# Lean as a Facilitator for AGVs Implementation: A Case Study

Stefano Frecassetti, Matteo Ferrazzi, Alberto Portioli-Staudacher

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Stefano Frecassetti, Matteo Ferrazzi, Alberto Portioli-Staudacher. Lean as a Facilitator for AGVs Implementation: A Case Study. 8th European Lean Educator Conference (ELEC), Nov 2022, Galway, Ireland. pp.95-107, 10.1007/978-3-031-25741-4_10. hal-04735681

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# Lean as a Facilitator for AGVs Implementation: A Case Study

Stefano Frecassetti¹[0000-0002-9649-314X] and Matteo Ferrazzi¹[0000-0001-9035-0773] and Alberto Portioli-Staudacher¹[0000-0002-9807-1215]

¹ Politecnico di Milano, Department of Management, Economics and Industrial Engineering, Milano, Italy

stefano.frecassetti@polimi.it

**Abstract.** The attention toward integrating Lean and Industry 4.0 (I4.0) has increased in the last few years. Nevertheless, the focus was mainly on how I4.0 can support Lean Management practices and how Lean tools can be digitalised through I4.0 technologies. Some authors have pointed out the need for further research on how Lean Management can support and benefit the introduction of Industry 4.0 technologies in firms. In particular, some authors supposed that Lean has a facilitating effect towards I4.0 implementations. This work aims to enrich the existing knowledge, stressing the support Lean Management can give to implementing I4.0 technologies. This article shows, through a case study conducted using the A3 framework, how Lean has been applied to reorganise a warehouse to facilitate the implementation of Automated Guided Vehicles (AGVs), showing the benefits of this approach.

**Keywords:** Lean, A3, Warehouse Management, AGV, Digitalisation

## 1 Introduction

At the beginning of this century, digital technologies were widely introduced, on a global scale, substantially changing how companies behave. This was particularly true in the last decade when the evolution of technologies had become continuous and unstoppable, forcing companies to adapt to this rapidly changing environment. On the one hand, this helped businesses to survive and, on the other, to improve their performance. In fact, thanks to the development of Industry 4.0 (I4.0) and the introduction of digital technologies related to it on the shop floor, the presence of digital technologies in the industrial and manufacturing fields has risen. Another strong push for this change has resulted from the outbreak of Covid-19. This has encouraged firms to move in the digital direction.

To support this change, several scholars and practitioners have started studying how to implement these new technologies to exploit their capabilities successfully [7]. In particular, many "mature" managerial theories have been used to drive the firms' digitalisation. The outcome of this research has shown, in many cases, a double-sided synergetic effect, meaning that these theories can benefit companies' digital transformation but also that digital technologies can renew and enhance even more the benefits brought by these managerial theories [1, 6, 9].
## 2 Literature Review

This synergetic effect is seen when digital technologies are integrated with Operational Excellence theories, especially with Lean Management [2]. Not an insignificant number of papers have been published in the past few years studying the integration of Lean with digital technologies and I4.0 [13, 17]. Several researchers have started studying how the practices of Lean Management impact different domains such as sustainability [12], digitalisation [5, 11], and the introduction of new technologies as the ones belonging to the Industry 4.0 paradigm [13, 17]. However, according to a study by Buer et al. [1], a great focus has been given to how the I4.0 and digitalisation phenomena support the application of Lean, particularly digitalising its tools. They also underlined the need for further research on how Lean Thinking can support the introduction of new and digital technologies in firms. In fact, even if some papers highlighted the issues that arise in the case of automation and the digitalisation of inefficient processes [3, 8], there is still much work to be done. This was also confirmed by Núñez-Merino et al. [9], where the highlighted research gap is related to how Lean principles can facilitate the introduction of I4.0 technologies.

It is evident that there is a strong need to address this gap in academia and the industry. Thus, this research will seek to add knowledge on this topic by answering the following research question:

*"How can a Lean approach facilitate the introduction of Industry 4.0 technologies?"*

This paper aims to contribute by adding knowledge on how Lean Management can help firms drive digital transformation and how a Lean environment is fundamental to implementing I4.0 technologies. This article is based on a single case study on a continuous improvement project carried out in a warehouse with the aim of improving its performance before the implementation of the two autonomous vehicles.

## 3 The Company

The company under analysis is a multinational company, a leader in the sector of inks and pigment production for various applications. This firm has always been characterised by a strong push for innovation from the customer side (i.e., adding several interesting and innovative features to their product, e.g., security features to fight fake products). This was also true from an internal perspective, where investments in innovative and digital technologies are regularly carried out to keep up with their operations. Among the several production plants established around the world, one situated in Italy started some years after the introduction of the Lean Management philosophy in their plants to improve operational performance. Thanks to the outstanding results obtained in previously conducted continuous improvement projects, the company has, this year, decided to start with another Lean implementation project. This project is mainly focused on warehouse management. In fact, within six months, the company will implement two new AGVs in the warehouse as part of its transition plan to an environment compliant with I4.0 technologies. This project was agreed upon
to get a more streamlined environment before the AGV implementation solved some issues the company faced. The main Italian plant in which this case study is carried out is divided into two sites: A and B. Site A has more than 100 employees coping with more than 10,000 codes for raw materials and finished products.

In contrast, site B is the production plant responsible for realising some special products, with more than 150 employees dealing with more than 5,000 different codes. The focus of this case study is the on-site A warehouse. This is the most consequential warehouse in the plant since around 40% of the stocked products are delivered from there, and it is here that the AGV will be implemented soon.

# 4 Methodology

This case study is based on the A3 problem-solving tool [14], which has successfully driven continuous improvement projects in several firms, as demonstrated by several cases in the current literature [4, 10, 16]. A3 framework is also recognised as one of the most effective tools within the Lean philosophy [15]. The firm has decided to follow this model's steps throughout all the projects to identify issues, wastage and inefficiencies within their processes. This resulted in an effective and efficient problem resolution through the PDCA (Plan – Do – Check - Act) cycle. This structure will be followed in subsequent lines, and the eight sections of the A3 will be presented as subsections of the P-D-C-A.

## 4.1 Plan

In this section, all the actions belonging to the Plan part of the A3 model related to this project will be thoroughly described.

### Problem Background

This continuous improvement project was mainly triggered by the need of the company to align its operations to a radical change in its warehouse, namely the upcoming implementation of two new AGV vehicles in the main warehouse. This deployment will bring substantial environmental changes, creating various opportunities and benefits, such as working cost reduction, improved productivity and retrieval time. Still, conversely, it was clear to the company that the current warehouse management was not optimal and ready for such a big change.

Starting from this pending structural change and analysing, at the same time, the dynamic and uncertain environment in which the firm operates, such as the Covid-19 pandemic and an expected increase in sales volumes by 10%, it was important to investigate the current situation of the warehouse. After some brief analysis, it was clear that there were some relevant issues regarding high space saturation and low operators' productivity, defined as the number of volumes in kilograms each operator can handle in a working day. The second problem was considered a consequence of bad management and will be solved by fixing the first one and then by introducing the AGVs.

Thus, tackling the first problem, related to high space saturation, the company reports a saturation of 91%, exceeding the threshold set at the corporate level. From this arose
the need to reorganise and optimise the space within the warehouse by increasing the total number of pallet load (PL) positions while simultaneously facilitating the introduction of the new vehicles. Because of this, the project's first step was defined by the need to understand the source of complexity resulting from the warehouse layout, PL storage and lack of data. Therefore, several Gemba walks were conducted in the warehouse area to understand better how space and activities are organised.

## Problem Breakdown

To have a clear understanding of the warehouse structure's criticalities and delve into the problem of space saturation, performing several Gemba walks turned out to be crucial for the firm, for the company was immediately clear that there were many issues in the current situation that needed to be fixed as soon as possible. Firstly, the distribution of the locations was not homogeneous, as the racks structures were not the same for all of them: in some aisles, the racks were engineered for storing three pallet loads per bay, while in others, only 2; then, some aisles have additional shelves to store other pallet loads while some others had not. Clearly, this situation was not good for implementing complex technology such as AGVs.

After other Gemba walks and deeper analysis, the firm could point out that other relevant issues were present relating to the codification of racks and data management: for instance, in a specific aisle, three columns were identified using the same code. Up until this point, this issue had not seemed to be a problem since the operator was only spending more time choosing the pallet to retrieve. Still, this would be a problem with AGVs implementation when they cannot behave like the operators. Furthermore, the presence of non-standard pallets, pallets of bigger size, had been detected in the racks, which can stock three pallet loads per bay, leading to the loss of one pallet placed for each non-standard pallet stored. This situation was causing trouble in inventory management; in fact, after comparing the data from the Gemba walks with those extrapolated from the firm information systems, a discrepancy between the two was highlighted. A low saturation was depicted by the information system (i.e., a higher number of free places available when they were actually occupied). This problem was relevant because when analysing data, the saturation appears wrongly. From the perspective of AGV implementation, the data coming from the systems must represent the environment faithfully.

Moreover, several structural constraints were found, such as the presence of the physical columns within the racks or in the middle of the aisles, which prevents the forklift from reaching the locations with the consequent loss of available pallet places. This forces the intervention of an operator, who must necessarily insert or move by hand, the goods stored on the ground-floor level to make maximum use of the space. This results in a major concern, particularly given AGV implementation, as the operator will no longer be able to enter the storage area, and products can be placed only on pallets. Lastly, several products which have been identified were then categorised as obsolete, thus considered as non-value-added items within the warehouse. These volumes reveal a problem for warehouse saturation, as they take away space from other products mostly requested by customers, which can represent a fruitful source of profit for the company.
After this phase, the company also decided to analyse the impact of the reduction of available pallet loads placed after the AGVs introduction. In fact, due to some embedded constraints of these vehicles, and if the current warehouse layout remains the same, certain aspects of the workspace need to be changed, thus reducing the available space. The outcome of the analysis showed a new condition with a saturation higher of almost one percentage point (i.e., 15 pallet loads places lost).

Thus, it was clear to the company that improving the current condition was absolutely necessary and needed to be done before implementing the new technology. This is especially true considering all of these problems were blocking the possibility of using different warehouse management policies, such as class-based storage (currently, the company is working with randomised storage, products are put in the first place available). Class-based storage could be optimal in this particular situation where some products are very often used, especially if compared to others. Furthermore, this bad management of the warehouse caused very low productivity of operators, which cannot be accepted with the AGVs because there will be the risk of not finalising the assigned mission. Since the AGV is more technologically advanced, it is more productive and precise but, at the same time, more rigid so mismanagement could lead to a decline in performance.

In this light, the company decided to rapidly proceed with the resolution of the issues, which impacted the saturation of the warehouse and was estimated as in the following Table 1:

<table><caption>**Table 1.** How current problems impact warehouse space</caption><thead><tr><td>Problem</td><td>Impact on the total number of pallet locations</td></tr></thead><tbody><tr><td>Unstandardised racks structure</td><td>6.20%</td></tr><tr><td>Physical columns and service room in the aisles</td><td>2.40%</td></tr><tr><td>The stock of obsolete products</td><td>7.13%</td></tr><tr><td>Unstandardised pallet loads</td><td>0.63%</td></tr><tr><td>The discrepancy between the Information System and the actual situation</td><td>1.35%</td></tr></tbody></table>
## Target Setting

Concerning the target setting, the main objective is to lower the space saturation of the warehouse, both in the short-term and long-term view. The Key Performance Indicator (KPI) used to measure space saturation is calculated as follows:

$$ \text{Space Saturation} = \frac{\text{\# Occupied Pallet Locations}}{\text{Total \# of Available Pallet Locations in the Warehouse}} $$

Since the current performance resulted in a 91% space saturation, which exceeds the threshold set by the company, which is about 80%, the main idea is to work on both the numerator and the denominator, thus reducing the occupied locations and increasing the nominal value of the total places available in the warehouse, reaching the desired saturation as the target set by the firm within the implementation of the AGVs. This will also allow the company to change the storage and management policies, leading to the easier implementation of AGVs characterised by fewer problems and higher performance.

## Root Cause Analysis

After the target definition, the root cause analysis was performed to investigate the causes of high space saturation further. The first tool used was the Ishikawa Diagram, which provided a general understanding and summary of the causes related to the problem. In contrast, a customised version of the FMEA was subsequently used to analyse that which influenced causes in terms of impact, visibility and importance, given AGV implementation. This allowed a study of those causes which have a greater overall impact on the identified high space saturation problem.

The outcome from the Ishikawa Diagram provided an overview of all factors impacting the space saturation of the warehouse. The traditional 5M categories were shaped and adapted to understand the problem under analysis better. In particular, the 5M were reshaped into three categories: Layout, Method and Information System. The main causes under these categories were:

* Layout – Structural ergonomics issues (columns in the middle of some aisles, old firefighting system, presence of a service room in the warehouse) and non-standardised rack structure

* Method – Reception of non-standardised pallet loads and stock of obsolete products

* Information System – Discrepancy between the information systems with the actually occupied pallet locations.

Taking into consideration the causes depicted in the Ishikawa Diagram, through a customised FMEA framework, it was possible to identify the overall impact of the causes related to high warehouse saturation. In particular, the parameters used to evaluate the effects of a cause were:
* Severity: An equivalent value is assigned according to the importance of the impact on space saturation. In particular, the severity determines the percentage of pallet locations lost for each cause concerning the total available warehouse locations.

* Visibility: also interpreted as detectability, this factor is evaluated based on how easy it is to identify the cause of the problem. More precisely, it can be seen through the Gemba walk that the value assigned is low; conversely is high if data analysis is required.

* Importance in view of AGV implementation: an assessment based on how relevant the problem will be when if not solved when the AGV will be implemented.

Following this analysis, each problem was evaluated according to the three parameters, and a Problem Priority Number (PPN) was computed as the product of the three factors (the higher the PPN, the higher the impact of the problem). In Table 2, it is possible to see the outcome of the FMEA with the most relevant causes.

<table><caption>Table 2. Amended FMEA for root causes' effect evaluation</caption><thead><tr><td>Item and Function</td><td>Failure Mode</td><td>Effects</td><td>Severity (1-10)</td><td>Visibility (1-10)</td><td>AGV Importance (1-10)</td><td>Problem Priority Number (PPN)</td></tr></thead><tbody><tr><td>Racks structure</td><td>Non-standardised dimensions</td><td>Loss of pallet locations</td><td>8</td><td>4</td><td>3</td><td>96</td></tr><tr><td>Structural ergonomics</td><td>Columns and service rooms in the middle of some aisle</td><td>Loss of pallet locations</td><td>5</td><td>1</td><td>3</td><td>15</td></tr><tr><td>Stocked products</td><td>Obsolete products</td><td>Loss of pallet locations</td><td>9</td><td>8</td><td>7</td><td>504</td></tr><tr><td>Reception of pallet loads</td><td>Non-standardised pallet dimensions</td><td>Loss of pallet locations</td><td>5</td><td>2</td><td>10</td><td>100</td></tr><tr><td>Information System data</td><td>The discrepancy with the actual data</td><td>Loss of pallet locations</td><td>2</td><td>6</td><td>7</td><td>84</td></tr></tbody></table>

## Countermeasures Definition

Starting from the causes identified, five main countermeasures have been developed. For each of these, the impact and effort required for implementing fixes have been analysed to give the right prioritisation to the various solutions. To estimate the improvement (i.e., the impact), the FMEA has also been used in this case through the computation of a Countermeasures Priority Number (CPN). The CPN is the product of the three parameters mentioned above, but this time the impact of the problem was calculated considering its relevance with the implemented countermeasures. Then the PPN reduction is computed as the delta (PPN minus the CPN) has been calculated. Taking the highest delta value and holding it as the common denominator, the
percentage of improvement of all other problems is calculated by classifying as low those values between 0-15 %, medium those values between 16-49 % and finally, high those values upwards from 50%. On the other hand, the effort has been qualitatively computed according to the time, costs and difficulty of implementation.

In Table 3, is it possible to see how the FMEA was used. The parameters used for evaluation are to be considered with respect to the future situation where the countermeasures will be fully implemented. At the same time, the proposed actions correspond to the problems having the same line in Table 2 (e.g., Line 1 problem – racks structure corresponds to Line 1 proposed action – change of racks).

<table><caption>Table 3. Amended FMEA for countermeasures' impact evaluation</caption><thead><tr><th>Proposed Actions</th><th>New Severity (1-10)</th><th>New Visibility (1-10)</th><th>New AGV Importance (1-10)</th><th>Countermeasure Priority Number (CPN)</th><th>Delta - PPN Reduction</th></tr></thead><tbody><tr><td>Racks change</td><td>1</td><td>1</td><td>1</td><td>1</td><td>95</td></tr><tr><td>External service room</td><td>2</td><td>1</td><td>2</td><td>4</td><td>9</td></tr><tr><td>Elimination of obsolete products + systematic and recurrent control on their level</td><td>4</td><td>4</td><td>7</td><td>112</td><td>392</td></tr><tr><td>Agreements with external suppliers</td><td>1</td><td>1</td><td>1</td><td>1</td><td>99</td></tr><tr><td>Periodical Gemba walks and comparison Information System data with shopfloor one</td><td>2</td><td>5</td><td>7</td><td>70</td><td>14</td></tr></tbody></table>

Afterwards, an economic evaluation of the costs and benefits was carried out to understand whether the money expenditures were worthwhile.

It is also important to say that the company studied two other countermeasures: the reallocation of the products according to a sort of class-based storage and the implementation of the AGV. These two have not been considered in the FMEA since their implementation was already agreed upon. Still, it is important to consider these since this project has been carried out in the light of AGV implementation. The results will estimate the overall impact after the full implementation. In particular, the new storage policy is developed through the Lean Warehousing approach using a crossed ABC-XYZ analysis to understand the optimal position for each product. A digital dynamic model has been preventively developed using a spreadsheet built to work in harmony with the company information system.
## 4.2 Do

The Do section only includes the part of the implementation of the countermeasure. In this case, the countermeasures are strictly related to warehouse structural and management changes; they will be physically done in the following months and mainly at the end of the year, simultaneously with the AGV implementation. The company board decided this due to convenience and functionality (i.e., in this period, the production department will operate at a reduced pace, and end-of-the-year inventories will be computed).

### Countermeasures Implementation

The relevant company stakeholders have decided to implement all the countermeasures proposed. In spite of this, several factors influenced the application of the reported improvement planning. In particular, major structural and organisational changes are planned in view of the AGV implementation. Therefore, the countermeasure implementation was postponed until the project's conclusion was reached.

Nevertheless, following the method described above, a prioritisation order for the countermeasures has been developed; thus, Table 4 shows how the final order of countermeasures has been obtained.

<table><caption>Table 4. Countermeasures prioritisation</caption><thead><tr><td>Problem</td><td>PPN</td><td>Countermeasure</td><td>CPN</td><td>Delta Improvement</td><td>Impact</td><td>Effort</td><td>Priority</td></tr></thead><tbody><tr><td>Non-standardised racks structure</td><td>96</td><td>Racks change</td><td>1</td><td>95</td><td>Medium</td><td>High</td><td>2</td></tr><tr><td>Physical columns and service room in the aisles</td><td>15</td><td>External service room</td><td>4</td><td>9</td><td>Low</td><td>High</td><td>3</td></tr><tr><td>The stock of obsolete products</td><td>504</td><td>Obsolete elimination + recurrent check</td><td>112</td><td>392</td><td>High</td><td>Low</td><td>1</td></tr><tr><td>Nonstandardised pallet loads</td><td>100</td><td>Supplier agreement</td><td>1</td><td>99</td><td>Medium</td><td>Medium</td><td>2</td></tr><tr><td>The discrepancy between the Information System and the actual situation</td><td>84</td><td>Periodical Gemba walks and comparison</td><td>70</td><td>14</td><td>Low</td><td>Medium</td><td>3</td></tr></tbody></table>
After implementing these countermeasures, products will be reallocated, and AGV
will be implemented. Hence, a Gantt chart has been designed to have a clear overview
of what and when has to be done to implement the solutions successfully.

In the end, is it possible to summarise what the company has done to solve the
problems to introduce the AGV in the following Figure, where the project and its
interaction with Lean are easily represented schematically.

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Schematical representation of the approach used, and the benefits given by using a Lean approach</figcaption></figure>

**4.3 Check**

The check phase is a very important step to understanding the success of the project through the measurement of the results and the comparison with the target. In this part, the expected results will be presented through a simulation of the expected environment after implementing the countermeasure.

**Results Monitoring**

Since the countermeasures will be implemented at the end of the year, there was a need
to verify their effectiveness in another way. Therefore, a simulated estimation of the
improvements in terms of space saturation has been done. Moreover, with all the
countermeasures implemented (without AGVs and reallocation) and considering the
KPI used for target definition, it has been registered as a positive impact. This impact
is positive in the numerator, with a reduction of the occupied PLs location. The
denominator will become bigger thanks to the increase in the nominal number of PL
places in the warehouse. This will lead to an overall reduction of space saturation.

Overall, these improvements lead to a higher number of pallet locations available (127) and an expected decreased saturation level equal to around 14 percentage points, thus meaning that the previously set target has been achieved. Also, from an economic
perspective, the investments to be made to change the warehouse structurally were considered fair by the firm and the payback time corresponded to a few years.

After these implementations, the products will be reallocated according to a new storage policy, and the AGV will be done. This will allow the company also to improve productivity: a comparison has been made comparing the two storage policies (current policy vs the new policy developed through a Lean Warehousing approach), hypothesising the use of AGVs in both cases. An increase of more than 45% in productivity with respect to the current condition is expected after implementing both countermeasures, a new storage policy and AGVs. This will clearly lead to a significant decrease in retrieval time, stocking time and labour cost.

## 4.4 Act

This last phase is crucial for understanding if the project was successfully carried out and sharing its benefits. Moreover, possible improvements or extensions of the project are presented in this section.

### Standardise and Share Success

This project has been recognised by the company's board as a very successful one and is fundamental for the proper AGV implementation in the warehouse. Thus, it will be considered a useful way to evaluate and introduce other potential technologies of I4.0.

Furthermore, several learning points have been collected for the next implementation, future warehouse management, and further improvement projects. Some of them are:

* Periodic Gemba Walks to check the alignment of the warehouse with the rules, policies and information systems.

* A systematic check of the product allocation according to the new storage model.

* Recurrent control of the possible obsolete products.

## 5 Conclusions

Even if the company was aware of the benefits of Lean projects conducted by the use of the A3 methodology, thanks to previous projects, this project corroborated the importance of systematic and recurrent use of the Lean approach. By using the A3, it is possible to tackle the issues constantly generated in every department by addressing the needed improvements. In this way, the companies can solve them and grasp the opportunities that new technologies can bring. In fact, thanks to this practical example, the company has confirmed the importance of applying Lean to their environment, especially when there is a need to introduce technologies related to I4.0.

From a broader perspective and to link the results with the current literature, this paper shows a successful and fruitful continuous improvement project aimed at introducing AGVs in a warehouse.

This work showcases an example of a company that understood the potential benefits of applying Lean before implementing digital technologies or I4.0. Thus it could be
considered an example of how Lean supports I4.0. As shown in detail in the results section, the impact of the continuous improvement project is even greater when introducing new technologies. In fact, due to the rigidity embedded in automated vehicles, the current problems in the warehouse will be enhanced by their introduction. Thus, a Lean approach should be used in the environment where I4.0 technologies will be introduced. The implementation will be easier through a streamlined process using the right tools and methodologies, and the impact on firms' performance will be higher. On the other hand, this case shows that in a continuous improvement project, where Lean is correctly applied, the final introduction of I4.0 technologies boosts its results. Hence, it is possible to argue that the synergetic effect in this specific case study is bi-directional; Lean favours, in a tangible way, the introduction of I4.0 technologies, but the latter raises the effectiveness of the former.

In conclusion, Lean is a continuously evolving paradigm that is changing and adapting to different environments. Firms that want to embark on the digital journey must streamline their processes and start with a widespread introduction to the lean culture. By doing this, the effect brought by the innovation of I4.0 technologies will be even stronger, and the effectiveness of the Lean tools will be enhanced.

# 6 Implications, Limitations and Further Research

## 6.1 Implications

This paper showed the importance of spotting and addressing improvement points before implementing I4.0 technologies. In this sense, the A3 model is considered here as a tool that can be relevant for this purpose. Likewise, other Operational Excellence tools presented in this paper, such as FMEA and Ishikawa Diagram, are fundamental to analysing the current situation.

Thus, from a theoretical point of view, it is possible to state that this paper exhibits Lean's importance in the digital era. Few empirical studies have addressed how a Lean approach can facilitate the implementation of I4.0 technologies. From this study, Lean is considered an essential practice to be applied before I4.0, and furthermore, its facilitating effect has been showcased.

From a more practical viewpoint, the outcomes of this research could be useful for other firms or practitioners dealing with the same issues as a framework on how to proceed and which are the expected benefits of an improvement project like this one.

## 6.2 Limitations and Further Research

Even though this paper has added knowledge to the literature, and its outcome could be useful for other firms and practitioners, its main limitation lies in the methodology. In fact, the results of a single case study are hardly generalizable and extendable to other contexts or firms [16, 18]. Thus, the authors suggest taking this case study as a starting point for further research. This paper could be considered as a basis for further research using other methodologies; for instance, it could be extended by using a multiple-case study approach. Also, other methodologies could be employed (e.g., simulation) for having a quantitative estimation of the benefits obtained when Lean is implemented prior to digital or I4.0 technologies.
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