Digital Transformation Models Applied to the Supply Chain in the Context of Industry 4.0

Digital transformation (DT) is primarily responsible for changing the industry, hence the expression Industry 4.0, in connection with the so-called fourth Industrial Revolution (^{Davenport & Harris, 2017}). An inherent aspect of DT is its degree of maturity based on its implementation in companies. There are several models to measure maturity, notably the one proposed by ^{Schumacher et al. (2019}), which guides companies through a systematic procedure, considering their contact with Industry 4.0 and determining specific fields of action, realization projects and roadmaps.

The supply chain model for Industry 4.0 (^{Garay et al., 2020}) has four pillars: virtual value creation and digital integration, material flow, real-time information and products and services flow; virtual value creation, value availability, digital service value, and digital integration value prevail as integrating elements to achieve this transformation, and data center service management (DCSM) acts as a guiding axis in the change processes.

The e-logistics model also deserves special mention. It aims to increase the traffic of end customers, carriers and companies related to freight transportation; its ultimate goal is to generate a competitive price via reverse online auction, based on the systematization of processes using hardware, software and web platforms (^{Valderrama et al., 2018}). It should be noted that aspects such as the level of service, customization and compliance with delivery schedules were disregarded.

Strategic Model to Improve Last Mile Delivery Performance in E-Commerce Parcel Delivery

Last mile logistics is the most expensive and time-consuming stage in the delivery process (^{Duarte et al., 2017}). It is an activity specific to asset-light logistics companies and is the most important factor in the relationship between the e-commerce retailer and the end customer, as the link between the two actors can be established or broken, directly affecting the customer’s decision to place a new order. ^{Bopage et al. (2019}) proposed a three-component strategic model to address the challenges proactively with an effective logistics strategy to drive the operating system in order to improve performance and increase the competitiveness of e-commerce. The strategy component refers to the various actions that converge in the organization’s overall strategy, including business planning and technological and human resources aimed at achieving operational excellence, which is based on the company’s capabilities and the competencies of the available workforce. The last mile operational performance component refers to the use of the organization’s resources, the quality of the services offered, lean systems related to operational efficiency and its impact on the environment. Finally, the benefits component refers to cost reduction, customer satisfaction, retailer satisfaction and external factors.

Model for Measuring the Competitiveness of Companies

The constant pursuit of competitiveness among service companies has led to the identification of factors that can impact it individually or as a whole, the most relevant of which are quality, price, technology, training and distribution channels Bonales et al., 2015), and some other factors such as innovation and productive flexibility ((^{Ramírez & Ampudia, 2018}).

Methodology of Digital Transformation in Peruvian Asset-Light Logistics SMEs

The proposed digital transformation methodology leads to the design of a model that considers the improvement of six factors related to the logistics performance index (LPI) as inputs. The first is strategy, which provides all relevant information about the market sector within which the company operates, as well as the needs of customers, competitors and factors that may affect performance over time. Then comes quality, related to satisfying the needs of current customers and identifying new needs in the future. Then, productivity, related to the efficiency and effectiveness of internal processes, for which reason the existing technology and the performance of internal processes are taken into account. Then, traceability, related to the identification of the status of each goods or services requested to the SMEs, considering the customer’s need for information. Then, technical level, based on the current and future technical capabilities of the organization’s employees. And the last one is profitability, aimed at achieving the economic objectives. As for the outputs, the model is designed to obtain a digital strategy, optimize processes and align and develop competitive advantages (Figure 1).

Source: Prepared by the authors

Figure 1 Digital Transformation Model to Increase the Competitiveness of SMEs in the Logistics Sector of Peru.  

In the digital transformation model, it is important to consider the three types of processes present in companies: strategic, central and support processes (^{Ricardo et al., 2015}). The digital plan, which must be aligned with the SME’s strategy, is developed in the strategic stage of the model; this is the first stage of the model and methodology that will allow the organization’s resources to be oriented efficiently, based on an analysis of the environment that influences the organization’s performance. The core processes aim to increase productivity and profitability of operations by structuring databases, omnichanneling and digitization of operational processes. Finally, the support processes aim to contribute to the good performance of the organization by means of its digital transformation (^{Bravo, 2009}).

The digital transformation methodology in Peruvian logistics SMEs is broken down as follows:

Methodology for Strategic Process

There are three main stages in the strategic process. In the first stage, an analysis of the environment is conducted focusing on the needs of customers and on how they are being met by the company. The second stage involves the alignment of the strategy expressed in a strategic plan that addresses short, medium and long-term components; a comparison of the current state of the strategy with the desired state is conducted to identify opportunities for improvement and determine the stages or actions that should be implemented to address the target market. Finally, the third stage entails deciding which actions to undertake and which will be prioritized; the digital plan is thus established, aimed at aligning current and future resources (human, infrastructure and technology) to the needs of customers and the external factors that affect the performance of asset-light logistics SMEs (Figure 2).

Source: Prepared by the authors

Figure 2 Digital Transformation Methodology for Strategic Processes. 

Methodology for Core Processes

The methodology for the core processes begins with the stage known as the structuring of databases of customers destined to receive goods or services. Its purpose is to streamline internal processes and capture the history of each recipient of the goods or services to be distributed based on the response levels of previous orders, as well as to facilitate the distribution process. Address inconsistencies are also detected at this stage, and transmitted to SME customers for correction, thus increasing the odds of delivery and reducing the number of distribution attempts, and ultimately improving productivity. In the second stage occurs the digitation of operational processes, which correlates with the traceability of each shipment requested to the SME. To this end, goods or services must be identified by means of a unique code, allowing customers or users of logistics services to identify the status of each request and its history. Various means of communication are used for this purpose, including websites and apps, among others. Finally, the third stage concerns omnichanneling, that is, communication with the recipients or customers of light-asset logistics SMEs in a two-way flow of information, for which both the applications and the way of accessing traceability information must contemplate multiple communication channels (Figure 3).

Source: Prepared by the authors

Figure 3 Digital Transformation Methodology for Core Processes. 

Methodology for Support Processes

The methodology for the support processes was structured in three stages. The first is the digital training process and involves the development of digital competencies in workers. The second is the supplier selection process, focusing on suppliers involved in the distribution processes that have an impact on service quality. The third stage involves technological adequacy, which involves the promotion of digital culture in light-asset logistics SME, and the assessment of the effectiveness of technological implementations (Figure 4).

Source: Prepared by the authors

Figure 4 Digital Transformation Methodology for Support Processes. 

Digital Transformation Level

The DTI was determined according to the achieved level of the SMEs under study. Most of them were classified as intermediate (58.82 %), followed by revolutionary (18.82 %), competitive (14.51 %) and explorer (7.84 %) (Table 3).

There is a low level of digital transformation, since approximately 66.66% of the companies do not surpass the intermediate level, hindering the promotion of e-commerce in Peru, as the restrictions that discourage it are related to traceability and the costs of the service.

Digital Transformation Model

The results showed that all variables are highly correlated with the level of digital transformation of each light-asset logistics SME; therefore, by improving such variables, the level of digital transformation and, thereby, the competitiveness of each SME would increase. It should be noted that the highest correlation is observed in the productivity variable (0.9260) and the lowest in the profitability variable (0.8588) (Table 4).

Multiple linear regression was used to determine the coefficients and the equation for predicting the results related to the level of digital transformation, given that each variable affects competitiveness differently. It was found that traceability and productivity are the most relevant variables and the ones that exert the greatest influence on the final model (Table 5).

Finally, considering the above coefficients, the model was described with the following equation (1):

Validation of Results Obtained by the Application of the Model and Methodology

To validate the impact of the results obtained, normality tests were performed to determine the extent to which the distribution of the observed data differed from that expected under a normal distribution with the same mean and standard deviation. The Kolgomorov-Smirnov and Shapiro-Wilk tests were used for this purpose (Table 6). For the purpose of optimizing data processing, a code was assigned to each variable as follows: code R2, corresponding to quality variable (RIC); code R3, corresponding to productivity variable (RIP); code R4, corresponding to technical level variable (RIN); code R23, corresponding to variables RIC, RIP; code R134, corresponding to the variables RIE, RIP, RIN; code R234, corresponding to the variables RIC, RIP, RIN; code R2345, corresponding to the variables RIC, RIP, RIN, RIT; and code R12345, corresponding to the variables RIE, RIC, RIP, RIN, RIT.

The following hypotheses were used for statistical testing:

H_o: The difference of indexes has a normal behavior.

H_a: The difference of indexes does not have a normal behavior.

As the significance level is less than 0.05, which is the established error for the study, the null hypothesis is rejected, and the alternative hypothesis is accepted. Consequently, the behavior of each variable was analyzed using parametric and non-parametric tests.

Analysis Using Parametric Tests

Upon verifying that the variables had parametric behavior, we compared the means of each of them using Student’s t-test for each variable and stating the following hypotheses:

H_o: DTIB = DTIA

H_a: DTIB ≠ DTIA

Where: H_o is the null hypothesis, H_a is the alternative hypothesis, DTIB is the digital transformation index before implementation, DTIA is the digital transformation index after implementation.

Test for Changes in the Variables Under Study

The results show the growth of the mean in the digital transformation index, as for code R12345 with 48.39%, code R134 with 37.42%, code R23 with 5.87%, code R234 with 10.03%, code R2345 with 24.78% and code R4 with 24.78%.

The use of the methodology was validated using the values of the digital transformation index obtained by the light-asset logistics SMEs before the application of the model (DTIB) and after the application (DTIA), as well as the calculation of the deviations and errors in each of the indexes through the Student’s t-test (Table 7).

The statistical results reject the null hypothesis (significance value of less than 0.05), proving that the methodology provoked changes in the variables under study. It can therefore be concluded that the variables described are directly impacted by the level of digital transformation, as improvement of processes and implementation of actions based on the needs of light-asset logistics SMEs was demonstrated (Table 8).

Analysis using nonparametric tests.

Regarding the non-parametric analysis, the Wilcoxon statistical test was used, and the hypotheses posed were as follows:

H_o: DTIB = DTIA

H_a: DTIB ≠ DTIA

Where: H_o is the null hypothesis, and H_a is the alternative hypothesis.

The treatment led to the analysis of the RIP (productivity) and RIC (quality) variables, yielding a 1.15% increase in the mean of digital transformation index for the first variable and 0.53% for the second variable (Table 9).

The Wilcoxon test results showed a significance level of less than 0.05; therefore, the null hypothesis was rejected, demonstrating the positive impact of the digital transformation methodology (Table 10).

Finally, competitiveness was calculated. It revealed important changes in the SMEs’ level: 5% of SMEs in the explorer level transitioned to the intermediate level, and 3.14% of SMEs in the intermediate level transitioned to the revolutionary level. In terms of the increase in competitiveness, SMEs in intermediate stage registered an increase of 8.59%, SMEs in revolutionary stage registered an increase of 2.62% and SMEs in competitive stage registered an increase of 2.60% (Table 11).