# Identifying the Opportunities for Enhancing the Digital Readiness Level of the Supply Chain

Chiara Cimini, Fabiana Pirola, Sergio Cavalieri

► To cite this version:

Chiara Cimini, Fabiana Pirola, Sergio Cavalieri. Identifying the Opportunities for Enhancing the Digital Readiness Level of the Supply Chain. IFIP International Conference on Advances in Production Management Systems (APMS), Aug 2020, Novi Sad, Serbia. pp.295-303, 10.1007/978-3-030-57993-7_34. hal-03630882

HAL Id: hal-03630882

https://inria.hal.science/hal-03630882

Submitted on 5 Apr 2022

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Identifying the opportunities for enhancing the digital readiness level of the supply chain

Chiara Cimini¹[0000-0001-8274-9821] Fabiana Pirola¹[0000-0003-4633-7568] and Sergio Cavalieri¹[0000-0002-7598-0379]

¹ Department of Management, Information and Production Engineering, University of Bergamo, Viale Marconi 5, Dalmine (BG), Italy
chiara.cimini@unibg.it, fabiana.pirola@unibg.it, sergio.cavalieri@unibg.it

**Abstract.** Assessing the digital readiness of companies is becoming crucial to undertake a successful journey towards the digitalization of industry. Recently, the attention of both scholars and practitioners has turned to an extension of the assessment to the whole Supply Chain (SC). Given the importance of reaching a high level of integration and digital readiness in all the various actors that collaborate in the SC, some Digital Supply Chain (DSC) assessment models have been proposed mainly in the consulting field. However, to fully exploit the benefits of measuring the digital readiness of a SC, companies need also to be supported in the identification and prioritization of the most relevant opportunities triggered by the deployment of DSC projects. To this purpose, this paper presents a framework that links the potentials of DSC implementations with the SC operation processes, highlighting the most suitable technologies to deploy them. The framework has been conceived starting from a literature review and aims at providing a valuable tool to all the stakeholders involved in the transformation towards Digital Supply Chains.

**Keywords:** Digital Supply Chain, Readiness Assessment, Digital Transformation

## 1 Introduction

The business landscape has been heavily broken out by the emergence of the COVID-19 pandemia which is affecting all the societies and economies at the global level, changing promptly and profoundly the lifestyle of people, altering the market dynamics, disrupting dramatically the operations of companies and, at a large extent, of the supply chains. These extreme and severe changes have further fueled some needs and opportunities which were already nurturing in the last years, regarding in particular the digital transformation of the organizations to enable smart working of white collars, on-line controlling and monitoring of production systems, remote assistance of machines, thus mitigating the downfall effects triggered by reduced mobility of people,
workers and goods. However, the appropriate adoption and use of these new technologies are not immediate, but require a deep understanding and companies must be prepared for them [1].

One of the main requirements is the achievement of a certain level of digital readiness and this clearly makes it extremely important to identify a valid and reliable model for measuring it. Several digital readiness assessment and maturity models have been developed, mainly in the consulting and academic contexts [2]. Nevertheless, they tend to focus on the single firm and not on the value chains in which it operates. This is the reason why the attention has been shifting to an extension of the analysis at a supply chain (SC) level, encompassing the upstream and downstream sides of a focal company.

This new vision stems from the idea that significant advantages, such as an additional increase in efficiency, competitiveness and mitigation of risks, can only be achieved with a high level of integration and digital readiness of all the various actors collaborating in the SC.

This paper devotes its focus on the phase after Digital Readiness Assessment (DRA), that is when companies are required to define the main improvement areas in the SC. After briefly defining the basic concepts of Digital Supply Chain (DSC) and readiness assessment (Section 2), the paper presents a framework, which merges the technological perspective with the operational one, providing a tool for practitioners that want to enhance the digital readiness of their SC (Section 3). Some limitations and further improvements of the conducted research are finally discussed (Section 4).

# 2 Background

## 2.1 Digital supply chain

Industry 4.0 is a new industrial trend in which process integration and product connectivity allow to increase performance [3]. After e-commerce, which has completely changed the way people buy and sell products all over the world, the SC is evolving towards DSC, that in literature is considered according to two main definitions.

The first definition concerns the digital product ecosystem, or the chain of companies interested in providing digital products that were originally physical, such as eBooks and MP3s. The physical product must pass through the SC to reach the consumer; in the same way, the digital products must pass processing phases before the final consumer can use them [4]. This definition focuses more on the product: in fact, its digital nature contributes to the creation of “digital supply chains”; consequently, their construction derives from the needs and characteristics of the product.

In the second definition, instead, the digital nature is not in the product, but in the process of designing, producing and supplying goods. Alicke et al. [5] define the DSC as the next-generation SC which relies at the field level on interconnected systems, automatization of plants, collaborative digital platforms.

For these reasons, the second definition of DSC represents an integration between the traditional concepts of SC and the digital transformation, as enabling technologies
generate new opportunities and trigger new managerial SC models and processes. In literature, it has been shown that the use of technologies is positively correlated to the joint performance of the supplier and the focal company; familiarity with new digital trends allows companies' openness to share core information with their suppliers, and vice versa [6].

In a nutshell, the digital transformation of the SC is based on new innovative approaches and new features, such as:

* Speed, which allows the delivery of goods in useful times and faster movements in internal and external logistics, and in the long term even to possible "predictive shipment" (patented by Amazon) [5].

* Global connectivity, i.e. the creation of effective global hubs to provide goods and services locally, supporting integration [7], collaboration [8] and coordination [9].

* Intelligence, which allows converting digital data into useful information (automated execution, actionable insights and accelerated innovation) [10, 11].

* Flexibility, which consists in using the information to react to problems or changes instantaneously; for example, planning becomes a continuous process that reacts dynamically to the constraints and requirements of evolution [5].

* Transparency, that allows anticipating disruptions thanks to a wide information visibility [10].

* Scalability and Granularity, that allows to adapt the capacity of the SC to the "peak and trough" volumes of market demand [5].

* Sustainability, supporting the compliance and responsibility with environmental and social norms [5].

## 2.2 DSC Readiness Assessment

To develop such a DSC, Büyüközkan et al. [11, p.168] proposed a framework considering three different steps that are “vital for organizational alignment”. The first step for digitalization is the definition of a digital strategy that exactly indicates what the company wants to achieve in terms of DSC features. Depending on the goal and its vision, as a second step, the company formulates the implementation strategy that allows it to achieve its objective(s) [13]. It considers "how, where, when and who" for the realization of the objective(s). Different aspects of SCM are affected by digital transformation and need to be defined in this step (e.g., integration, processes, automation, and analytics). The technological infrastructure must support the company strategy. Defining the nature of the infrastructure means better understanding the technological priorities and requirements (step 3) [14].

In order to support companies in these steps, DRA represents a valuable tool to suggest both the digital strategy definition and implementation.

Readiness is defined as the state of “being both psychologically and behaviorally prepared to take action (i.e., willing and able)” [15] and is strictly connected to a change process. The systemic analysis of an organization's ability to cope with transformational process or change is defined as measuring or assessing readiness, since readiness assessment aims at identifying the risks, the opportunities, and the potential challenges that
might arise when change processes (concerning new processes, procedures, organiza-
tion, etc.) are implemented within an actual organizational context [16]. Changing read-
iness means having the right conditions and resources to support certain company ini-
tiatives, to have clear goals and visions of change and to have objectives and the moti-
vation to face it. As a result, assessing the readiness is fundamental, as companies often
do not know their position towards their competitors and changes in general. Moreover,
readiness assessment models can be used for comparison and benchmarking.

In the Industry 4.0 context, both scholars and consultancy groups have proposed
several readiness assessment tools or maturity models (e.g.,[16, 17]). However, they
often focus on a single enterprise, maybe with a distinction between SMEs and large
companies. So far, few researches deal with the DSC readiness. The assessment devel-
oped by the Digital Supply Chain Institute [18] has been applied to some companies
operating in the manufacturing and service industry. The objective of this assessment
is to analyze the SC from an internal focus to a customer-supplier focus. The assessment
is divided into four areas (Demand, People, Technology and Risk), each area containing
three levels. The DSC assessment developed by the consulting group Tecsys [19] aims
at analyzing the critical milestones for an end-to-end SC, i.e. Executive Buy-In, Finan-
cial Foundations, Infrastructure Essentials, SC Operations, Cross-Functional Collabo-
ration and Strategic Momentum. These ones are used to develop a roadmap to support
digital transformation. Other assessment tools have been provided by the other consult-
ing groups as well, such as Gartner [20] and McKinsey [5].

All these DRA represent significant tools for companies to identify their current
level of digital readiness, but they lack in providing a clear view of the improvements
that companies need to implement to increase their readiness level. Given this gap, fur-
ther reflections about the post-assessment phase are required. For this reason, in the
next paragraph a framework for recognizing the most interesting opportunities offered
by the new technologies to improve the digital supply chain readiness is proposed.

3 The DSC improvements framework

In the McKinsey assessment [5], a Compass map with a series of improvement levers
classified in six categories (planning, strategy, collaboration, order management, per-
formance management and physical flow) originating from the DSC is presented.
To conceive our framework, we took the Compass map as a starting point to identify
the main improvement areas, merging this perspective with other two dimensions: the
Industry 4.0 technologies and the SC processes as defined by the Supply Chain Opera-
tions Reference (SCOR) model [21]. Similar approaches have been used in literature to
build systematic literature mapping[22]. In this way, the framework provides the com-
pany with an overview of the digital results that can be potentially reached, indicating
at the same time the processes that would benefit it and the technological solutions
necessary to implement them successfully.

The resulting framework (represented in Fig. 1) has been developed based on aca-
demic articles, conference proceedings, and the SCOR model (version 12.0).
<table><caption>Table 1. Digital achievements description</caption><thead><tr><th>Category</th><th>Digital Achievements</th><th>Description</th></tr></thead><tbody><tr><td rowspan="2">Strategy</td><td>Micro segmentation</td><td>It refers to the monitoring and the data collection related to demand, behaviors and relevant information through which it is possible to identify smaller segments of customers to whom offering products, services and promotions with high personalization.</td></tr><tr><td>Proactive customer service</td><td>It allows anticipating problems encountered by the consumer, for example, by creating products that are able to record data and information about their operation or utilization and communicate them to the suppliers in advance.</td></tr><tr><td rowspan="2">Planning</td><td>Predictive analytics for scenario planning</td><td>It consists in exploiting the potential of a large quantity of data to be able to predict possible future scenarios and act accordingly to obtain the best possible economic return or to reduce any losses.</td></tr><tr><td>Closed-loop planning</td><td>It allows the transformation of planning activities into a continuous and dynamic process.</td></tr><tr><td>Physical Flow</td><td>Automation of warehousing</td><td>Automation technologies can increase the speed of picking operations, optimize the space and the layout of the warehouse and reduce human error in inventory operations, transforming the warehouse from a cost center to a source of competitive advantage.</td></tr><tr><td rowspan="3">Order mgmt.</td><td>Digital payments and invoices</td><td>They allow companies to capitalize on aspects such as repayments and discounts due to upfront payments, adequately manage their supplier base and reduce invoice processing and payment costs.</td></tr><tr><td>Fraud detection</td><td>It deals with the identification of possible frauds such as failure by suppliers to comply with contractual conditions, billing of non-delivered material or even corruption, thanks to the availability and correct analysis of a huge quantity of data.</td></tr><tr><td>Online order monitoring</td><td>It allows companies to improve the service offered to customers who are able to monitor at any time different types of information constantly updated on their orders.</td></tr><tr><td rowspan="3">Collaboration</td><td>Product and design process optimization</td><td>It refers to the potential of new technologies in increasing the coordination and cooperation both internally with company functions such as marketing and production, and externally by involving suppliers in the product development cycle.</td></tr><tr><td>End-to-end connectivity</td><td>It allows that different actors at different levels of the supply chain can obtain data and visibility in real-time, enhancing communication and coordination of activities and processes.</td></tr><tr><td>Online transparency</td><td>It concerns the sharing of information with the partners in the SC, referring to Key Performance Indicators (e.g., service level) or real-time data (e.g., the position of a truck carrying a specific order).</td></tr><tr><td rowspan="2">Performance mgmt.</td><td>Digital performance management</td><td>It refers to the use of models that automatically and continuously process data and detect real-time performances to analyze and improve efficiencies.</td></tr><tr><td>Automated root cause analysis</td><td>It refers to the implementation of machine learning techniques to automatically identify problems, their causal dependencies and their root causes.</td></tr></tbody></table>

Each cell of the framework is based (and therefore justified) on an academic contribution or, rarely, on a web article, which explain the correlation between the three elements described above. The sources that have been used are marked in the cells with
an acronym (letters S for SCOR or T for technologies and a sequential number) and can
be consulted at the link:
https://drive.google.com/open?id=1h9ZHJ7pqzcCzn_cTxRMNiPIT52ohvZEJ.

Observing the framework, it is possible to notice that the proposed digital achieve-
ments affect mainly four out of the five SCOR processes. The process Return can be
improved introducing only IoT technologies and data analytics to obtain data from the
products' lifecycle, enabling improved customer assistance and planning activities.

Actually, it is quite evident that IoT and Big Data analytics, along with Autonomous machines, are the technologies more relevant to the realization of almost all the digital achievements. Also Simulation seems to support several digital achievements and can affect notably the companies' planning activities, potentially increasing in a significant way the performances of all the SC. Conversely, technologies such as Augmented Reality or Additive manufacturing seem to have less application in the DSC.

4 Conclusion

The paper presents a framework on the adoption of digital solutions for the optimization of SC processes, showing the advantages that they would entail and the business pro- cesses that would be involved and would benefit from it. Some limitations and further development of this research can be envisioned. First, the framework is conceived as a tool to be used in a following phase respect to a DRA of the SC. To this purpose, the digital achievements could be enriched and reorganized according to specific assess- ments, refining the framework also according to different perspectives (e.g., suppliers, customers). Future research will test the validity of the framework, evaluating how it could be applied jointly with a readiness assessment. In a second instance, the frame- work can be improved merging financial aspects of the technology implementations, evaluating technical feasibility along with returns on investments.

References

1. Nasution, R.A., Rusnandi, L.S.L., Qodariah, E., Arnita, D., Windasari, N.A.: The Evalua-
tion of Digital Readiness Concept: Existing Models and Future Directions. AJTM. 11, 94–
117 (2018). https://doi.org/10.12695/ajtm.2018.11.2.3.

2. Colli, M., Berger, U., Bockholt, M., Madsen, O., Møller, C., Wæhrens, B.V.: A maturity assessment approach for conceiving context-specific roadmaps in the Industry 4.0 era. Annu. Rev. Control 48, 165–177 (2019). https://doi.org/10.1016/j.arcontrol.2019.06.001.

3. Dalenogare, L.S., Benitez, G.B., Ayala, N.F., Frank, A.G.: The expected contribution of Industry 4.0 technologies for industrial performance. Int. J. Prod. Econ 204, 383–394 (2018).

4. Hines, T.: From analogue to digital supply chains: implications for fashion marketing. In: Fashion Marketing: Contemporary Issues. 26-47. Routledge (2001).

5. Alicke, K., Rachor, J., Seyfert, A.: Supply Chain 4.0 – the next-generation digital supply chain. McKinsey. (2016).
6. Scuotto, V., Caputo, F., Villasalero, M., Giudice, M.D.: A multiple buyer – supplier relationship in the context of SMEs’ digital supply chain management. Prod. Plan. Control. **28**, 1378–1388 (2017). https://doi.org/10.1080/09537287.2017.1375149.

7. Rai, A., Patnayakuni, R., Seth, N.: Firm Performance Impacts of Digitally Enabled Supply Chain Integration Capabilities. MIS Quart. **30**, 225–246 (2006).

8. Min, S., Roath, A.S., Daugherty, P.J., Genchev, S.E., Chen, H., Arndt, A.D., Glenn Richey, R.: Supply chain collaboration: what’s happening? Int. J. Log. Manag. **16**, 237–256 (2005). https://doi.org/10.1108/09574090510634539.

9. Iddris, F.: Digital Supply Chain: Survey of the Literature. Int. J. Bus. Res. Manag. **9**, 47–61 (2018).

10. Hanifan, G., Sharma, A., Newberry, C.: The Digital Supply Network. A new paradigm for Supply Chain Management. **8** (2014).

11. Trkman, P., McCormack, K., de Oliveira, M.P.V., Ladeira, M.B.: The impact of business analytics on supply chain performance. Decis. Support Sys. **49**, 318–327 (2010). https://doi.org/10.1016/j.dss.2010.03.007.

12. Büyüközkan, G., Göçer, F.: Digital Supply Chain: Literature review and a proposed framework for future research. Comput. Ind. **97**, 157–177 (2018).

13. Pradabwong, J., Braziotis, C., Tannock, J.D.T., Pawar, K.S.: Business process management and supply chain collaboration: effects on performance and competitiveness. Supply Chain Manag. **22**, 107–121 (2017). https://doi.org/10.1108/SCM-01-2017-0008.

14. Townsend, M.A.Y.A.: Priorities for building organizational infrastructure, (2006).

15. Weiner, B.J.: A theory of organizational readiness for change. Implement. Sci. **4**, 67 (2009).

16. Pirola, F., Cimini, C., Pinto, R.: Digital readiness assessment of Italian SMEs: a case-study research. J. Manuf. Technol. Manag. (2019). https://doi.org/10.1108/JMTM-09-2018-0305.

17. Vivares, J.A., Sarache, W., E., H.: A maturity assessment model for manufacturing systems. J. Manuf. Technol. Manag. **29**, 746–767 (2018). https://doi.org/10.1108/JMTM-07-2017-0142.

18. DSCI: Digital Supply Chain Transformation Guide: Essential MetricsD, https://www.dscinstitute.org/assets/documents/Digital-Supply-Chain-Transformation-Guide-Essential-Metrics_DSCI_Oct2.pdf, (2017).

19. Tecsys: IDN Supply Chain Transformation, https://infohub.tecsys.com/idn-supply-chain-transformation-readiness-assessment-tool, (2018).

20. Poole, C., Steutermann, S., Lord, P., Ruggles, K., Chadwick, K., Dominy, M., Howard, V., Loveland, J.: Gartner’s Demand-Driven Model for Supply Chain Maturity, (2015).

21. APICS: SCOR Supply Chain Operations Reference Model, https://www.apics.org/docs/default-source/scc-non-research/apicsscc_scor__quick__reference__guide.pdf, (2017).

22. Wolf, H., Lorenz, R., Kraus, M., Feuerriegel, S., Netland, T.H.: Bringing Advanced Analytics to Manufacturing: A Systematic Mapping. In: Ameri, F., Stecke, K.E., von Cieminski, G., and Kiritsis, D. (eds.) Advances in Production Management Systems. Production Management for the Factory of the Future. pp. 333–340. Springer International Publishing, Cham (2019). https://doi.org/10.1007/978-3-030-30000-5_42.

23. Rüßmann, M., Michael, Lorenz, M., Gerbert, P., Waldner, M., Justus, J., Engel, P., Harnisch, M.: Industry 4.0: The future of productivity and growth in manufacturing industries. Boston Consulting Group. **9**, (2015).