# Supply Chain Quality Improvement Based on Customer Compliance

Rene Maas, Eduard Shevtshenko, Tatjana Karaulova

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Rene Maas, Eduard Shevtshenko, Tatjana Karaulova. Supply Chain Quality Improvement Based on Customer Compliance. 14th Doctoral Conference on Computing, Electrical and Industrial Systems (DoCEIS), Jul 2023, Monte Da Caparica, Portugal. pp.230-242, 10.1007/978-3-031-36007-7_17 hal-05473076

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# Supply Chain Quality Improvement Based on Customer Compliance

Rene Maas<fnref n="1" />,<fnref n="2" />✉, Eduard Shevtshenko<fnref n="1" />,<fnref n="2" />, and Tatjana Karaulova<fnref n="1" />,<fnref n="3" />

**Abstract.** Current research is focused on topics of interest in the digital transformation of the supply chain, data analytics and system automation perspectives. The study aims to test the hypothesis that real-time information on the supply chain could be used to improve customer service quality and lead to a more reliable supply chain. Research is done to analyse supply chain performance using SCOR-based KPI model. The Integration of the methods presented in the study focuses on a solution that minimises supply chain failures, decreases failure elimination time, and improves customer satisfaction. Originality is that the proposed mechanism, is based on the Supply Chain Operations Reference (SCOR) model and Bayesian Belief Network (BBN) to estimate the influence of KPI metrics improvements on Supply Chain efficiency. Along that using the network of interconnected KPI-s, solution will show which operational level best practices influence the strategic level metrices the most.

**Keywords:** Supply Chain Operations Reference (SCOR) · Key Performance Indicators (KPIs) · Bayesian Belief Network (BBN) · Digitalisation · Reliability · Customer Service Quality

## 1 Introduction

Digitalising the supply chain is essential to eliminate the communication barriers between supply chain stakeholders. Digital Supply Chains share data and work together to guarantee that processes are seamless and free of bottlenecks, disruptions, and failures. Indeed, effective communication supported by real-time information over digital platforms enables prompt decision-making for improved supply chain performance. Digital technologies characterising industry 4.0 present a new strategy for increasing product quality and enhancing process efficiency [1].

Businesses can use the SCOR model to analyse the supply chain performance and how well processes are aligned with business objectives [2]. The SCOR reference model can be applied to define process architecture that aligns with essential business functions and goals [3, 4].

<footnote n="1">¹ University of Tartu, Ülikooli 18, 50090 Tartu, Estonia
{Rene.Maas,Eduard.Sevtsenko,Tatjana.Karaulova}@tktk.ee</footnote>

<footnote n="2">² TTK UAS, Pärnu Mnt 62A, 10135 Tallinn, Estonia</footnote>

<footnote n="3">³ Tallinn University of Technology, Ehitajate Tee 5, 19086 Tallinn, Estonia</footnote>
The authors suggest measuring its performance by key performance indicators built based on the SCOR v12.0 model. It may be achieved by developing the SCOR database to establish the links between processes with performance metrics and best practices [5]. The suggested approach can be used across industries using standard definitions for any supply chain process.

The innovative Digital Customer feedback mechanism supports the selection of SCOR-based KPIs to measure the reliability of the Supply Chain. It enables joint analysis of internal and external business process on customer satisfaction and help the decision-makers select the best practices and tools to improve the overall customer journey in the garment and electronics industries.

The authors developed a generic model that enables measuring the current level of the Supply Chain reliability based on data collected from companies. The authors developed the SCOR database to map the description of problems with SCOR business processes, measure the most critical processes to be improved by SCOR third level KPI-is, select the possible corrective actions by applying the SCOR best practices, and assess their influence on Supply Chain reliability improvement by Bayesian Belief Network before implementation.

## 1.1 Contribution to Connected Cyber Physical Spaces

Cyber physical space consists of cyber physical systems. Cyber-Physical Systems (CPS) could be defined as technologies that manage interconnected systems and helps to change information between physical assets and digital systems [6, 7] that deal with the physical and informational aspects of the system [8].

The supply chain and proposed mechanism for digitalising it contribute to cyber physical spaces in many aspects. First, it means that physical forms of the supply chain also have a cyber part. We could easily find the relation between manufacturing 4.0 and digitalising the supply chain. In each step of the physical supply chain, background functions that are called in literature cyber supply chain processes to add value through information generation and analyse and optimise processes [9]. Cyber physical manufacturing faces challenges in the digital supply chain and the support of decision making. The proposed mechanism will help make quick decisions on improvement needed processes.

There are intentions to find a bonding solution with e-commerce logistics and cloud-based solutions for better utilisation of resources while reducing waiting and wastage [10]. It is essential to understand the solutions for bonding physical and cyberspace using accurate- time information as we will propose mechanisms that will sense reclamations in a real-time manner and monitor processes that are affected by the reclamations.

As part of the supply chain, there is manufacturing; recent research focuses on the Digital twins that are mentioned as one of the constructors of cyber physical systems in the context of industry 4.0 [11, 12]. It also means more decision systems are server based and will manage resources in a more centralised manner.

Industry 4.0 context is necessary to add key performance metrics for a sustainable supply chain [13]. It shows the importance of using the KPIs for greener and more sustainable production in the supply chain context. The proposed solution digitises supply chain metrics that will help identify chain problems faster and solutions that have proven successful.
Using cyber physical systems in supply chain management has shown promising results in performance in the chain [14]. The importance of digitalised side of the supply chain [15] states that it will help support supply chain performance and contribute to overall business performance.

As the supply chain idea is to direct manufacturing towards producing consumer value [16–18], digital solutions for consumers are needed. The proposed mechanism will cover consumer journeys and help to inform them of supply disturbances in advance.

In current research, authors propose a solution to collect physical items claims information, assess their influence on supply chain SCOR based performance metrics, consider the previously made decisions, and discover the best corrective actions to improve the existing business processes. Authors believe that the supply chain performance metrics could be fed with data with automated data collection or sensors. Authors introduce how physical items collected data could be used to construct a supply chain BBN model with standardised processes and metrics to assess process improvement efficiency.

## 1.2 SCOR Concept

The Supply Chain Council (SCC) takes the Supply Chain Operations Reference (SCOR) as the official supply chain management diagnostics standard. This is why we used it as a base for our research. The best practices and people skills sections of SCOR introduces the skills management framework within SCOR.

The critical point of the model is the ability to visualise the interconnections between all Supply Chain partners business processes to speed up the feedback provided to customers and select the most efficient improvement actions. Thus, the SCOR model effectively controls and diagnoses supply chains. For example, the SCOR framework for “Perfect Order Fulfilment” measured by second and third-level KPI-s metrics clearly shows bottlenecks and possible alternative solutions for building up a system for enterprises reliability level monitoring and efficient management.

## 1.3 Problem Description

Due to the supply chain complexity, failures frequently appear. The problem to be addressed through the research is that supply chain processes improvement and on-time, accurate delivery for customers are not achieved. Until now, enterprises track the reclamations and returns but do it from their company's perspective. For the enterprises it is unknown, how will different level KPI-s change, when particular process is improved. For the customer, it is unknown that when reclamation is submitted, are the reorders free of the same problems.

Problems that occur in the enterprise will change the KPI metrics. Our mechanism will find the processes that is related with the metrics and what best practice to implement to improve process and through it the KPI-s itself.

The research questions are:

* Does mechanism give improvement of reliability in sustainable supply chain by faster and more accurate process improvement?

* Does the mechanism give faster response about defects and confidence that defects do not occur on re order?
Hypothesis for thesis:

The suggested mechanism increases customer service quality and supports quick decision making for entire supply chain participants, which gives economic relations between enterprises.

## 2 Main Idea of Research and Used Methods

The mechanism suggested in the current research includes integration of the methods for supply chain failure minimisation and to decrease the failure elimination time.

<figure><img src="image_4.png" /><figcaption>Fig. 1. Processes improvement based on customer feedback</figcaption></figure>

The suggested mechanism is applied in the following order:

1. SCOR database development

2. Select the list of complaints resulting from improper use of products and faults in their delivery from the ERP system.

3. Connecting the list of complaints with SCOR process codes from SCOR-DB.
4. Efficiency analysis of SCOR best practices by using BBN. We should select the most suitable techniques from the suggested best practices from SCOR and analyse their influence on the supply chain effectiveness. For this aim we suggest use metrics of the considered supply chain processes.

Based on customer complaints, Fig. 1 visualises the steps for eliminating product defects and delivery accuracy in the supply chain. The decisions for more efficient use of a supply chain should be taken using the BBN analysis.

## 2.1 SCOR Database Development

The objective of a company manager is the optimisation of SC performances. To quickly find a solution for improving supply chain processes, our research group suggests a database structure based on SCOR standards, which operates with the supply chain data with the highest efficiency, see Fig. 2. As separate tables, it includes all SCOR sections (Processes, Performances, Best Practices, and People/Skills).

<figure><img src="image_5.png" /><figcaption>Fig. 2. SCOR-DB structure with an example of database tables filling</figcaption></figure>
The benefits of using the SCOR database are:

* The database allows quickly find a needed process due to the process's hierarchical structure and the SCOR attributes.

* The database suggests all applicable best practices for processes, metrics, and practice reference components (see Table 1.).

* SCOR database allows determining the impact of changing one metric on another performance metric.

* The database allows the addition of new processes and attributes to a specific process based on enterprise skills.

* DB techniques make maintaining large volumes of information more accessible and apply other data analysis methods.

<table><caption>**Table 1.** Example of SCOR-DB data</caption><thead><tr><td>Process KEY SCOR name</td><td>Reliability</td><td>Best Practices</td></tr></thead><tbody><tr><td>sD.13 Receive and verify Product by Customer</td><td>RL.2.4 Perfect Condition<br>RL.3.12% Of Faultless Installations<br><b>RL.3.55 Warranty and Returns</b><br><b>RL 3.42 Orders Delivered Defect Free Conformance</b></td><td>BP.147 Receiving Goods<br>Inspection<br>BP.089 Perfect Pick Put away</td></tr></tbody></table>

## 3 Validation of Customer Feedback Mechanism

The authors compare factors influencing the customer journey experience in garment and automotive electronics production. There are considerable differences between those sectors, as automotive electronics products are sold based on long lasting contracts to customers, and garments are a high mix low volume product, where the sales in driven by replenishment processes. The order amounts, product quality and delivery precision in those sectors vary significantly.

The authors use a scientific study based on qualitative and quantitative research methodology. During the research, the authors collected and analysed the data provided by commonly used internal processes related to delivery precision and external processes related to customer complaints, applied appropriate reliability KPIs and investigated the predicted efficiency of selected best practices implementation on the reliability measures of the company by using the BBN model.
## 3.1 Case Study from Garment and Electronics Industries

The authors analysed the data from four small garments and one automotive electronics production company. In both companies, the defects are usually discovered by the final consumer. In the garment sector, the defect can be caused by applying the wrong care procedures; either the products are destroyed by inappropriate use. Technological defects are avoided by quality checking in the production process and before delivery.

Every electronic device is checked thoroughly before delivery to achieve the high-quality level required by customers. Defects found should be corrected, and failed components should be replaced. Any deviation in quality causes a delay in payment or price reduction in the worst cases. External defects can be found in some cases by the end customer. It can be a defect when wrong care procedures are used or physical damage happens. Technical faults are avoided by quality checking in the production process and before delivery.

The number of defects is not similar. Defects are presented as a percentage of the total amount of orders (see Table 2.).

<table><caption>Table 2. Empirical data received from garment and automotive electronics companies</caption><thead><tr><th rowspan="2"></th><th colspan="6">Reliability target 99,5%</th></tr><tr><th colspan="3">Internal Processes and CDA</th><th colspan="3">External (Complaints)</th></tr></thead><tbody><tr><th scope="row">Garment Enterprise data</th><td>RL 3.45<br>Payment<br>documentation<br>accuracy</td><td>RL 3.43<br>Other<br>required<br>documentation<br>accuracy</td><td>RL 3.55<br>Warranty and<br>Returns</td><td>RL 3.42<br>Orders<br>Delivered<br>Defect Free<br>Conformance</td><td>RL 3.24<br>Orders/ lines<br>received<br>damage Free</td></tr><tr><th scope="row">Failures indexing</th><td>99%</td><td>98%</td><td>92,5%</td><td>95%</td><td>97%</td></tr><tr><th scope="row">Electronics Enterprise data</th><td>RL 3.49<br>Lack<br>of production<br>capacity</td><td>RL 3.20<br>Lack<br>of material<br>from an<br>external source</td><td>RL 3.27<br>Increased<br>customer<br>demand near<br>the time</td><td>RL 3.55<br>Warranty and<br>Returns</td><td>RL 3.12<br>% Of<br>Faultless<br>Installations</td><td>RL 3.21<br>% Of<br>Orders/ lines<br>received with<br>the correct<br>content</td></tr><tr><th scope="row">Failures indexing</th><td>12/32<br>37,5%</td><td>11/32<br>34,4%</td><td>6/32<br>8,1%</td><td>11/26<br>42,3%</td><td>12/26<br>46,2%</td><td>1/26<br>3,8%</td></tr><tr><th scope="row">% of failures for the<br>total amount</th><td>12*100/6935<br>= 0,17%</td><td>11*100/6935<br>= 0,15%</td><td>9*100/6935<br>= 0,13%</td><td>11*100/6935<br>= 0,15%</td><td>12*100/6935<br>= 0,17%</td><td>1*100/6935<br>= 0,01%</td></tr></tbody></table>

Reclaims/returns were aligned with SCOR model processes. After that, the performance metrics for the process are picked from the database. Performance metrics are divided into sections; this case study implements only the Reliability section, offering a view of supply chain reliability.

Third level SCOR KPIs were calculated based on the reclamations and were entered into the BBN model. The authors investigated third-level KPI impact to second-level KPI and, finally Perfect order fulfilment strategic target. Third-level KPI are connected to related business processes and the best practices applied to improve them.

Analysing third-level KPIs authors found out that both companies had reclamations, it could be found electronics producing company case has a lower percentage (0.15%) compared to the garment (7,5%). “Orders On Time in Full” KPI has shown better results
in electronics companies (99,41%) compared to garment (92.5%). It means garment companies had more reclamations and less on time and full complete orders deliveries. Overall, electronics manufacturing companies showed better results due to excellent quality management and developed standards in this sector. Also, the requirements for suppliers in the automotive industry are significantly higher than in the garment industry.

Technological defects cover the cases when non-appropriate components are used. Those defects usually are avoided by visual testing and repair process to avoid appearing at functional and final testing.

Table 3. shows the complaints that require consideration and action for their correction. For more effective supply chain it is necessary to complement the list of reclaims with the SCOR process code (KEY) and its name.

<table><caption>**Table 3.** Fragment of List of complaints with Reliability Performances (RL) and Best Practices (BP) in electronic company</caption><thead><tr><td>Case Nr</td><td>Product</td><td>Quantity</td><td>Customer complaint</td><td>Root cause analysis</td><td>SCOR Process KEY/Name</td><td>SCOR Perform. Code/Name</td><td>SCOR Best Practices Code/Name</td></tr></thead><tbody><tr><td>Case 1</td><td>Prod 1</td><td>2</td><td>Function failure</td><td>Component 1 component damaged, suspect that it happened in the router, monitoring</td><td>sM1.3: Produce and Test</td><td>RL.3.55 Warranty and Returns</td><td>BP.152 Automated Data Capture (ADC)</td></tr></tbody></table>

Figure 3 shows the generic model with processes and metrics suitable for the garment and automotive electronics industries. The model allows selecting enterprise specific processes and KPI-s using SCOR standard model. Everything that is not needed could take out of the model. It will give reusability aspects. Many enterprises could use the same model structure as the baseline to collect best practices and data. When new enterprises start using the model, already used best practices could be included and added (see Fig. 1).

Accordingly, to the SCOR model, every process has their best practices to improve it. On Fig. 3, they are shown on the bottom. Processes are found on the next tier, named “processes level”. When using the BP that is connected with the process by the arrow, showing the relationship, it should improve that process. Every process is related to many levels of metrics. In Fig. 3, they are shown from level 3 – 1, from bottom to top. Metrics could be fed with many kinds of data, in this paper, it is fed with reclamations, showing how many orders there were and how many of them had the defect. Those defects could situate with processes that influenced again the metrics above them.

For example, when a defect occurs on process “sD1.14 install product” and reclamations are submitted, the data is inserted about the reclamation to the named process.
Next, we could see on the network what is the best practice to improve the process and when it is improved, subsequent orders should be free of those defects. At the same time, we could see from the network how reclamations change the KPI-s that are situated with it.

<figure><img src="image_6.png" /><figcaption>Fig. 3. BBN structure generic model based on Fig. 2</figcaption></figure>

Figure 4 shows a case study specific model with reclamations data for textile industry. Percentages indicate the metrics influence level. A higher level means the influence on the second and first levels of metrics is significant. The highest impact on the second-level KPI for the textile case study is the third-level KPI, "RL 3.55 Warranty and Returns". The SCOR recommends applying the "BP.089 -Perfect Pick Put away" and B.147 Receiving Goods Inspection. After implementing those practices in Textile industry, it is expected to increase the Perfect Order Fulfilment from 54% to 56% eliminate the problems related to current returns (see Fig. 4).

The main challenge that small and medium electronics-producing enterprises face is an expectation of 99,8% of orders fulfilment in time. The total order amount for the electronics company during the considered period (one year) was 6935. The number of defects is not similar. Defects are presented as a percentage of the total failure for every SCOR process KEY (see Table 2).

The highest impact on the second-level KPI for the current case study is the third-level KPI, "RL 3.49 Schedule Achievement", RL 3.21 and RL3.24. The SCOR recommends applying the "BP.0172 -Production Scheduling Optimisation" and B.147 best practices. After implementing those practices in Electronics industry, it is expected to increase
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Evaluation of processes KPI BBN network after best-practice tools implementation for the Textile industry</figcaption></figure>

the Perfect Order Fulfilment from 99,56% to 99,61% eliminate the problems related to current returns (see Fig. 5).

<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Evaluation of processes KPI BBN network after best-practice implementation for the Electronics industry</figcaption></figure>

## 4 Discussion

Garment production faces more external defects than electronics companies [19, 20]. It could be better-quality management, but it could also be that the number of problems is more frequently rising in production. Internal difficulties in garment production are related to finishing products, as electronics are associated mainly with a lack of materials.
Electronics producing companies could have a higher rate of problems with the supply side and few issues with the operational side of the process. It shows the problem with supply chain reliability as missing parts lead to worse Perfect Order Fulfilment. It will mean that issues resonate in the supply chain [20].

As both case studies have different problems, the Best Practices also vary. Still, one common Best Practice, “Receiving Goods Inspection,” is suggested for both cases. It will lead to better quality input and gives an overview of the quality in the early stages before the production starts. After implementing best practise calculated by BBN.

Using the BBN network and finding the most KPI-s influencing process, will help enterprises to make quicker and more thorough decisions. When the most influencing process is found, the enterprise could improve it faster and it will lead to a situation when the customer makes re-order, named defects are eliminated.

As the proposed mechanism is capable of working in real time, data is sdigitalised and processes are connected with reclamations, it will give a customer a sense of security that re-ordering does not contain the same defects in the future.

# 5 Conclusions

The supply chain digitalisation mechanism and generic BBN template were developed and validated in garment and automotive electronics production companies. We present how to solve digitally improve the customer journey experience by providing faster responses to customer complaints and providing better quality products and services to the consumers. The supply chain could be viewed as a whole using the SCOR-based KPI model. Using real-time information decreases the time to link reclamations with a dedicated process, helping to improve the processes faster. The generic BBN template model improves the selection and efficiency validation of best practices on selected KPI [21]. The authors supplied the collected data to the BBN model to support decision making of what process improvement gives the best results on named KPIs.

The current study demonstrated how the solution can be applied in real life, however the study is limited to manufacturing companies, future research will study model applicability to different sector companies. The introduced solution enabled to increase the Supply chain reliability, by increasing the perfect order fulfilment measure. The solution developed by authors enabled garment companies to focus on core issues related to RL3.42 Orders delivered defect free conformance and the RL 3.55 Warranty and returns. After applying the best practices recommended by SCOR improved the garment company reliability by 4% and electronical company reliability by 0,5%.

It should be done to clear out similarities and specificities. Future work is to obtain more operational data for quantitative analysis. The current case study recommends implementing a digital solution that guarantees the “Perfect Pick and Put Away” and “Receiving Goods Inspection”. Authors validated that those best practices can be successfully applied to improve the strategic targets of garment and automotive electronics companies.

**Acknowledgement.** This research has been financed by the European Social Fund via the IT Academy programme.
References

1. Tortorella, G.L., Fetterman, D.: Implementation of industry 4.0 and lean production in Brazilian manufacturing companies. International Journal of Production Research 56(8), 2975-2987 (2017)

2. Lockamy, A., McCormack, K.: Modelling supplier risks using Bayesian networks. Ind. Manag. Data Syst. 112(2), 313-333 (2012). https://doi.org/10.1108/02635571211204317

3. Taghizadeh, H., Hafezi, E.: The investigation of supply chain's reliability measure: a case study. J. Industr. Eng. Int. 8, 22 (2012). https://doi.org/10.1186/2251-712X-8-22

4. APICS, Supply Chain Operations Reference Model SCOR, Version 12.0, 2017 APICS

5. Shevtshenko, E., Mahmood, K., Karaulova, T., Raji, I.O.: Multitier digital twin approach for agile supply chain management. In: Proceedings of the 2020 ASME International Mechanical Engineering Congress and Exposition, pp. 1–10 (2020)

6. Lee, J., Bagheri, B., Kao, H.A.: A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems. Manufact. Lett. 3, 18–23 (2015). https://doi.org/10.1016/j.mfg-let.2014.12.001

7. Baheti, R., H. G.: Cyber-physical systems. Impact Control Technol. 12(1), 161–166 (2011)

8. Chen, L., Dui, H., Zhang, C.: A resilience measure for supply chain systems considering the interruption with the cyber-physical systems. Reliab. Eng. Syst. Saf. 199 (2020). https://doi.org/10.1016/j.ress.2020.106869

9. Panetto, H., Iung, B., Ivanov, D., Weichhart, G., Wang, X.: Challenges for the cyber-physical manufacturing enterprises of the future. Annual Rev. Control 47, 200–213 (2019). https://doi.org/10.1016/j.arcontrol.2019.02.002

10. Kong, X.T.R., et al.: Cyber physical ecommerce logistics system: an implementation case in Hong Kong. Comput. Industr. Eng. 139 (2020). https://doi.org/10.1016/j.cie.2019.106170

11. Park, K.T., Son, Y.H., Noh, S.D.: The architectural framework of a cyber physical logistics system for digital-twin-based supply chain control. Int. J. Prod. Res, 1-22 (2020). https://doi.org/10.1080/00207543.2020.1788738

12. Tao, F., Qi, Q., Wang, L., Nee, A.Y.C.: Digital twins and cyber-physical systems toward smart manufacturing and industry 4.0: correlation and comparison. Engineering, 5(4), 653-661 (2019). https://doi.org/10.1016/j.eng.2019.01.014

13. Morella, P., Lambán, M.P., Royo, J., Sánchez, J.C., Corrales, L.D.C.N.: Development of a new green indicator and its implementation in a cyber-physical system for a green supply chain. Sustainability (Switzerland) 12(20), 1-19 (2020). https://doi.org/10.3390/su12208629

14. Frazzon, E.M., Silva, L.S., Hurtado, P.A.: Synchronising and improving supply chains through the application of cyber-physical systems. IFAC-PapersOnLine 28(3), 2059-2064 (2015). https://doi.org/10.1016/j.ifacol.2015.06.392

15. Lee, K.L., Azmi, N.A.N., Hanaysha, J.R., Alzoubi, H.M., Alshurideh, M.T.: The effect of digital supply chain on organisational performance: an empirical study in Malaysia manufacturing industry. Uncertain Supply Chain Manag. 10(2), 495-510 (2022). https://doi.org/10.5267/j.uscm.2021.12.002

16. Esper, T. L., et al.: Everything old is new again: the age of consumer-centric supply chain management. J. Bus. Logist. 41(4), 286-293 (2020). https://doi.org/10.1111/jbl.12267

17. Min, S., Zacharia, Z.G., Smith, C.D.: Defining supply chain management: in the past, present, and future. J. Bus. Logist. 40(1), 44-55 (2019). https://doi.org/10.1111/jbl.12201

18. Stolze, H.J., Mollenkopf, D.A., Flint, D.J.: What is the right supply chain for your shopper? Exploring the shopper service ecosystem. J. Bus. Logist. 37(2), 185-197 (2016). https://doi.org/10.1111/jbl.12122

19. Murumaa, L., Shevtshenko, E., Karaulova, T., Mahmood, K., Popell, J.: Supply chain digitalisation framework for service/product satisfaction, modern materials and manufacturing.
In: IOP Conference Series, Materials Science and Engineering; Bristol, vol. 1140, p. 012041
(2021). https://doi.org/10.1088/1757-99X/1140/1/012041

20. Shevtshenko, E., Maas, R., Murumaa, L., Karaulova, T., Raji, I.O., Popell, J.: Digitalisation of supply chain management system for customer quality service improvement. J. Mach. Eng. 22

21. Shevtshenko, E., Wang, Y.: Decision support under uncertainties based on robust Bayesian networks in reverse logistics management. Int. J. Comput. (2009)