Global Variable Data Printing Market was valued at $22.51 Billion in 2022, and is projected to reach $60.56 Billion by 2030, at a CAGR of 13.17% from 2023 to 2030.

The global variable data printing market size crossed USD 15.2 billion in 2025 and is likely to register a CAGR of over 12.2%, exceeding USD 48.06 billion revenue by 2035, attributed to growing e-commerce industry supports market growth.

The size of the Variable Data Printing (VDP) Software market was valued at USD XXX million in 2024 and is projected to reach USD XXX million by 2033, with an expected CAGR of XX% during the forecast period.

Discover the booming Variable Data Printing (VDP) machine market! Learn about its $2.5B (2025) size, 7% CAGR, key drivers, and top players like HP and Xerox. Explore market trends and future projections in our comprehensive analysis.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Variable Data Shrink Sleeve Printing Market Outlook

According to our latest research, the global Variable Data Shrink Sleeve Printing market size reached USD 1.87 billion in 2024, demonstrating robust expansion driven by the increasing demand for personalized packaging solutions across various industries. The market is expected to grow at a CAGR of 7.1% from 2025 to 2033, projecting a market value of approximately USD 3.49 billion by 2033. This growth is primarily fueled by advancements in digital printing technologies, the rising trend of product customization, and stringent regulations regarding packaging authenticity and traceability.

The surge in demand for unique and personalized packaging is one of the key growth factors propelling the Variable Data Shrink Sleeve Printing market. As brands and manufacturers strive to differentiate their products on crowded shelves, the ability to incorporate variable data such as barcodes, QR codes, serialized numbers, and customized graphics has become crucial. This trend is particularly prominent in the food and beverage sector, where consumer engagement and anti-counterfeiting measures are vital. The flexibility offered by variable data printing enables brands to launch limited edition products, regional campaigns, and promotional activities, thus enhancing consumer interaction and brand loyalty.

Technological advancements in printing methods have significantly contributed to the market's upward trajectory. The integration of digital printing technology has revolutionized the shrink sleeve printing process, enabling high-speed, cost-effective, and high-quality production of short runs and complex designs. Flexographic and gravure printing also continue to evolve, offering improved color accuracy and substrate versatility. These innovations have made it easier for manufacturers to respond quickly to market trends and regulatory requirements, while reducing waste and operational costs. As a result, the adoption of variable data shrink sleeve printing is expanding across industries that require agility and precision in their packaging operations.

Another major growth driver is the increasing emphasis on regulatory compliance and product security. Governments and industry bodies worldwide are implementing stricter regulations to combat counterfeiting and ensure product authenticity, especially in sensitive sectors such as pharmaceuticals and personal care. Variable data printing allows for the integration of tamper-evident features and traceability elements directly onto shrink sleeves, providing a robust solution to meet these compliance standards. Moreover, the rise of e-commerce and global supply chains has further heightened the need for secure and trackable packaging, reinforcing the role of variable data shrink sleeve printing in modern packaging strategies.

Regionally, the Asia Pacific market stands out as a major contributor to global growth, supported by rapid industrialization, expanding retail sectors, and a burgeoning middle-class population. North America and Europe also exhibit strong demand, driven by advanced manufacturing infrastructure and a high focus on product innovation. Meanwhile, emerging markets in Latin America and the Middle East & Africa are witnessing increasing adoption, albeit at a relatively slower pace, as local brands recognize the value of sophisticated packaging in enhancing brand image and consumer trust.

Printing Technology Analysis

The printing technology segment of the Variable Data Shrink Sleeve Printing market encompasses digital printing, flexographic printing, gravure printing, offset printing, and other emerging technologies. Digital printing has emerged as the fastest-growing sub-segment, owing to its unparalleled ability to deliver high-quality, customizable prints with minimal setup time. The technology’s capacity for on-demand printing and short production runs makes it ideal for brands seeking to implement targeted marketing campaigns or comply with regi

Data in social and behavioral sciences are routinely collected using questionnaires, and each domain of interest is tapped by multiple indicators. Structural equation modeling (SEM) is one of the most widely used methods to analyze such data. However, conventional methods for SEM face difficulty when the number of variables (p) is large even when the sample size (N) is also rather large. This article addresses the issue of model inference with the likelihood ratio statistic Tml. Using the method of empirical modeling, mean-and-variance corrected statistics for SEM with many variables are developed. Results show that the new statistics not only perform much better than Tml but also are substantial improvements over other corrections to Tml. When combined with a robust transformation, the new statistics also perform well with non-normally distributed data.

This paper explores a unique dataset of all the SET ratings provided by students of one university in Poland at the end of the winter semester of the 2020/2021 academic year. The SET questionnaire used by this university is presented in Appendix 1. The dataset is unique for several reasons. It covers all SET surveys filled by students in all fields and levels of study offered by the university. In the period analysed, the university was entirely in the online regime amid the Covid-19 pandemic. While the expected learning outcomes formally have not been changed, the online mode of study could have affected the grading policy and could have implications for some of the studied SET biases. This Covid-19 effect is captured by econometric models and discussed in the paper. The average SET scores were matched with the characteristics of the teacher for degree, seniority, gender, and SET scores in the past six semesters; the course characteristics for time of day, day of the week, course type, course breadth, class duration, and class size; the attributes of the SET survey responses as the percentage of students providing SET feedback; and the grades of the course for the mean, standard deviation, and percentage failed. Data on course grades are also available for the previous six semesters. This rich dataset allows many of the biases reported in the literature to be tested for and new hypotheses to be formulated, as presented in the introduction section. The unit of observation or the single row in the data set is identified by three parameters: teacher unique id (j), course unique id (k) and the question number in the SET questionnaire (n ϵ {1, 2, 3, 4, 5, 6, 7, 8, 9} ). It means that for each pair (j,k), we have nine rows, one for each SET survey question, or sometimes less when students did not answer one of the SET questions at all. For example, the dependent variable SET_score_avg(j,k,n) for the triplet (j=Calculus, k=John Smith, n=2) is calculated as the average of all Likert-scale answers to question nr 2 in the SET survey distributed to all students that took the Calculus course taught by John Smith. The data set has 8,015 such observations or rows. The full list of variables or columns in the data set included in the analysis is presented in the attached filesection. Their description refers to the triplet (teacher id = j, course id = k, question number = n). When the last value of the triplet (n) is dropped, it means that the variable takes the same values for all n ϵ {1, 2, 3, 4, 5, 6, 7, 8, 9}.Two attachments:- word file with variables description- Rdata file with the data set (for R language).Appendix 1. Appendix 1. The SET questionnaire was used for this paper. Evaluation survey of the teaching staff of [university name] Please, complete the following evaluation form, which aims to assess the lecturer’s performance. Only one answer should be indicated for each question. The answers are coded in the following way: 5- I strongly agree; 4- I agree; 3- Neutral; 2- I don’t agree; 1- I strongly don’t agree. Questions 1 2 3 4 5 I learnt a lot during the course. ○ ○ ○ ○ ○ I think that the knowledge acquired during the course is very useful. ○ ○ ○ ○ ○ The professor used activities to make the class more engaging. ○ ○ ○ ○ ○ If it was possible, I would enroll for the course conducted by this lecturer again. ○ ○ ○ ○ ○ The classes started on time. ○ ○ ○ ○ ○ The lecturer always used time efficiently. ○ ○ ○ ○ ○ The lecturer delivered the class content in an understandable and efficient way. ○ ○ ○ ○ ○ The lecturer was available when we had doubts. ○ ○ ○ ○ ○ The lecturer treated all students equally regardless of their race, background and ethnicity. ○ ○

This study incorporates variables such as global value chain participation rates obtained from the Asian Development Bank (ADB) input-output tables and U.S. FDI inflows obtained from Statista. Other economic indicators include China's GDP growth rate, population growth rate, economic openness, and technological readiness

This dataset contains simulated datasets, empirical data, and R scripts described in the paper: “Li, Q. and Kou, X. (2021) WiBB: An integrated method for quantifying the relative importance of predictive variables. Ecography (DOI: 10.1111/ecog.05651)”.

A fundamental goal of scientific research is to identify the underlying variables that govern crucial processes of a system. Here we proposed a new index, WiBB, which integrates the merits of several existing methods: a model-weighting method from information theory (Wi), a standardized regression coefficient method measured by ß* (B), and bootstrap resampling technique (B). We applied the WiBB in simulated datasets with known correlation structures, for both linear models (LM) and generalized linear models (GLM), to evaluate its performance. We also applied two other methods, relative sum of wight (SWi), and standardized beta (ß*), to evaluate their performance in comparison with the WiBB method on ranking predictor importances under various scenarios. We also applied it to an empirical dataset in a plant genus Mimulus to select bioclimatic predictors of species’ presence across the landscape. Results in the simulated datasets showed that the WiBB method outperformed the ß* and SWi methods in scenarios with small and large sample sizes, respectively, and that the bootstrap resampling technique significantly improved the discriminant ability. When testing WiBB in the empirical dataset with GLM, it sensibly identified four important predictors with high credibility out of six candidates in modeling geographical distributions of 71 Mimulus species. This integrated index has great advantages in evaluating predictor importance and hence reducing the dimensionality of data, without losing interpretive power. The simplicity of calculation of the new metric over more sophisticated statistical procedures, makes it a handy method in the statistical toolbox.

Methods To simulate independent datasets (size = 1000), we adopted Galipaud et al.’s approach (2014) with custom modifications of the data.simulation function, which used the multiple normal distribution function rmvnorm in R package mvtnorm(v1.0-5, Genz et al. 2016). Each dataset was simulated with a preset correlation structure between a response variable (y) and four predictors(x1, x2, x3, x4). The first three (genuine) predictors were set to be strongly, moderately, and weakly correlated with the response variable, respectively (denoted by large, medium, small Pearson correlation coefficients, r), while the correlation between the response and the last (spurious) predictor was set to be zero. We simulated datasets with three levels of differences of correlation coefficients of consecutive predictors, where ∆r = 0.1, 0.2, 0.3, respectively. These three levels of ∆r resulted in three correlation structures between the response and four predictors: (0.3, 0.2, 0.1, 0.0), (0.6, 0.4, 0.2, 0.0), and (0.8, 0.6, 0.3, 0.0), respectively. We repeated the simulation procedure 200 times for each of three preset correlation structures (600 datasets in total), for LM fitting later. For GLM fitting, we modified the simulation procedures with additional steps, in which we converted the continuous response into binary data O (e.g., occurrence data having 0 for absence and 1 for presence). We tested the WiBB method, along with two other methods, relative sum of wight (SWi), and standardized beta (ß*), to evaluate the ability to correctly rank predictor importances under various scenarios. The empirical dataset of 71 Mimulus species was collected by their occurrence coordinates and correponding values extracted from climatic layers from WorldClim dataset (www.worldclim.org), and we applied the WiBB method to infer important predictors for their geographical distributions.

In principle, experiments offer a straightforward method for social scientists to accurately estimate causal effects. However, scholars often unwittingly distort treatment effect estimates by conditioning on variables that could be affected by their experimental manipulation. Typical examples include controlling for post-treatment variables in statistical models, eliminating observations based on post-treatment criteria, or subsetting the data based on post-treatment variables. Though these modeling choices are intended to address common problems encountered when conducting experiments, they can bias estimates of causal effects. Moreover, problems associated with conditioning on post-treatment variables remain largely unrecognized in the field, which we show frequently publishes experimental studies using these practices in our discipline's most prestigious journals. We demonstrate the severity of experimental post-treatment bias analytically and document the magnitude of the potential distortions it induces using visualizations and reanalyses of real-world data. We conclude by providing applied researchers with recommendations for best practice.

This repository provides the raw data, analysis code, and results generated during a systematic evaluation of the impact of selected experimental protocol choices on the metagenomic sequencing analysis of microbiome samples. Briefly, a full factorial experimental design was implemented varying biological sample (n=5), operator (n=2), lot (n=2), extraction kit (n=2), 16S variable region (n=2), and reference database (n=3), and the main effects were calculated and compared between parameters (bias effects) and samples (real biological differences). A full description of the effort is provided in the associated publication.

Variable Message Signs (VMS) in York.

For further information about traffic management please visit the City of York Council website.

*Please note that the data published within this dataset is a live API link to CYC's GIS server. Any changes made to the master copy of the data will be immediately reflected in the resources of this dataset.The date shown in the "Last Updated" field of each GIS resource reflects when the data was first published.

Statistics on Global Value Chains: Percentage distribution of companies that outsource or considered doing so, for reasons of and degree of importance. Triennial. National.

Variables and data sources.

This folder contains the scripts and data necessary to implement Sparse Factor Analysis (SFA) as outline in Kim, Londregan, and Ratkovic (2018). The README file contains all relevant information.

This study combines a decade of daily weather, traffic, and air quality data from Norway's six largest cities. The data is sourced from the Norwegian Public Roads Administration, the Norwegian Institute of Air Research, and the Norwegian Meteorological Institute. Careful selection and verification of monitoring stations were conducted to ensure accuracy and consistency. Initially focusing on the top ten populous cities, monitoring sites for traffic and air pollution were scrutinized. Weather variables were then aligned with selected sites, resulting in a dataset spanning 2009 to 2018. It includes key pollutants like NO, NO2, NOx, PM2.5, and PM10. This dataset has significant potential for further analysis and informing policy decisions, making it valuable for researchers and policymakers studying the connections between weather, traffic, and air quality in urban areas.

The dataset used is US Census data which is an extraction of the 1994 census data which was donated to the UC Irvine’s Machine Learning Repository. The data contains approximately 32,000 observations with over 15 variables. The dataset was downloaded from: http://archive.ics.uci.edu/ml/datasets/Adult. The dependent variable in our analysis will be income level and who earns above $50,000 a year using SQL queries, Proportion Analysis using bar charts and Simple Decision Tree to understand the important variables and their influence on prediction.

Variable Inc Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Synthetic data for assessing and comparing local post-hoc explanation of detected process shift

DOI

10.5281/zenodo.15000635

Synthetic dataset contains data used in experiment described in article submitted to Computers in Industry journal entitled

Assessing and Comparing Local Post-hoc Explanation for Shift Detection in Process Monitoring.

The citation will be updated immediately after the article will be accepted.

Particular data.mat files are stored in a subfolder structure, which clearly assigns the particular file to

on of the tested cases.

For example, data for experiments with normally distributed data, known number of shifted variables and 5 variables are stored in path ormal\known_number\5_vars\rho0.1.

The meaning of particular folders is explained here:

normal - all variables are normally distributed

not-normal - copula based multivariate distribution based on normal and gamma marginal distributions and defined correlation

known_number - known number of shifted variables (methods used this information, which is not available in real world)

unknown_number - unknown number of shifted variables, realistic case

2_vars - data with 2 variables (n=2)

...

10_vars - data with 10 variables (n=2)

rho0.1 - correlation among all variables is 0.1

...

rho0.9 - correlation among all variables is 0.9

Each data.mat file contains the following variables:

LIME_res nval x n results of LIME explanation

MYT_res nval x n results of MYT explanation

NN_res nval x n results of ANN explanation

X p x 11000 Unshifted data

S n x n sigma matrix (covariance matrix) for the unshifted data

mu 1xn mean parameter for the unshifted data

n 1x1 number of variables (dimensionality)

trn_set n x ntrn x 2 train set for ANN explainer,

         trn_set(:,:,1) are values of variables from shifted process

         trn_set(:,:,2) labels denoting which variables are shifted 

         trn_set(i,j,2) is 1 if ith variable of jth sample trn_set(:,j,1) is shifted

val_set n x 95 x 2 validation set used for testing and generating LIME_res, MYT_res and NN_res