketing list for different types of machine code is using basic ABC segmentation. Moreover, it also allows splitting of the products and identifying the main items for range of goods which is used in accordance with ABC segments. It is further worth noting how descriptive analysis is used for matrix customer commodity where an ‘A’ client buying C product is able to produce B client. Moreover, it is also able to create a realistic matrix to segregate various combinations among products and customers. In the real-world situation, the descriptive analysis is highly beneficial for using the relevant information of standardisation and shipping over the last 12 months for the purpose of calculating the average output. In this regard, the default variance pertaining to typical COV is seen as 0.5. This acts as the main threshold for variability in the market (Chehbi-Gamoura, Derrouiche, Damand and Barth 2020).
The example of descriptive analysis can be identified with average demand interval where the delivery of the particular units is split among four months or 12/3 travels over a 12-month cycle. It is also worth noting how is conducive for keeping the overall count of the SKU customers which is effective for the overall sales plan. Lastly, the main examples of this would analysis evident with predicting forecast mistake and providing the metrix for variability in the volume (Govindan, Cheng, Mishra and Shukla 2018).
Task 1 – Distribution network schematic diagram for all potential flows
Figure 1: Distribution network schematic diagram showing all the potential connections/flows
(Source: As created by the author)
Task 2 – Formulation of linear programming model
In the context of producing a linear programming model, the formulation of all the variables, constant and objective function for the model is explained below as follows:
Variables
There exists a total of six variables in the LPP model which is listed as follows:
Based on the formulation of the variables we are able to identify that all the variables are nonnegative
Objective Function
Cost minimisation and identified as the main function of the linear programming model.
Z = Sum-Product of cost and initialized values
Constraints
The following are the constraints associated with the linear programming model:
Customer Demand
Maximum capacity of the factory
Maximum throughput of the depot
Task 1 – Program and solve the model developed in part B in EXCEL using Solver
Based on the solution of the excel solver, the optimal distribution network has been highlighted using the yellow colour.
|
Supplier |
Optimal Distribution Plan |
||||||
|
Liverpool |
Brighton |
Newcastle |
Birmingham |
London |
Exeter |
Sum Row |
|
|
Supplied to |
factory |
factory |
depot |
depot |
depot |
depot |
|
|
Depots |
|||||||
|
Newcastle |
0 |
– |
0 |
||||
|
Birmingham |
0 |
50000 |
50000 |
||||
|
London |
0 |
55000 |
55000 |
||||
|
Exeter |
40000 |
0 |
40000 |
||||
|
Customers |
0 |
||||||
|
C1 |
50000 |
0 |
– |
0 |
– |
– |
50000 |
|
C2 |
– |
– |
0 |
10000 |
0 |
– |
10000 |
|
C3 |
0 |
– |
0 |
0 |
0 |
40000 |
40000 |
|
C4 |
0 |
– |
0 |
35000 |
– |
0 |
35000 |
|
C5 |
– |
– |
– |
5000 |
55000 |
0 |
60000 |
|
C6 |
20000 |
– |
0 |
– |
0 |
0 |
20000 |
|
Sum Column |
110000 |
105000 |
0 |
50000 |
55000 |
40000 |
Solver Optimal solution generated based on objective function cell, variable cells and constants are stated below as follows:
Figure 2: Distribution network schematic diagram showing optimal solution
(Source: As created by the author)
Based on the objective of minimising the cost, it is conducive for Liverpool Factory directly supply to C 1 and C 6. In addition to this, the Birmingham Depot to look forward to supply the products to C 2, C 3, C 4 and C 5.
References
Anitha, P. and Patil, M.M., 2018. A review on data analytics for supply chain management: a case study. International Journal of Information Engineering and Electronic Business, 10(5), p.30.
Aryal, A., Liao, Y., Nattuthurai, P. and Li, B., 2018. The emerging big data analytics and IoT in supply chain management: a systematic review. Supply Chain Management: An International Journal.
Brintrup, A., Pak, J., Ratiney, D., Pearce, T., Wichmann, P., Woodall, P. and McFarlane, D., 2020. Supply chain data analytics for predicting supplier disruptions: a case study in complex asset manufacturing. International Journal of Production Research, 58(11), pp.3330-3341.
Carutasu, V., Aspects of the Cycling Phenomenon in the Linear Programming Problem (Lpp) Through the Example of Marshall and Suurballe. In International conference KNOWLEDGE-BASED ORGANIZATION (Vol. 24, No. 3, pp. 20-25).
Chehbi-Gamoura, S., Derrouiche, R., Damand, D. and Barth, M., 2020. Insights from big Data Analytics in supply chain management: an all-inclusive literature review using the SCOR model. Production Planning & Control, 31(5), pp.355-382.
Govindan, K., Cheng, T.E., Mishra, N. and Shukla, N., 2018. Big data analytics and application for logistics and supply chain management. Transportation Research Part E: Logistics and Transportation Review, 114, pp.343-349.
Hallikas, J., Immonen, M. and Brax, S., 2021. Digitalizing procurement: the impact of data analytics on supply chain performance. Supply Chain Management: An International Journal.
Kamble, S.S., Gunasekaran, A. and Gawankar, S.A., 2020. Achieving sustainable performance in a data-driven agriculture supply chain: A review for research and applications. International Journal of Production Economics, 219, pp.179-194.
Prasad, E., 2020, November. Divergent solutions of linear programming problem (LPP) and system of linear equations (SLE) for a particular coefficient matrix. In AIP Conference Proceedings (Vol. 2277, No. 1, p. 140001). AIP Publishing LLC.
Raman, S., Patwa, N., Niranjan, I., Ranjan, U., Moorthy, K. and Mehta, A., 2018. Impact of big data on supply chain management. International Journal of Logistics Research and Applications, 21(6), pp.579-596.
Riahi, Y., Saikouk, T., Gunasekaran, A. and Badraoui, I., 2021. Artificial intelligence applications in supply chain: A descriptive bibliometric analysis and future research directions. Expert Systems with Applications, 173, p.114702.
Tipi, N., 2021. Supply chain analytics and modelling: Quantitative tools and applications. Kogan Page Publishers.
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