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Supply Chain Management Assignment: Emerging Trends In Logistics


Task: Problem statement considered in this supply chain management assignment:
Coolies has heard about the advantages that digitization brings to supply chains, and is keen to get started on the transformation. Coolies decided that this would be a great time to visualize its global network, and identify how this transformation can move forward. After identifying their traditional KPI-SCs for each node, Coolies plan to develop three digital supply chain KPIs (KPI-SCs) for all nodes (Step 1), based on the information provided in this Workbook and the DSCI paper.

Visualizing the traditional and digital KPI-SCs (Step 2) will help Coolies understand how far along the digital transformation they have come and how the remaining tasks should be planned.
Generally, their supply chain network consists of multiple nodes across Australia, including DCs and retail outlets, as well as a few international factories.
Using the network nodes and traditional KPI-SCs data, students are required to answer the following questions by referring to the contents of this workbook. As part of data cleaning process, you might be required to adjust the data or rename certain columns to successfully run the Supply Chain Map LogHub module:

1. Based on the traditional KPI-SCs provided for each node, identify at least 3 digital KPI-SCs that can be used by these nodes (read the DSCI paper). Justify your digital KPI-SCs selection and identify the source of information for each digital KPI.
2. Explain how the digital KPI is calculated, why it is chosen for that node, and describe any risks associated with the digital KPI-SCs.
3. Adjust the data set based on your findings and answer to Q2, and visualize the entire network for Coolies using the Supply Chain Map LogHub module, incorporating both traditional and digital KPI-SCs (total of 6 KPIs) for each node in the visualization. Note: It is assumed that the same KPI-SCs will apply to all similar node type in the data set.


Digital Supply Chain KPIs
1. Factory:

• Overall Equipment Effectiveness (OEE) Dashboards:As stated in this supply chain management assignment, it generally produced "score metrics" for "scheduled score run". It makes sure that industrial capacity of equipment’s is taken into consideration for undermining 'machine level monitoring' as well as 'bottlenecks". This is further divided by "Total Run Time" of a single asset along with "total planned production" (Moons, Waeyenbergh and Pintelon, 2019).Hence, it does not address realistic problems to production needs. It is indeed man aggregated metric that cause for "obfuscation rather than clear precision of production constraints".
• Machine Set Up Time: It involves calculation of a time frame that is required for preparation of a machine for further running. It helps in undermining the optimal lead time required for next round of production efficiency (Devet al. 2019). However, lack of appropriate confidence interval can lead towards inefficiencies along with "demand and production uncertainties" (McKinsey and Company, 2016).
• On Standard Operating Efficiency: It involves calculation of a time frame that is required for preparation of a machine for further running. It helps in undermining the optimal lead time required for next round of production efficiency (Qorri, Mujki? and Kraslawski, 2018). However, lack of appropriate confidence interval can lead towards inefficiencies along with "demand and production uncertainties"

2. DC/Warehouse:
• Day’s sales of inventory (DSI): This further indicates liquidity ratio of company's inventories based on digital supply chain. However, risks of IT anomalies as well as automated calculations can be prevalent without monitoring (De Vass, Shee and Miah, 2018). This allows for the company to take preventive interventions prior to the actual circumstance of risks.
• Automated inventory reorder points:This technological KPI can help in assessment of ways in which company can initiate due to lower level of inventory management. It also helps in safety management of digital stocks in a digital management chain. This is further composite of a “repetitive inventory management tasks’ that are considered with tracking of inventory flow from elements within supply chains such as “retailers, wholesalers, distributors, and other businesses”(Ozkan-Ozen, Kazancoglu and Mangla, 2020).It allows for the benefits of enjoying real-time inventory management for any firm operating within digital infrastructures of supply chain. It similarly reduces scope of human error with actual handling of inventory items with rational and analytical means. It would further help in assimilation of a number of components and uncertainty indexes that would otherwise not be considered through means of human performances.
• Perfect Order Rate:It helps to fulfil customer-centric goals of the company with a stipulated time frame. This is also a time taking procedure to depend the entire supply chain on (Ghadgeet al. 2021).

3. Retail Outlet
• Social media tracking:It is also a 'profitable lead generation tool" that can be administered to note down followership of a company along with monthly trends. It occurs while considering mismanagement of 'real-time crisis alerts"(Ozkan-Ozen, Kazancoglu and Mangla, 2020). Additionally, the calculation can also be done based upon identification of the value for "conversion goal" as well as through proper estimation of traffic and "revenue-based search volume". This would further facilitate for the calculation of "SEO Return on Investment" (Ghadge et al. 2021). Also, there is an increased demand for paid searches and thus, the company at initial position might have to invest high revenues.
• SEO Optimisation:It helps in initialising an intensive use of searching keywords that would enable for the company to look after retail sales at optimal amount. This would help for the company to initiate greater participation within the warehousing system through integrating automated technologies.
• User lifetime value:It depends on their initial sales pattern and their recent purchase statistics.This also signifies upon the ways in which the company would retain existing customers with strategic means(Ozkan-Ozen, Kazancoglu and Mangla, 2020). It integrates with number of customers within company's database as well as lower profitability in sections of products or services (Barykinet al. 2021). As this is measured for future income generation, thus the model can be regarded as a predictable one.


Node Type





Retail Outlet

Digital Supply Chain Metrics


Overall Equipment Effectiveness (OEE) Dashboards

Day’s sales of inventory (DSI)

Social media tracking


This can be effective in determining overall production capacity of a particular machine based on its relative productivity.

It generally represents a financial ratio that is reflective of average time-frame that a company takes in order to turn its production line into inventory.

Sources for this KPI range from manageable audience size, engagement rates within social or digital media, mentions and tagging from customers as well as social media "ROI"

Why this Metric

It is considered in order to look after critical measurements that would signify upon the "gauge efficiency of machines" used in factory production chains.

It is important as the KPI significantly concerns itself with COGS of goods along with starting date of digital inventory management till the ending.

This KPI can be significant while measuring total percentage of all customer footprints through social media networks.

Calculation Method

It is calculated through consideration of 'availability, performance and quality" taken together into a multiplied format.

It is calculated through consideration of factors such as "Day’s sales of inventory (DSI)"

It is calculated by a basic stage of dividing total audience or customers number by 100 for each post impression.


OEE does not directly involve opts calculations with company's goals and performance objectives

Lack of appropriate demand forecasting is one of the common lags. The KPI relays itself to average number of digital inventory management that occurs within a given point of time.

Risks associated with such tracking might include   inefficiencies in "deep data and metrics reporting"


Machine Set Up Time 

Automated inventory reorder points

SEO Optimisation


This is taken into consideration through optimisation of SMED or "Single Minute Exchange" of factory related technologies.

This is generally found in 'automated software inventory systems' that would calculate final inventory account based on reordering statistics of a single product.

SEO optimisation can be done through the use of unique keywords that can drive optimal amount of traffic to the company's website.

Why this Metric

It helps in reducing load on machine due to repetitive cyclic factory production.

This technological KPI can help in assessment of ways in which company can be informed regarding the lowered levels of inventory.

This metric can help in atomisation of volume of traffic while also focusing on "high-ranking keywords". This way the company would be sufficient in increasing 50 times faster appearances in social media networking sites.

Calculation Method

The actual set up time is considered based on FBM application that is further a series of collaborative features "F' and "C" in any relevant digital software.

This is calculated through the basic formula of multiplying "average daily user points" with 'total lead times in days" to replenish a particular inventory product.

Calculation can be done through the pulling of "conversion rates" for singular analytics goal".


Risks are administered based on "Holding Period Return" through TQM measurement software. .

Lack of training to analyse the KPI and reach to a strategic concept regarding inventory allocation.

SEO rankings are not permanent in nature and hence, company's statistics, priorly based on such KPIs are more likely to variate.


On Standard Operating Efficiency

Perfect Order Rate

User lifetime value


The efficiency with which a factory production line continues with transitions in time of production and opts overall cost efficacy in market is calculated through this KPI.

It is gained by the number of orders that are shipped to the valuable customers without the scope of any errors.

This signifies on the measurement of value index of a particular demographics of consumer

Why this Metric

Operational efficiency measurement through integration of digital tools helps in analysis of all the key variants that can be taken into account in order to make for the availability of "capital expenditure" as well as wide dynamics of "resource elicitation" and market satisfaction statistics.

Attaining "high perfect rate" would signify upon company's capacity to boost a particular product or services in terms of quality and quantity.

The higher the number of ULV, higher will be the company’s profits within a stipulated time.

Calculation Method

It is normally calculated through "a piece rate or incentive system" based on "post-production analysis".

It is calculated by the total number of orders sent to customers by the total number of returns done with reviews of faults in products.

The simplest formula to calculate "lifetime value' is through having an average of the net number of "orders" further multiplied by "yearly purchase number" and then by carrying out an average of "retention times" within the concerned years.


Risks might include environmental or externalities involved in transformation of analytical outcomes into reality. This could be regressive as company's progressive strategies would be entirely dependent on such KPIs.

Analysis of POI or "Perfect Order Index" can be misinterpreted without the governance of appropriate analytical procedures.

Taking account for a number of issues ranging from statistics of B2B performances


Table 1: Digital SC KPIs
(Source: Researcher)

Barykin, S.Y., Bochkarev, A.A., Dobronravin, E. and Sergeev, S.M., 2021. The place and role of digital twin in supply chain management. Academy of Strategic Management Journal, 20, pp.1-19.
Bicocchi, N., Cabri, G., Mandreoli, F. and Mecella, M., 2019. Dynamic digital factories for agile supply chains: An architectural approach. Journal of Industrial Information Integration, 15, pp.111-121.
De Vass, T., Shee, H., & Miah, S. (2018). The effect of “Internet of Things” on supply chainintegration and performance: An organisational capability perspective. Australasian Journalof Information Sysytems, 22.
Dev, N.K., Shankar, R., Gupta, R. and Dong, J., 2019. Multi-criteria evaluation of real-time key performance indicators of supply chain with consideration of big data architecture. Computers & Industrial Engineering, 128, pp.1076-1087
Ghadge, A., Kara, M.E., Moradlou, H. and Goswami, M., 2020.The impact of Industry 4.0 implementation on supply chains. Journal of Manufacturing Technology Management.133,pp. 103
Ivanov, D., 2020. Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transportation Research Part E: Logistics and Transportation Review, 136, p.101922. McKinsey and Company. (2016). Big data and the supply chain: Capturingthe benefits (Part 2). Retrieved from Moons, K., Waeyenbergh, G. and Pintelon, L., 2019.Measuring the logistics performance of internal hospital supply chains–a literature study. Supply chain management assignmentOmega, 82, pp.205-217.
Mor, R.S., Bhardwaj, A. and Singh, S., 2018. Benchmarking the interactions among performance indicators in dairy supply chain: an ISM approach. Benchmarking: An International Journal.12, pp. 220
Ozkan-Ozen, Y.D., Kazancoglu, Y. and Mangla, S.K., 2020.Synchronized barriers for circular supply chains in industry 3.5/industry 4.0 transition for sustainable resource management. Resources, Conservation and Recycling, 161, p.104986.
Qorri, A., Mujki?, Z. and Kraslawski, A., 2018. A conceptual framework for measuring sustainability performance of supply chains. Journal of Cleaner Production, 189, pp.570-584.


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