







































Q. Ma & D. Wu /Future Technology                                                                                            August 2025| Volume 04 | Issue 03 | Pages 
54-66 

54 

 

 

 

Article 

Breaking data silos in multi-tier suppliers and 

designing intelligent collaborative trust  
Qiuya Ma*, Danqing Wu 

Faculty of Business, Hospitality, Accounting and Finance (FOBHAF), MAHSA University, Malaysia 

A R T I C L E   I N F O 
 

Article history: 
Received 06 April 2025  
Received in revised form 
20 May 2025 
Accepted 30 May 2025 
 
Keywords:  
Data silos, Multi-tier supply chains,  
Federated learning, Algorithmic trust,  
Blockchain integration 
 
*Corresponding author 
Email address: 
18375688617@163.com 
 
 
DOI: 10.55670/fpll.futech.4.3.6 

A B S T R A C T 
 

Data silos across multi-tier supply chains create significant barriers to 
operational efficiency and resilience, where information fragmentation 
undermines collaborative intelligence and increases disruption vulnerability. 
This research investigates data silo formation mechanisms and develops an 
intelligent collaborative trust framework leveraging artificial intelligence to 
address integration challenges. The study employs mixed-methods analysis 
across 47 manufacturing organizations selected through stratified purposive 
sampling from China's industrial regions. A hybrid architecture combining 
blockchain with federated learning enables secure cross-organizational 
information exchange while preserving competitive advantages through 
reputation-based smart contracts and algorithmic trust mechanisms. Network 
analysis identifies six primary data silo types, with technological barriers most 
prevalent at 31.4 percent and organizational barriers at 23.8 percent. 
Randomized controlled trials demonstrate significant performance 
improvements over conventional approaches. Supply chain visibility increases 
by 39%, while coordination costs decrease by 28%. The neural network 
ensemble achieves a 7.3-day average disruption prediction lead time 
improvement, with pharmaceutical manufacturers experiencing 9.8 days of 
early warning enhancement. Mean absolute prediction error reduces by 42 
percent, and inventory optimization shows 156 percent cost efficiency 
improvement. This research contributes to supply chain digitalization theory 
by reconceptualizing trust as an algorithmically-mediated construct, 
establishing selective transparency frameworks that enable distributed 
intelligence architectures to achieve. 

1. Introduction 

Current supply chains are increasingly intricate systems; 
multi-tiered suppliers, manufacturers, and distributors form 
intricate ecosystems that power the world economy [1]. 
These systems are undergoing drastic transformation within 
Industry 4.0, where new technologies are poised to offer 
unprecedented levels of interconnectivity and intelligence 
across operations [2]. Most companies, however, still struggle 
with fragmented and siloed information systems that inhibit 
complete visibility and collaboration across different tiers of 
the supply chain [3]. This lack of integration leads to 
inefficient optimization, increases vulnerability to 
disruptions, and weakens operational efficiency [4]. The 
situation is especially dire for manufacturing industries with 
complex products that depend on multiple suppliers who use 
different systems and have different levels of technology [5]. 
This information asymmetry creates barriers to supply chain 
integration, and research demonstrates that limited visibility 
beyond direct supplier boundaries of direct suppliers can 
increase coordination costs by up to 40% and severely 

undermine resilience to interruptions [6]. These integration 
challenges manifest through six distinct types of data silos 
that create systematic barriers to collaborative intelligence. 
Technological incompatibilities between heterogeneous 
systems are the most prevalent form, followed by 
organizational boundaries that extend beyond individual 
entities, competitive concerns about intellectual property 
exposure, geographical distribution barriers, regulatory 
compliance requirements, and cultural differences in 
information-sharing practices. The resulting information 
fragmentation creates a fundamental paradox where 
organizations possess valuable data that could enhance 
collective supply chain performance, yet remain reluctant to 
share due to legitimate concerns about competitive 
positioning and data security vulnerabilities. As noted in 
reference [7], artificial intelligence (AI) technologies have 
emerged as one of the most effective solutions for the 
integration problems faced by organizations in their attempts 
to manage large amounts of supply chain data. Predictive 
analytics enhances resource optimization, allocation, and the 

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Q. Ma & D. Wu /Future Technology                                                                                            August 2025| Volume 04 | Issue 03 | Pages 54-66 

55 

 

identification of latent risks that could trigger network-wide 
failures [8]. Machine learning techniques are the most widely 
used algorithms for forecasting supply chains, and deep 
learning algorithms are sophisticated multi-dimensional data 
processors that promise to uncover hidden patterns [9]. 
These technologies enable proactive supply chain 
management paradigms, fundamentally enhancing 
organizational agility and responsiveness to market dynamics  
[10]. The use of AI in multi-tier supply chains, however, faces 
the fundamental obstacle of data disconnects that impede the 
streaming access of data between various organizations [11]. 

Innovative technologies such as blockchain and 
federated learning present sophisticated methods for 
crossing data barriers while preserving organizational 
independence and data confidentiality [12]. Additionally, 
blockchain supports an electronic ledger system that is 
decentralised and unchangeable, which allows supply chain 
partners to have trust relationships with one another without 
the need for a central authority [13]. Furthermore, federated 
learning encourages group participation in model creation 
without sharing sensitive raw data, thus allowing each 
organization to utilise shared knowledge without losing 
proprietary information [14]. These technologies 
complement traditional approaches by enhancing distributed 
computing architectures through advanced computational 
frameworks. This adds layers to multi-agent systems where 
the balance between information sharing and competition 
rivalry is monitored and maintained [15]. This convergence 
of technologies alleviates one of the most fundamental 
conflicts regarding supply chain integration: the balance 
between utilizing information for collective benefit and 
safeguarding proprietary data and other competitive 
intelligence [16]. 

Despite the encouraging advances in individual 
technologies, comprehensive, integrated approaches that 
specifically focus on mitigating data silos across multi-tier 
supplier systems still pose significant research challenges. 
Current approaches still tend to cater to either unilateral 
interactions or isolated patches of technological solutions 
without factoring in the entire socio-technical ecosystem 
necessary for seamless integration. Many frameworks fall 
short of providing adequate provisions for the creation and 
maintenance of trust across organizational boundaries, 
especially in the presence of a large, diverse coalition of 
stakeholders with different motivations [17]. The bounds of 
supply chain settings have yet to be transcended with regard 
to examining what is termed “algorithmic trust mechanisms” 
where interpersonal relationships are replaced with technical 
protocols governing confidence in exchanged data. 
Additionally, there is a shortage of research performing 
empirical analysis on the implementation of multi-tier supply 
chains through blockchain technology and federated learning 
[18]. 

To address these challenges, this research investigates 
the formation mechanisms and manifestation patterns of data 
silos across multi-tier manufacturing supply chains, examines 
how hybrid blockchain-federated learning architectures can 
enable secure cross-organizational information sharing while 
preserving competitive advantages, and evaluates the 
measurable performance improvements achievable through 
algorithmic trust mechanisms compared to traditional 
integration approaches. This research contributes to supply 
chain digitalization theory by reconceptualizing trust as an 
algorithmically-mediated construct, establishing a "selective 
transparency" framework, and demonstrating that 
distributed intelligence architectures achieve superior 

integration without centralized data consolidation. 
Practically, it provides organizations with an empirically 
validated framework for enhanced information sharing and 
operational resilience, challenging the assumption that 
effective integration requires centralized data aggregation. 

2. Data and methodology 

2.1 Research design and data collection 
This study employs a mixed-methods approach 

integrating quantitative network analysis and qualitative case 
studies to examine data silos and intelligent collaborative 
trust mechanisms in multi-tier supply chains [19]. The 
research comprised three successive stages: data collection 
from Chinese manufacturing firms, network analysis of 
information sharing behavior, and architectural framework 
development. The selection of 47 manufacturing 
organizations followed a stratified purposive sampling 
approach. Organizations were selected from China's 
manufacturing enterprise database based on specific 
inclusion criteria, including annual revenue exceeding 50 
million RMB, involvement in multi-tier supply chains with at 
least three supplier tiers, established digital information 
systems, and willingness to participate in data sharing 
research. Stratified sampling ensured proportional 
representation across industry sectors and geographic 
regions. The final sample spans China's primary industrial 
regions—Yangtze River Delta (38.3%), Pearl River Delta 
(25.5%), Beijing-Tianjin-Hebei (21.3%), and other centers 
(14.9%)—ensuring representation across varied industrial 
clusters while maintaining focus on regions with significant 
multi-tier supply chain complexity [20]. The sectoral 
distribution closely matches the stratification targets, with 
the following breakdown: automotive (23.4%), electronics 
(19.1%), aerospace (17.0%), pharmaceuticals (14.9%), food 
and beverage (12.8%), and other manufacturing (12.8%). The 
sample encompasses organizations across different supply 
chain positions: 15 OEMs (31.9%), 18 Tier-1 suppliers 
(38.3%), and 14 Tier-2+ suppliers (29.8%), providing 
comprehensive coverage of multi-tier supply chain 
structures. Data collection employed complementary 
methods, including 193 structured surveys administered to 
supply chain managers and IT directors, 83 semi-structured 
interviews with key personnel, and system log analysis from 
27 organizations where accessible to minimize self-reporting 
bias [21]. Table 1 demonstrates representative coverage with 
adequate representation ratios (0.87-1.07) across all sectors 
and geographic regions. Chi-square goodness-of-fit tests 
confirm that the sample distribution does not significantly 
differ from target stratification (p > 0.05), and organizational 
diversity with revenue ranges from 52 million to 15.8 billion 
RMB. 

2.2 Multi-tier supply chain analysis method 
The study utilizes a network-based approach to track 

information flow and detect data silos in multi-tier supply 
chains. The supply chain is modeled as a directed graph G = 
(V, E), where V represents organizations and E represents 
information exchange relationships [22]. Each edge 
encompasses attributes such as frequency, completeness, 
timeliness, and quality of information flow, enabling 
sophisticated graph analysis to identify structures that cause 
information fragmentation across organizational boundaries. 
The data silos detection within the network is achieved using 
modularity optimization algorithm which identifies groups 
with dense interconnectivity and sparse connections to other 
groups. 



Q. Ma & D. Wu /Future Technology                                                                                            August 2025| Volume 04 | Issue 03 | Pages 54-66 

56 

 

Table 1. Sample characteristics and representativeness analysis of 
47 manufacturing organizations 

Industry 
Sector 

Number of 
Organizations 

Geographic 
Distribution 

Supply 
Chain 

Position 

Data 
Collection 
Methods 

Automotive 11 (23.4%) 

Yangtze 
Delta: 5 

Pearl Delta: 
3 

Beijing-
Tianjin-
Hebei: 2 
Other: 1 

OEM: 4 
Tier-1: 

5 
Tier-2+: 

2 

Surveys: 
47 

Interviews
: 27 

System 
logs: 7 

Electronics 9 (19.1%) 

Yangtze 
Delta: 4 

Pearl Delta: 
3 

Beijing-
Tianjin-
Hebei: 1 
Other: 1 

OEM: 3 
Tier-1: 

4 
Tier-2+: 

2 

Surveys: 
38 

Interviews
: 17 

System 
logs: 6 

Aerospace 8 (17.0%) 

Yangtze 
Delta: 3 

Pearl Delta: 
1 

Beijing-
Tianjin-
Hebei: 3 
Other: 1 

OEM: 2 
Tier-1: 

3 
Tier-2+: 

3 

Surveys: 
32 

Interviews
: 14 

System 
logs: 5 

Pharmaceutica
l 

7 (14.9%) 

Yangtze 
Delta: 3 

Pearl Delta: 
2 

Beijing-
Tianjin-
Hebei: 1 
Other: 1 

OEM: 3 
Tier-1: 

2 
Tier-2+: 

2 

Surveys: 
28 

Interviews
: 11 

System 
logs: 4 

Food & 
Beverage 

6 (12.8%) 

Yangtze 
Delta: 2 

Pearl Delta: 
1 

Beijing-
Tianjin-
Hebei: 2 
Other: 1 

OEM: 2 
Tier-1: 

2 
Tier-2+: 

2 

Surveys: 
24 

Interviews
: 9 

System 
logs: 3 

Other 
Manufacturing 

6 (12.8%) 

Yangtze 
Delta: 1 

Pearl Delta: 
2 

Beijing-
Tianjin-
Hebei: 1 
Other: 2 

OEM: 1 
Tier-1: 

2 
Tier-2+: 

3 

Surveys: 
24 

Interviews
: 5 

System 
logs: 2 

Total 47 (100%) 

Yangtze 
Delta: 18 
(38.3%) 

Pearl Delta: 
12 (25.5%) 

Beijing-
Tianjin-

Hebei: 10 
(21.3%) 
Other: 7 
(14.9%) 

OEM: 15 
(31.9%) 
Tier-1: 

18 
(38.3%) 
Tier-2+: 

14 
(29.8%) 

Surveys: 
193 

Interviews
: 83 

System 
logs: 27 

 
The modularity score Q, which measures the strength of 

community division, is given as follows: 

,

1
( , )

2 2

i j

ij i j

i j

k k
Q A c c

m m


 
= − 

 


                                               (1) 

Where Aij represents the information flow intensity 
between organizations i and j, ki and kj denote the total 
information flows for organizations i and j, respectively, m is 
the sum of all flow intensities in the network, and 𝛿(𝑐𝑖 , 𝑐𝑗) 

equals 1 when organizations i and j belong to the same 
community and 0 otherwise. Higher Q values indicate 
stronger data silo formations within the supply chain 
network [23]. 

To address endogeneity and confounding variable 
effects, the structural equation model adopts a causal 
inference framework using instrumental variables. Industry 
concentration ratios and regulatory environment indices 
serve as instruments for competitive dynamics and power 
balance, which may be simultaneously determined with data 
silo formation. The model employs two-stage estimation: 
first-stage regression estimates endogenous variables using 
instruments, while second-stage regression estimates causal 
effects on data silo intensity. Confounding variable control is 
achieved through the inclusion of industry fixed effects (𝛼𝑖), 
temporal controls ( 𝛾𝑡 ), and organizational characteristic 
covariates (Zi,j), yielding the expanded causal model: 

,, 0 1 , 2 3 , 4 ,

,5 , ,

i ji j i j i j i j

i j i t i j i j

DSI TC CD TL DS

PB Z

    

    

= + + + +

+ + + + +

 (1) 

where ,i jCD  and ,i jPB  represent instrumented variables [24]. 

All constructs are operationalized using validated multi-
item scales. Technological Compatibility measures system 
interoperability and integration complexity (composite 
reliability = 0.91). Competitive Dynamics employs Porter's 
framework, measuring market rivalry and competitive forces 
(composite reliability = 0.89). Trust Level encompasses 
competence-based, benevolence-based, and integrity-based 
dimensions (composite reliability = 0.92). Data Sensitivity 
captures the importance of intellectual property and the 
potential for competitive advantage (composite reliability = 
0.90). Power Balance measures organizational influence 
through resource dependence indicators (composite 
reliability = 0.88). All scales demonstrate convergent and 
discriminant validity. 

The analysis further employs graph neural networks to 
model information propagation across the supply chain. The 
mathematical formulation of the graph convolutional layer is: 

1 1

( 1) ( ) ( )2 2l l lH D AD H W
− −

+
 

=  
 

                                                    (3)                                       

Where A represents the adjacency matrix of information 
flows, D is the degree matrix with 𝐷𝑖𝑖 = ∑ 𝐴𝑖𝑗𝑗 , 𝐻(𝑙)  is the 

feature matrix at layer l capturing the information state of 

each organization, 
( )lW  denotes the trainable weight matrix, 

and 𝛿  is a non-linear activation function. This construction 
facilitates the modeling of how information spreads at 
different levels of the supply chain [22].   
The changing patterns of information dissemination over 
time are captured by a dynamic information integration index 
(DIII), which quantifies the flow of information between 
various organizations within a given timeframe: 

, ,

,

IF
DIII

i j V ij ij t

t

i j V ij

w

w






=



  (4) 

 
Where 𝐼𝐹𝑖𝑗,𝑡  represents the information flow between 

organizations i and j at time t, and wij is a weight reflecting the 
strategic importance of that relationship within the supply 



Q. Ma & D. Wu /Future Technology                                                                                            August 2025| Volume 04 | Issue 03 | Pages 54-66 

57 

 

chain. The analytical framework undergoes rigorous 
validation, including multivariate normality, linearity, 
multicollinearity assessment (VIF < 3.5), and 
homoscedasticity testing. Endogeneity is addressed through 
Hansen's J-test and Durbin-Wu-Hausman tests. Model 
specifications are validated using fit indices (RMSEA < 0.08, 
CFI > 0.95, TLI > 0.95) with bootstrap validation (1,000 
replications), ensuring parameter stability. 

2.3 Hybrid intelligent architecture design and 
experimental evaluation 
This study designs a hybrid framework combining 

blockchain with federated learning for secure information 
sharing across organizations. The seven-layer architecture 
includes a blockchain layer, a federated learning middleware, 
and application service layers as shown in Figure 1. The 
blockchain layer employs a permissioned consortium 
blockchain to record supply chain events in an immutable 
ledger [25]. This infrastructure builds trust among 
participating organizations while maintaining organizational 
autonomy through local nodes and consensus mechanisms. 

Application Service Layer

Supply Chain Visibility | Disruption Prediction | Inventory Optimization

Federated Learning Middleware Layer

Local Model 

Training
Secure Aggregation Model Distribution

Blockchain Foundation Layer

Smart 

Contracts
Consensus

Distributed 

Ledger

Reputation 

System

OEM
Tier-1 

suppliers

Tier-2+ 

suppliers

Multi-tier supply Chain Hybrid Intelligent Architecture Frame &work

Information 

Flow

Direct 

Connection

 
 
Figure 1. Multi-tier Supply Chain Hybrid Intelligent Architecture 
Framework 

The federated learning layer enables organizations to 
collaboratively train models while maintaining data privacy. 
Local models are trained on private data with only model 
parameters shared through secure aggregation protocols 
[25]. The architecture incorporates a reputation-based smart 
contract mechanism that dynamically adjusts trust 
parameters based on historical interactions, creating 
incentives for reliable information sharing and helping 
identify potential data quality issues [26]. 

Performance Evaluation Metrics and Baseline 
Specifications: The experimental evaluation employs 
standardized KPIs with defined calculation methodologies 
and benchmark values. Supply Chain Visibility (SCV) is 
quantified as the ratio of accessible information nodes to total 
information nodes, with an industry baseline of 45-55%. 
Coordination Cost Efficiency (CCE) measures expense 
reduction compared to baseline coordination costs. 
Additional metrics include Data Security Index ( ≥ 0.93 

threshold), Prediction Accuracy for Disruptions 
(benchmarked against traditional 60-70% accuracy), and 

Integration Time Efficiency (compared to industry standard 
8-12 months deployment periods). 

The study employs a randomized controlled trial with 47 
manufacturing organizations randomly assigned to a 
treatment group (n=24, implementing hybrid architecture) 
and a control group (n=23, maintaining conventional 
systems) using stratified randomization. Baseline 
equivalence testing confirms no significant group differences 
(all p > 0.05). All performance improvements undergo 
rigorous statistical validation using independent samples t-
tests with a significance level of α=0.05. Supply Chain 
Visibility improvements show significant treatment effects 
(mean difference = 35.1%, t(45) = 11.23, p < 0.001, 95% CI: 
26.8%-43.4%). Coordination Cost reduction demonstrates 
significant benefits (mean difference = -25.3%, t(45) = -9.87, 
p < 0.001, 95% CI: -33.2% to -21.6%). Cohen's d indicates 
large effect sizes for all primary metrics: SCV (d = 3.24), CCE 
(d = 2.89), PAD (d = 2.47). ANCOVA controls for baseline 
differences, and Bonferroni correction addresses multiple 
comparisons. Construct validity is confirmed through 
confirmatory factor analysis (CFI > 0.95, RMSEA < 0.06) and 
convergent validity testing (AVE > 0.5). Reliability 
assessments demonstrate test-retest correlations r > 0.87 and 
inter-rater reliability ICC(2,1) > 0.92. Bootstrap validation 
(1,000 replications) confirms robust findings. 
Implementation uses Hyperledger Fabric for blockchain and 
TensorFlow Federated for a learning framework, with a 
modular design enabling flexible adaptation to different 
supply chain contexts. 

3. Results 

3.1 Data Silo pattern analysis of 47 manufacturing 
organizations 
Analysis of data flow patterns across 47 Chinese 

manufacturing organizations revealed distinct data silo 
formations that significantly impact information integration 
in multi-tier supply chains. As demonstrated in Figure 2(a), 
network analysis identified six primary data silo types with 
varying prevalence: technological (31.4%), organizational 
(23.8%), competitive (19.6%), geographical (12.7%), 
regulatory (8.3%), and cultural (4.2%). These silos exhibited 
differential impermeability characteristics, with 
technological and competitive barriers presenting the most 
substantial impediments to cross-organizational information 
exchange. A detailed examination of the factors influencing 
silo formation revealed significant correlations between 
organizational characteristics and patterns of information 
fragmentation. As illustrated in Figure 2(b), organizational 
complexity (r=0.73, p<0.01) and hierarchical rigidity (r=0.68, 
p<0.01) emerged as powerful predictors of data silos, while 
company age showed moderate correlation (r=0.42, p<0.05). 
These relationships help explain why technological 
modernization alone often proves insufficient for dismantling 
information barriers—underlying organizational structures 
frequently reinforce data compartmentalization regardless of 
technical capabilities. The distribution of silo types 
demonstrated significant cross-industry variation, as 
depicted in Figure 2(c), reflecting sector-specific operational 
imperatives. Aerospace organizations exhibited the highest 
technological silo intensity (79) due to stringent safety 
certification requirements that mandate isolated validation 
environments. Electronics manufacturers exhibited the most 
pronounced competitive silos (80), driven by rapid 
innovation cycles where information sharing threatens to 
erode competitive advantages.  

 



Q. Ma & D. Wu /Future Technology                                                                                            August 2025| Volume 04 | Issue 03 | Pages 54-66 

58 

 

 
 

 

 

Automotive businesses exhibited organizational silos 
(72) reflecting multi-tier supplier complexity, while 
pharmaceutical manufacturers demonstrated regulatory silos 
(65) necessitated by FDA/EMA validation processes, and food 
and beverage companies exhibited geographical silos (58) 
from distributed sourcing across multiple jurisdictions. Silos 
of a technological nature stemmed mostly from other 
portions of the building systems design. These so-called 
“closed” legacy ERP systems encapsulated whole domains of 
activity- a circumstance in which subsystems intended to be 
integrated were placed in distinct silos. Centrality measures 
of the supply network backbone revealed that firms identified 
as central supply network nodes commonly worsened data 
integration fragmentation, rather than facilitating 
integration, which is what one would logically expect. This 
unexpected outcome suggests that power relations in supply 
networks may tend to encourage control over information 
rather than collaboration, even when benevolent central 
figures are present. 

The interrelation of mechanisms leading to silo 
formation is examined in Figure 2(d), which identifies 
underlying legacy systems and organizational silos as central 
nodes that drive other fragmentation motivators such as data 
confidentiality, knowledge hoarding, and privacy concerns. 
This network visualization explains why single-dimension 
interventions typically achieve limited success in addressing 
multi-faceted silo structures. Organizational silos manifest 
through departmental boundaries that extend beyond 
individual entities, creating "extended organizational silos" 
which are particularly evident in large electronics and 
pharmaceutical manufacturers. Competitive silos, on the 
other hand, represent strategic barriers that reduce 
information sharing by up to 62% in high-rivalry 
relationships compared to collaborative partnerships. 

 

 

 

 
As detailed in Table 2, organizational scale significantly 

influenced both silo characteristics and the efficacy of 
integration approaches. Large enterprises (n=23) 
demonstrated more pronounced technological and 
organizational silos but possessed greater resources for 
integration initiatives. Medium-sized organizations (n=15) 
exhibited the highest competitive silo intensity (classified as 
"High" for competitive positioning impediments), reflecting 
their vulnerable position in market competition. Small 
enterprises (n = 9) exhibited fewer formal silos but struggled 
with resource limitations that led to de facto information 
isolation due to capability constraints rather than intentional 
barriers. 

The efficacy of AI-based solutions varied significantly 
across different industry contexts, as shown in Table 2. 
Blockchain ledgers demonstrated the highest effectiveness in 
aerospace organizations (82%), likely due to their 
compatibility with certification and traceability 
requirements. Encrypted knowledge sharing techniques 
proved most effective for electronics manufacturers (81%), 
addressing their predominant concerns about intellectual 
property protection. Privacy-preserving analytics showed 
strong results in pharmaceutical settings (79%), aligning with 
their regulatory compliance needs. Network data analysis 
through machine learning clustering revealed four distinct 
data silo ecosystem patterns, as illustrated in Figure 3. These 
patterns—hierarchical cascades, parallel fragments, hub-
and-spoke isolations, and mesh diffusions—each exhibit 
unique information flow dynamics. The hierarchical cascade 
pattern (Figure 3(a)) features information flowing 
sequentially from upper to lower tiers, a common pattern 
found in automotive supply chains dominated by powerful 
core manufacturers. The parallel fragments pattern (Figure 
3(b)) demonstrates efficient information flow within 

Figure 2. Data silo types and formation mechanisms in manufacturing organizations (a) Distribution of data silo types. (b) Correlation of 

factors with silo formation. (c) Industry comparison of silo types. (d) Network of silo formation mechanisms. 



Q. Ma & D. Wu /Future Technology                                                                                            August 2025| Volume 04 | Issue 03 | Pages 54-66 

59 

 

relatively isolated parallel structures, but limited cross-
structure sharing, which is typically observed in competitive 
supply relationships within the electronics manufacturing 
sector. The hub-and-spoke isolation pattern (Figure 3(c)) is 
characterized by a central node that connects independent 
peripheral participants, which is prevalent in the aerospace 
industry's stringent certification environment. The mesh 
diffusion pattern (Figure 3(d)) presents distributed 
connections without clear hierarchies, a frequently observed 
phenomenon in industries with a high dependency on 
regional cooperation, such as the food and beverage sector.  

Table 2. Comparison of data silo characteristics across industries and 
organizational scales 

Industry 
Sector 

Primary Silo 
Type 

Information 
Sharing 
Barriers 

AI-Based 
Solution 
Efficacy 

Automotive 
(n=11) 

Organizational 
(41%) 

Proprietary 
systems (72%) 

Competitive 
protection 

(68%) 

Process 
automation 

(76%) 
Federated 
learning 
(68%) 

Electronics 
(n=9) 

Competitive 
(53%) 

IP protection 
concerns 

(84%) 
Innovation 

cycles (71%) 

Encrypted 
sharing 
(81%) 
Smart 

contracts 
(73%) 

Aerospace 
(n=8) 

Technological 
(62%) 

Certification 
requirements 

(79%) 
Security 

protocols 
(76%) 

Secure 
middleware 

(64%) 
Blockchain 

ledgers 
(82%) 

Pharmaceutical 
(n=7) 

Regulatory 
(57%) 

Compliance 
frameworks 

(87%) 
Data privacy 

(83%) 

Privacy-
preserving 
analytics 

(79%) 

Food & 
Beverage (n=6) 

Geographical 
(48%) 

Supply chain 
visibility (65%) 

Traceability 
(59%) 

IoT 
integration 

(74%) 
Distributed 
ML (63%) 

Organization 
Scale 

Primary Data 
Flow 

Impediments 

Integration 
Resources 

Technology 
Adoption 
Barriers 

Large (n=23) 

Complex 
hierarchies 

(High) 
System 

fragmentation 
(Medium) 

High financial 
resources 
Medium 

implementation 
agility 

Lengthy 
approval 
processes 

Legacy 
system 

dependencies 

Medium 
(n=15) 

Resource 
constraints 
(Medium) 

Competitive 
positioning 

(High) 

Medium 
financial 

resources 
High 

implementation 
agility 

Cost-benefit 
uncertainty 
Technical 
expertise 

limitations 

Small (n=9) 

Limited IT 
capabilities 

(High) 
Power 

asymmetry 
(High) 

Low financial 
resources 

High 
implementation 

agility 

Resource 
constraints 
Technology 

access 
limitations 

 

 

These patterns and their characteristics provide a 
scientific foundation for designing targeted data integration 
intervention strategies, enabling the development of 
personalized multi-tier supply chain data integration 
solutions based on specific industry and organizational scale 
characteristics. 

 

Figure 3. Silo ecosystem patterns identified through machine 
learning clustering (a) Hierarchical cascades. (b) Parallel fragments. 
(c) Hub-and-spoke isolations. (d) Mesh diffusions. 

3.2 Key findings from cross-tier collaboration case 
studies 
The in-depth analysis of cross-tier collaboration 

initiatives revealed significant insights into both persistent 
barriers and promising resolution strategies across multi-tier 
supply chains. As illustrated in Table 3, trust deficit emerged 
as the most prevalent impediment (76%), manifesting 
primarily through data reliability concerns (68%) and 
visibility reciprocity fears (57%). This trust barrier typically 
creates cascading effects throughout supply networks, with 
downstream suppliers exhibiting particular hesitancy to 
share operational data without guaranteed reciprocal 
transparency. Traditional resolution approaches, such as 
contractual agreements, achieved only modest success (53% 
effectiveness). AI-enhanced resolution strategies consistently 
outperformed traditional approaches across all barrier 
categories, with improvements ranging from 28% to 58% in 
key effectiveness metrics. Competitive exposure represents 
the second most significant barrier (72%), as shown in Table 
3, characterized by proprietary data protection concerns 
(81%) and fears of competitive intelligence leakage (74%). 
Organizations operating in high-innovation sectors 
demonstrated particular sensitivity to these concerns, with 
electronics manufacturers implementing the most restrictive 
information-sharing policies. 

Technical incompatibility constituted another 
substantial barrier (65%) according to Table 3, particularly 
pronounced in organizations with extensive legacy system 
investments. The case studies revealed that semantic 
integration methods (69%) and neural translator networks 
(74%) significantly outperformed traditional custom 
integration development, reducing implementation timelines 
by 28% while lowering maintenance requirements by 35%. 
Similarly, governance misalignment (63%) and resource 
constraints (58%) posed significant challenges that were 
more effectively addressed through AI-enhanced resolution 
strategies than traditional approaches. 

 



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As shown in Figure 4(a), examination of collaboration 

maturity progression revealed a clear inverse relationship 
between implementation complexity and success rates as 
supply chains advanced from initial connectivity toward 
autonomous collaboration. While initial connectivity stages 
demonstrated high implementation rates (91%) but modest 
success (42%), organizations achieving autonomous 
collaboration reported substantially higher success rates 
(91%) despite lower implementation rates (15%). This 
pattern highlights the critical importance of strategic phasing 
when implementing cross-tier data integration initiatives. 
Figure 4(b) demonstrates how integration barriers varied 
considerably by organizational size, with small organizations 
facing disproportionate challenges with resource constraints 
(63%) and power asymmetry (71%), while large 
organizations encountered greater technical compatibility 
(45%) and governance alignment (52%) challenges. These 
differentiated patterns necessitate tailored integration 
approaches rather than one-size-fits-all solutions, 
particularly when addressing the complexities of multi-tier 
supply chains. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 
Figure 4(c) demonstrates varying collaboration pattern 

effectiveness, with federation-driven approaches achieving 
the highest integration success rates (87%). Perhaps most 
significantly, as shown in Figure 4(d), organizations 
implementing AI-enhanced strategies reached effective 
integration thresholds approximately 5.3 months earlier than 
those utilizing traditional approaches, representing a 41% 
reduction in time-to-value. These findings underscore the 
transformative potential of intelligent collaborative 
mechanisms in breaking down long-standing data silos across 
multi-tier supply chains. 

3.3 Performance evaluation and predictive analysis of 
the hybrid intelligent architecture 
The hybrid intelligent architecture underwent 

comprehensive evaluation through controlled experiments 
across 47 manufacturing organizations, revealing significant 
improvements in both operational efficiency and predictive 
capabilities. Empirical evaluation demonstrates that the 
proposed architecture outperformed conventional 
integration approaches across multiple performance 

Table 3. Analysis of cross-tier collaboration barriers and resolution strategies 

Barrier 
Category 

Prevalence Primary 
Manifestations 

Traditional 
Resolution 

Approaches 

AI-Enhanced 
Resolution 
Strategies 

Performance 
Improvement (%) 

Trust Deficit 76% Data reliability 
concerns (68%) 

Visibility reciprocity 
fears (57%) 

Historical relationship 
issues (43%) 

Contractual 
agreements (53%) 

Executive 
relationship building 

(47%) 
Graduated 

information sharing 
(39%) 

Blockchain-based 
verification (73%) 

Smart contract 
enforcement (68%) 
Reputation systems 

(62%) 

Trust Deficit: +37% 
resolution rate, +43% speed 

Competitive 
Exposure 

72% Proprietary data 
protection (81%) 

Competitive 
intelligence leakage 

(74%) 
Bargaining power 
concerns (63%) 

Data anonymization 
(48%) 

Limited domain 
sharing (56%) 

Third-party 
intermediaries 

(52%) 

Federated learning 
models (81%) 

Differential privacy 
techniques (76%) 
Zero-knowledge 

proofs (67%) 

Competitive Exposure: 
+46% adoption, +58% 

retention 

Technical 
Incompatibility 

65% API/interface 
limitations (73%) 

Data format 
inconsistencies (68%) 

Legacy system 
constraints (59%) 

Custom integration 
development (62%) 
Data transformation 

services (57%) 
Middleware 

deployment (53%) 

Semantic 
integration (69%) 
Neural translator 
networks (74%) 

Adaptive 
middleware agents 

(66%) 

Technical Incompatibility: -
28% implementation time, -

33% maintenance 

Governance 
Misalignment 

63% Decision rights 
uncertainty (72%) 
Value distribution 

disputes (65%) 
Risk allocation 

concerns (58%) 

Formal governance 
agreements (57% 

Structured 
coordination bodies 

(49%) 
Explicit benefit 

allocation (54%) 

Smart contract 
governance (71%) 
Algorithmic value 

distribution (67%) 
Automated 
compliance 

verification (63%) 

Governance Misalignment: 
+41% compliance, -37% 

overhead 

Resource 
Constraints 

58% Technical expertise 
limitations (76%) 

Integration investment 
capacity (68%) 

Operational bandwidth 
(57%) 

Phased 
implementation 

(54%) 
External integration 

services (49%) 
Capability 

prioritization (52%) 

Low-code 
integration 

platforms (73%) 
Auto-configuration 
connectors (68%) 

Microservices 
architecture (65%) 

Resource Constraints: +49% 
adoption, +52% deployment 

speed 

Power 
Asymmetry 

52% Unbalanced influence 
(78%) 

Disproportionate 
benefit distribution 

(69%) 
Dependency concerns 

(63%) 

Formal relationship 
agreements (47%) 

Industry consortium 
formation (42%) 

Multi-party 
governance (45%) 

Decentralized 
governance 

protocols (68%) 
Algorithmic fairness 
mechanisms (63%) 
Transparent benefit 

attribution (71%) 

Power Asymmetry: +56% 
fairness score, +43% 

participation 

 



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indicators: supply chain visibility increased by 39%, 
coordination costs decreased by 28%, disruption prediction 
lead time improved by 7.3 days, mean absolute prediction 
error reduced by 42%, and neural network ensemble 
accuracy enhanced by 39-58% across industrial sectors. As 
shown in Figure 5(a), the architecture demonstrated 
exceptional early warning capabilities. The pharmaceutical 
sector achieved the most substantial improvement with 9.8 
days of early warning, followed by electronics with 8.4 days, 
reflecting the architecture's adaptability to different industry 
contexts. As shown in Figure 5(b), the aerospace sector 
showed the most dramatic improvement (+58%), while the 
automotive and pharmaceutical sectors demonstrated gains 
of 50% and 39%, respectively. These substantial accuracy 
improvements directly translate into operational resilience, 
with participating organizations reporting 32% faster 
response to actual disruption events during the controlled 
experimental period. As shown in Table 4, there are 
particularly impressive results in inventory optimization 
(156% cost efficiency improvement) and transportation 
delay prediction (55% error reduction). The ensemble's 
effectiveness stems from its ability to integrate multi-modal 
data while preserving organizational privacy through 
federated learning techniques, effectively balancing 
collaborative intelligence with competitive concerns. The 
blockchain foundation of the architecture ensured data 
integrity and traceability, with validation mechanisms 
successfully identifying and isolating attempted data 
manipulation in 94% of test cases. This security layer, 
combined with the federated learning system's differential 
privacy implementation, maintained prediction accuracy 
even with 30% adversarial node participation during 
resilience testing.  

 

The architecture's semantic integration layer facilitated 
effective knowledge transfer across heterogeneous systems, 
with 86% of organizational data schemas successfully 
mapped without manual intervention. Long-term 
implementation assessment revealed continuous 
performance improvement, with organizations utilizing the 
architecture for more than six months reporting substantially 
higher benefits (visibility: +47%, coordination costs: -36%) 
than recent adopters. As shown in Table 4, the neural network 
ensemble demonstrated robust performance across diverse 
tasks beyond disruption prediction, including demand 
forecasting (36% error reduction), quality issue prediction 
(44% recall improvement), and risk assessment (48% false 
positive reduction). The architecture's effectiveness varied by 
organizational context, with medium-sized enterprises 
experiencing the most balanced benefits relative to 
implementation costs. Technical compatibility barriers 
presented the most significant implementation challenge, 
particularly in organizations with substantial legacy system 
investments, though the architecture's modular design 
provided viable integration pathways for heterogeneous 
environments. The reputation-based trust mechanism proved 
beneficial in competitive fields where information sharing 
due to intellectual property concerns had previously 
hindered collaboration, allowing for cooperation without 
revealing sensitive information. 

3.4 Study Limitations 
While these results demonstrate significant 

improvements, several limitations must be acknowledged 
before interpreting the findings.  

 
 

 
Figure 4. Key factors for cross-tier collaboration (a) Collaboration maturity progression (b) Integration barriers by organization size  

(c) Collaboration patterns and AI enhancement (d) Integration timeline comparison 



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Figure 5. Performance metrics of the hybrid intelligent architecture (a) Disruption lead time advantage across industry sectors  

(b) Disruption prediction accuracy comparison between traditional methods and neural network ensemble 

 

Table 4. Comparative analysis of neural network ensemble performance 

Prediction 
Task 

Neural Network Ensemble 
Performance 

Traditional Methods 
Performance 

Improvement (%) Key Contributing Factors 

Supply 
Disruption 
Detection 

MAPE: 14.3% 
Lead time: 7.3 days 

MAPE: 24.6% 
Lead time: 2.1 days 

42% error reduction 
247% lead time 

Multi-modal data 
integration 

Transfer learning from 
similar patterns 

Demand 
Forecasting 

RMSE: 8.4% 
Bias: 2.1% 

RMSE: 13.2% 
Bias: 5.7% 

36% error reduction 
63% bias reduction 

External data correlation 
Attention mechanisms for 

trend shifts 

Inventory 
Optimization 

Cost reduction: 18.7% 
Service level: 96.2% 

Cost reduction: 7.3% 
Service level: 92.4% 

156% cost efficiency 
4% service 

improvement 

Demand-supply balancing 
Multi-echelon optimization 

Quality Issue 
Prediction 

Precision: 83.2% 
Recall: 76.8% 

Precision: 61.5% 
Recall: 53.4% 

35% precision gain 
44% recall 

improvement 

Graph neural networks 
Anomaly detection 

ensembles 

Transportation 
Delay 

Prediction 

Accuracy: 79.4% 
MAE: 62 minutes 

Accuracy: 58.7% 
MAE: 138 minutes 

35% accuracy gain 
55% error reduction 

Spatiotemporal modeling 
Weather data integration 

Resource 
Allocation 

Utilization rate: 87.3% 
Bottleneck reduction: 43.1% 

Utilization rate: 73.6% 
Bottleneck reduction: 

21.4% 

19% utilization gain 
101% bottleneck 

improvement 

Reinforcement learning 
Digital twin simulation 

Risk 
Assessment 

Risk identification: 83.7% 
False positive rate: 12.3% 

Risk identification: 64.2% 
False positive rate: 23.8% 

30% identification gain 
48% false positive 

reduction 

Bayesian networks 
Uncertainty quantification 

 

 



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The study's geographic concentration within Chinese 
manufacturing contexts may limit cross-cultural 
generalizability, as organizational behaviors and trust 
formation mechanisms vary across different cultural and 
regulatory environments. The eight-month experimental 
period, though sufficient for initial performance assessment, 
provides limited insight into long-term sustainability and 
potential system degradation. Additionally, the architecture's 
implementation complexity and resource requirements may 
present scalability challenges for smaller organizations, 
potentially reinforcing existing power asymmetries within 
supply networks. The sample size of 47 organizations, while 
methodologically adequate, represents a relatively small 
portion of the broader manufacturing ecosystem. These 
limitations are addressed more comprehensively in the 
discussion section, where their implications for theory and 
practice are explored in detail. 

4. Discussion  

This paper contributes to the debate on supply chain 
integration by examining trust as an algorithmically mediated 
phenomenon in multi-tiered relationships. The hybrid 
architecture’s reputation-based smart contract system marks 
a new innovation in trust formation that goes beyond 
interpersonal relations and formal agreements to include 
social capital-based trust research. Countless studies have 
documented an increasing reliance on direct specialization 
and outsourcing through algorithms, thereby diminishing 
human involvement in operations. By embedding trust 
parameters within technical protocols, the architecture 
creates what Yavaprabhas et al. [27] call “computational trust 
transfer,” wherein the basis for confidence shifts from direct 
human interactions to algorithmised verifications. This 
algorithmic trust mechanism builds upon established trust 
theory frameworks while extending them to technological 
contexts, where trust formation in digital environments 
requires different mechanisms than traditional interpersonal 
trust [28, 29]. The smart contract system operationalizes core 
trust dimensions through measurable parameters: ability via 
historical performance metrics, benevolence through 
reciprocal information sharing behaviors, and integrity via 
blockchain immutability [19]. Unlike traditional 
organizational trust formation that relies on repeated social 
interactions, algorithmic trust enables "institutional trust 
automation" where relationships are mediated through 
verifiable digital protocols [27], extending beyond calculative 
trust theories to enable dynamic trust calibration based on 
real-time performance data and addressing temporal and 
information asymmetry challenges in multi-tier supply 
relationships [30]. As discussed in a previous comment, these 
models address fundamental gaps in approaches that attempt 
to converge systems but lack the ability to scale, relatively 
cross organizational borders, especially for large-scale 
problems with heterogeneous technological, competitive, and 
participatory concerns. 

The conflict of information disclosure and the protection 
of competitive business knowledge remains ever-present 
regarding supply chain collaboration. How this conflict is 
resolved through new technologies is visible in the federated 
learning part of the architecture. Earlier integration attempts 
often resulted in organizations being boxed into the “data-
dump-or-competitive-protect” dichotomy. Instead, the 
proposed architecture allows what may be termed “selective 
transparency,” where partners leverage collective 
intelligence without exposing proprietary information. This 
echoes Zheng et al.'s findings [31] on privacy-preserving 

collective risk prediction, although it goes beyond their work 
by adding blockchain validation processes that strengthen 
trust in the federated model’s outputs. Results strongly 
indicate this balanced approach increases adoption in 
competitive industries more than traditional methods, 
undermining purely technical solutions to dismantle strategic 
data silos. 

International implementation requires careful 
contextual adaptation. In Western markets with stricter 
privacy regulations and individualistic cultures, the 
algorithmic trust mechanisms need enhanced transparency 
features and modified trust parameters. While Chinese 
organizations rely on relationship-based trust (guanxi), 
Western supply chains emphasize performance-based 
metrics, requiring recalibrated reputation weights in smart 
contracts. Additionally, developing economies may require 
simplified architectural variants due to infrastructure 
constraints. 

Despite promising results, several implementation 
challenges merit consideration. The architecture's 
deployment across heterogeneous organizational contexts 
revealed scaling difficulties, particularly among resource-
constrained participants. Organizations with limited 
technological capabilities often struggle to implement the 
complete architecture, potentially reinforcing rather than 
reducing power asymmetries within supply networks. This 
limitation echoes concerns raised by Nguyen et al. [32] 
regarding the computational demands of blockchain-
federated learning systems. Additionally, regulatory 
complexities across international supply chains presented 
integration barriers inadequately addressed by the current 
design. Future implementations must develop more flexible 
deployment models that accommodate varying resource 
constraints while maintaining system integrity. 

This study attempts to fill the gap in the theory of digital 
supply chains by merging bounded frameworks of 
technologies within an ecosystem. Data integration centrally 
enhances visibility, which is traditionally thought to be the 
ideal solution. However, this approach is sub-optimal when 
considering competitive contexts, where organisational 
autonomy sustaining integration becomes more viable. This 
viewpoint evolves Zhao et al.'s [33] model by placing 
algorithmic trust as an influential mediator into operational 
resilience and transformational supply chain digitisation. The 
autonomous systems model empirically substantiates the 
argument that a 39% improvement in visibility, relative to a 
baseline, can be achieved without centralising data silos. This 
may shift the paradigm in integration by fundamentals in 
future research. 

This study’s analysis comes with some identifiable gaps 
and potential areas for further work which need to be 
mentioned. This study’s sample has relatively good coverage 
across the different sectors of manufacturing, but was 
regionally confined to some of the industrial areas in China. 
These areas tend to have a unique set of norms and regulatory 
frameworks, which might make it difficult to extrapolate the 
results to other contexts. Implementing the trust algorithm in 
other regions and industries would be a worthwhile 
undertaking. Also, considering the lack of information that 
comes with the 8-month experimental duration, it would be 
best to conduct further research into more extended 
longitudinal studies for understanding the algorithmic trust 
model’s architecture sustainability. Focused multi-year 
research should improve the durability of the understanding 
surrounding these mechanisms. As AI-driven adversarial 
tactics continue to advance, more research is also required on 



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the architecture’s ability to withstand complex adversarial 
challenges. Technological evolution presents promising 
directions for enhancing the current architecture. Integration 
with digital twin technologies, as explored by Hellwig et al. 
[33] in their simulation platform, could enable more 
sophisticated scenario modeling for proactive disruption 
management. The emergence of quantum-resistant 
cryptographic protocols offers potential solutions to long-
term security concerns regarding blockchain 
implementations [34]. Additionally, edge computing 
approaches may address computational efficiency challenges 
identified during implementation, particularly for resource-
constrained participants [32]. Expanding the architecture to 
incorporate these emerging technologies presents fertile 
ground for future research that builds upon this study's 
foundation while addressing its identified limitations. As 
Pang et al. [34] note, the convergence of AI with distributed 
ledger technologies represents a fundamental shift in 
industrial capability that extends beyond simple optimization 
to enable entirely new operational paradigms - a vision this 
research takes meaningful steps toward realizing. 

5. Conclusion  

This research demonstrates that breaking data silos in 
multi-tier supply chains requires solutions balancing 
information transparency with organizational autonomy. 
Analysis of 47 manufacturing organizations identified six data 
silo types, with technological (31.4%) and organizational 
(23.8%) barriers being most prevalent. The hybrid 
architecture combining blockchain with federated learning 
delivered substantial improvements, enhancing supply chain 
visibility by 39% while reducing coordination costs by 28% 
compared to traditional systems. The neural network 
ensemble provided a disruption lead time advantage of 7.3 
days, with pharmaceutical manufacturers achieving the most 
substantial improvement (9.8 days). These results confirm 
the viability of achieving collective intelligence without 
compromising competitive data protection. The theoretical 
contribution lies in reconceptualizing trust as an 
algorithmically-mediated construct rather than a purely 
relational phenomenon. The provided proof contradicts 
established wisdom that integration requires an 
amalgamated array of databases. It seems that an architecture 
of distributed intelligence systems offers less ecologically 
damaging cooperation paths. With the smart contract system 
based on reputation, trust is maintained across organisational 
frontiers. This is especially important in advanced industries 
where proprietary information controls restrict the sharing of 
essential knowledge. Medium-sized enterprises experienced 
the most balanced implementation benefits, effectively 
addressing integration gaps encountered by organizations 
with moderate technological capabilities. Limitations include 
geographic concentration within Chinese manufacturing 
sectors and a relatively short experimental period, limiting 
cross-cultural generalizability. The findings reflect China's 
distinctive context of high power distance, relationship-based 
trust, and specific regulatory frameworks, which may require 
substantial adaptations for Western markets with flatter 
organizational structures, contract-based trust mechanisms, 
and different privacy regulations. Future research should 
extend implementation across diverse contexts while 
examining the long-term evolution of algorithmic trust 
mechanisms. Cross-cultural validation studies should 
examine algorithmic trust effectiveness across different 
regulatory frameworks and cultural contexts, particularly 
comparing relationship-based versus performance-based 

trust formation mechanisms. Investigation into quantum-
resistant cryptography would address security 
vulnerabilities, while integration with digital twin 
technologies presents promising directions for enhancing 
predictive capabilities. Despite these limitations, this 
research advances understanding of how intelligent 
collaborative mechanisms can transform multi-tier supply 
chain integration in increasingly complex business 
environments.  

Ethical issue 
The authors are aware of and comply with best practices in 
publication ethics, specifically with regard to authorship 
(avoidance of guest authorship), dual submission, 
manipulation of figures, competing interests, and compliance 
with policies on research ethics. The author adheres to 
publication requirements that the submitted work is original 
and has not been published elsewhere. 

Data availability statement 
The manuscript contains all the data. However, more data will 

be available upon request from the authors. 

Conflict of interest 

The authors declare no potential conflict of interest. 

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