Consumer Packaged Goods Research Articles

Leadership in Crisis: Navigating the Challenges of COVID-19

Abstract—The COVID-19 pandemic presented unprecedented challenges to global industries, with the Consumer Packaged Goods (CPG) sector being significantly impacted due to lock- downs, supply chain disruptions, and workforce safety concerns. This paper examines how a leading CPG manufacturer navigated the crisis, highlighting the leadership strategies and operational adjustments employed to sustain production, ensure employee safety, and maintain market presence. Key areas of focus in- clude the implementation of automation, establishment of robust quarantine protocols, adoption of flexible work schedules, and incentivization of front-line employees. Additionally, the paper explores the complexities of managing supply chains and vendor relationships during the pandemic and illustrates how, while many competitors were forced to shut down or faced shortages due to inadequate preparedness and inability to adapt safety protocols, the manufacturer remained operational by swiftly reacting to the evolving situation, resulting in increased sales. Drawing on this case study, the paper provides actionable steps for organizations to prepare for future similar scenarios, emphasizing the importance of resilience and proactive leadership in crisis management. Index Terms—Leadership, Crisis Management, COVID-19, CPG Industry, Automation, Supply Chain Resilience, Employee Incentives, Flexible Work Schedules, Food Manufacturing, Or- ganizational Resilience

Challenges and Solutions in Scaling Operations Globally: A Guide for U.S. Food and BeverageExecutives

Abstract—This paper explores the challenges and solutions associated with scaling operations globally in the food and beverage sector from a U.S. executive’s perspective. It examines why expanding internationally makes sense for companies with successful domestic products, highlighting the advantages of U.S. innovation and lower costs of innovation compared to local markets abroad. The paper outlines a strategic roadmap for global expansion, including test markets, partnerships with local firms, and focusing on countries with positive sentiments toward U.S. food safety standards. A hypothetical case study illustrates the growth process, emphasizing the need for internal buy-in, patience, and a long-term view. Potential pitfalls and strategies for success are discussed, along with competitive analysis and the role of disruption in scaling products. Index Terms—Global Expansion, Food and Beverage Industry, Consumer Packaged Goods (CPG), International Operations, U.S. Innovation, Market Entry Strategy, Competitive Analysis, Disruption

Prediction of future customer needs using machine learning across multiple product categories.

In recent years, computational approaches for extracting customer needs from user generated content have been proposed. However, there is a lack of studies that focus on extracting unmet needs for future popular products. Therefore, this study presents a supervised keyphrase classification model which predicts needs that will become popular in real products in the marketplace. To do this, we utilize Trending Customer Needs (TCN)-a monthly dataset of trending keyphrase customer needs occurring in new products during 2011-2021 across multiple categories of Consumer Packaged Goods e.g. toothpaste, eyeliner, beer, etc. We are the first study to use this specific dataset and employ it by training a time series algorithm to learn the relationship between features we generate for each candidate keyphrase on Reddit to the ones in the dataset 1-3 years in the future. We show that our approach outperforms a baseline in the literature and through Multi-Task Learning can accurately predict needs for a category it wasn't trained on e.g. train on toothpaste, cereal, and beer products yet still predict for shampoo products. The findings from this research could provide many advantages to businesses such as gaining early access into markets.

Consumer Perception and Satisfaction of D2c (Digital-to-consumer) in Consumer-packaged Goods (CPG): a Study Focused on Bengaluru Urban Region

Consumer Perception and Satisfaction of D2c (Digital-to-consumer) in Consumer-packaged Goods (CPG): a Study Focused on Bengaluru Urban Region

Category–level drivers of the market share–rank power law relationship

PurposeThis paper aims to analyze variations in the parameters of the market share–rank power law across consumer packaged goods (CPG) categories.Design/methodology/approachThe authors use a two-level hierarchical linear model to examine the relationships between category-level variables and the parameters of the market share–rank power law in 790 CPG categories.FindingsThe slope of the market share–rank power law is shallower – indicating more equal market shares – in categories of high importance to retailers and those with high levels of promotional activity or high-volume purchases. Higher levels of market share inequality are associated with categories with high overall prices.Research limitations/implicationsTo the best of the authors’ knowledge, this is the first research to show the systematic influence of category characteristics on the relationship between brands’ market shares and their ranks, thus, identifying a key moderator for this important empirical generalization in marketing.Practical implicationsWhile market leadership may be a desirable goal for many brands, the corresponding market share at the top brand does vary. Moreover, the share premium for being number one in the category (gap between the top and other highly ranked brands) can be greatly affected by retailers’ strategies. In addition, the slope of the power law has desirable qualities as a measure of market concentration. However, the empirical study shows that category characteristics must be considered when analyzing differences in concentration across categories or time.Originality/valueWhile other studies document variations in the market share–rank power law relationship, to the best of the authors’ knowledge, this is the first that models these variations as a function of observable category characteristics. The comprehensive nature of the data demonstrates the universality of the market share–rank power law relationship across CPG categories in the USA.

RETRACTION NOTICE: Planning for Migration of Export Materials into Finished products CPG (Consumer Packaged goods)

The production of materials can lead to unnecessary inventory in plants and reduce living space in the warehouse. It is for this reason that the objective of this research is an innovative proposal that has to do with an optimized process to reduce wasted materials in inventory. For this, a market investigation will be carried out to do analysis with average base data for changes in materials in finished products. In the end, our proposal will achieve better productivity levels, better calculations of lead time with the supplier and alignment with the plant, and greater visibility of materials in development.

Large-scale Probabilistic Forecasting of Consumer Engagement of CPG Products using Heterogeneous Web Data

Consumer Packaged Goods (CPG) play a pivotal role in customer-centric industries. Understanding the distinctive features of such products and how customers engage with them is essential for CPG manufacturers to create customer-winning products. In this study, we explore an innovative data-centric approach to forecast customer engagement for CPG products by monitoring their digital evolution, particularly in the Snacks category in the USA. Traditional methods for consumer analysis such as surveys, focus groups are time consuming, costly and ineffective with limited scope. They also lack a forecasting component, making it difficult for CPG companies to make forward-looking decisions. However, with the emergence of big data, we can leverage user generated public data from social media, web search, e-commerce platforms to estimate consumer engagement and make long-term forecasts. To achieve this, we propose a systematic approach to accumulate and prepare large datasets from heterogeneous web sources for CPG products. We then use state-of-the-art deep learning based time series forecasting models to efficiently train and predict consumer engagement for the next 12 months, benchmarking their computational efficiency and forecasting performance. Our findings indicate that the DeepAR model outperforms all other models, with the lowest NRMSE (=0.378), RMSE (=14.848) MSE (=220.457), MASE (=0.871), and sMAPE (=0.306) values. Furthermore, we demonstrate methods for computing single forecasting points and prediction intervals using the forecasted sample distribution from the probabilistic models. The proposed approach will provide CPG businesses with valuable insights to make informed decisions about product development, marketing strategies, and supply chain management.

New product family demand planning: AddressingSKU‐level spread bias

AbstractNew product supply chain planning is challenging, primarily due to the lack of historical demand data. Rarely, however, do the academic literature or companies differentiate the demand forecasting process for new products from existing ones, despite their increased reliance on judgmental estimates. This research focuses on how judgmental errors lead to an under‐estimation of the difference between the highest‐ and lowest‐demand stock‐keeping units (SKUs), and consequently negatively impact supply chain planning for new product family introductions. A generalized empirical model and accompanying discrete event simulation are developed and applied to data from a major consumer packaged goods (CPG) firm during the launch of a new cosmetics product family. This application allows us to identify a focal type of judgmental error (identified as the SKU‐level spread bias) inherent to new product forecasting and to provide a new theoretical understanding of how this type of bias harms supply chain performance. Via an empirically driven theory‐building approach that iterates between the simulation outcomes and existing literature, SKU‐level spread bias is demonstrated to harm demand forecasts and, thereby, supply chain plans. Our unique theory‐building approach advances theory by identifying planner SKU‐level spread bias as a new source of bias that firms should seek to mitigate when introducing new product families.

Distribution velocity in wine retailing

Little is known about the relationship between distribution and market share in the wine category. Understanding the influences of product and distribution characteristics at the SKU-level and incorporating them into marketing strategy and planning has important managerial and academic implications. Sales of 3,524 wine SKUs across 4,218 stores in 4 states in the US over one year of observation are analyzed. We use an established distribution velocity model (Reibstein & Farris 1995) to estimate the relationship between distribution and market share. We then use the market share deviations from the expected values and apply a secondary robust regression to investigate possible relationships between various product- and distribution characteristics and those market share deviations. The results show that the distribution velocity in wine retailing is convex and increasing, in line with previous findings for other consumer-packaged goods in the marketing literature. Beyond distribution breadth, we find that overall parent brand performance (above), unit price (above), packaging type (above), country-of-origin, grape variety, sales consistency (above) and store specialization (below) are associated with above or below expected market share of wine SKUs.

THE EFFECT OF GREENWASHING ON BRAND EQUITY AND GREEN PURCHASE INTENTION AT GARNIER IN JAKARTA

Climate change and global warming are causing various natural disasters. One of the causes is packaging waste from consumer-packaged goods products (in the form of food, drinks, and dairy products such as soap, cosmetics, etc.) which are difficult to decompose. Public awareness is needed to preserve the environment, one of which is by using environmentally friendly products such as Garnier through the "green beauty" movement. The purpose of this research is to examine how greenwashing affects brand equity, including brand trust and brand association with Garnier products, and to examine the effect of brand equity on green purchase intention. The quantitative and descriptive method was used by distributing questionnaires via Google form to 128 respondents in Jakarta using the Structural Equation Model (SEM) analysis tool to test the hypothesis. The results showed that the five hypotheses proved significant and acceptable, namely, green brand equity has a positive effect on green purchase intention, brand credibility, and brand associations are also proven to have a positive effect on green brand equity, while greenwashing has a negative effect on brand credibility and brand associations, especially in consumer-packaged goods. Thus, Garnier is expected not to do greenwashing so that it can maintain consumer trust in Garnier products and continue to improve campaign programs that invite people to care about the environment, one of which is by buying environmentally friendly packaging products such as Garnier.

Calibrating Sales Forecasts in a Pandemic Using Competitive Online Nonparametric Regression

Motivated by our collaboration with Anheuser-Busch InBev (AB InBev), a consumer packaged goods (CPG) company, we consider the problem of forecasting sales under the coronavirus disease 2019 (COVID-19) pandemic. Our approach combines nonparametric regression, game theory, and pandemic modeling to develop a data-driven competitive online nonparametric regression method. Specifically, the method takes the future COVID-19 case estimates, which can be simulated via the susceptible-infectious-removed (SIR) epidemic model as an input, and outputs the level of calibration for the baseline sales forecast generated by AB InBev. In generating the calibration level, we focus on an online learning setting where our algorithm sequentially predicts the label (i.e., the level of calibration) of a random covariate (i.e., the current number of active cases) given past observations and the generative process (i.e., the SIR epidemic model) of future covariates. To provide robust performance guarantee, we derive our algorithm by minimizing regret, which is the difference between the squared [Formula: see text]-norm associated with labels generated by the algorithm and labels generated by an adversary and the squared [Formula: see text]-norm associated with labels generated by the best isotonic (nondecreasing) function in hindsight and the adversarial labels. We develop a computationally efficient algorithm that attains the minimax-optimal regret over all possible choices of the labels (possibly non-i.i.d. and even adversarial). We demonstrate the performances of our algorithm on both synthetic and AB InBev’s data sets of three different markets (each corresponds to a country) from March 2020 to March 2021. The AB InBev’s numerical experiments show that our method is capable of reducing the forecast error in terms of weighted mean absolute percentage error (WMAPE) and mean squared error (MSE) by more than 37% for the company. This paper was accepted by J. George Shanthikumar, data science. Funding: This work was partially supported by the Massachusetts Institute of Technology Data Science Lab and AB-InBev Corporation. Supplemental Material: The e-companion and data are available at https://doi.org/10.1287/mnsc.2023.4969 .

How does the stakeholder exposure of vertical integration influence environmental performance?

In this study, we examine the relationship between Vertical Integration (VI) and Environmental Performance (EP) to understand the impact of Stakeholder exposure on organizational behavior. We collected data on emissions, environmental initiatives, and financial information in the consumer-packaged goods (CPG) industry from two independent secondary sources between 2002 and 2012. We utilized a financial measure for VI and operationalized EP both with objective measures (carbon emissions and waste) and semi-objective measures (the number of environmental initiatives implemented). We show that highly integrated firms produce less waste and show greater alignment in “saying” (stating objectives publicly) versus “doing” (reducing carbon footprint and waste). Yet, firms also engage in fewer environmental initiatives and do not emit less CO2. Taken together, this provides a more nuanced understanding of the relationship between VI and EP and how organizational pressures influence strategic action.

Final paper of What are the Beneficial Effects of Big Data on the Front-End Information and Efficiency, Performance of the Company in the CPG Industry

This research paper is to find out the benefits of data analysis on the front-end information and internal efficiency of the consumer-packaged goods (CPG) industry. Combining various scholarly types of sources, our research first figures out the steps before analyzing the data, then discusses the advantages of extensive data analysis on both external and internal performance of a CPG company, and finally points out some potential limitations to extensive data analysis on economic grounds.

Gender-Based Pricing in Consumer Packaged Goods: A Pink Tax?

This paper studies the extent to which firms segment, differentiate, and price discriminate on the basis of gender in the personal care category.

Price volatility of revenue managed goods: Implications for demand and price elasticity

Revenue management practices are widely employed in various sectors. These mechanisms dynamically adjust prices observed by consumers typically through the control of price classes and their availability. As such, these pricing environments tend to exhibit some predictable behaviors as the product approaches the expiration or consumption date but may also result in varying degrees of price volatility. Such price paths may ultimately alter consumer behavior, e.g., via delayed purchase timing (i.e., strategic behavior) or different willingness to pay. Accordingly, we assess consumers’ responses to the realized price changes induced by revenue management mechanisms, using fare and sales data from aviation markets. Our empirical analyses reveal that price elasticity decreases (in absolute terms) in the degree of price volatility, whereas the realized demand is lower when price volatility is higher. This suggests that as prices become more volatile, consumers become more oblivious to these price fluctuations and may end up paying more for the products—thereby confirming and generalizing a behavior previously documented for consumer packaged goods (CPGs), which follow dramatically different pricing regimes. While this may suggest support for higher and more volatile prices, we find that given the higher prices, the overall demand decreases, highlighting a delicate trade-off firms face. To distill the insights from the empirical analyses, we formulate two hypotheses, which we test in a laboratory setting by means of an ad hoc experiment.

The Metabolic Matrix: Re-engineering ultraprocessed foods to feed the gut, protect the liver, and support the brain.

Ultraprocessed food is established as a metabolic disruptor acting to increase adiposity, reduce mitochondrial efficiency, drive insulin resistance, alter growth, and contribute to human morbidity and mortality. Consumer packaged goods (CPG) companies are beginning to understand the detrimental impact of the food they market, and have employed substitution strategies to reduce salt, sugar, and fat. However, the harms of ultraprocessed foods are far more complex than any single component, and are not ameliorated by such simple substitutions. Over the past 2 years, the authors have worked with the Kuwaiti Danish Dairy Company (KDD) to conduct a comprehensive scientific evaluation of their entire commercial food and beverage portfolio. Assay of the macronutrients, micronutrients, additives, and toxins contained in each of their products was undertaken to determine the precise nature of each product's ingredients as well as the health impacts of processing. The authors formed a Scientific Advisory Team (SAT) and developed a tiered "Metabolic Matrix" founded in three science-based principles: (1) protect the liver, (2) feed the gut, and (3) support the brain. The Metabolic Matrix categorizes each product and provides the criteria, metrics, and recommendations for improvement or reformulation. Real-time consultation with the KDD Executive and Operations teams was vital to see these procedures through to fruition. This scientific exercise has enabled KDD to lay the groundwork for improving the health, well-being, and sustainability of their entire product line, while maintaining flavor, economic, and fiscal viability. This process is easily transferrable, and we are sharing this effort and its approaches as a proof-of-concept. The key aim of our work is to not only make ultraprocessed food healthier but to urge other food companies to implement similar analysis and reformulation of their product lines to improve the metabolic health and well-being of consumers worldwide.

The competing roles of variety seeking in new brand adoption

We examine how consumers' attribute-level variety-seeking behaviors can explain their propensity to adopt a new brand in the consumer packaged goods (CPG) market. We leverage consumers' purchase history among extant brands prior to a new brand introduction as an indicator of the new brand adoption upon its launch. We incorporate variety seeking at two product attribute levels – brand and flavor – and find that variety seeking along these two dimensions predicts different outcomes for new brand adoptions. The estimation results of our discrete-time hazard model show that consumers' brand-level and flavor-level variety-seeking behaviors affect their likelihood to adopt a new brand in different manners: consumers who purchase various brands are more likely to adopt a new brand, whereas those who choose various flavors are less likely to adopt a new brand. The results also show that the new brand's price promotions and in-store displays can affect the role of variety seeking in consumers' new brand adoption. We assess the robustness of our findings by replicating our empirical model with an additional measure of variety seeking as well as a variety-seeking measure in another product category.

Innovation Potential, Insider Sales, and IPO Performance: How Firms Can Mitigate the Negative Effect of Insider Selling

At the time of an initial public offering (IPO), firms seek to maximize their stock market value. The authors theorize and show that a firm's innovation potential—firm outputs and activities that contribute to the development of future new products—can be used by the managers of IPO firms as a credible signal of the quality of the firm. Using a sample of 370 IPO firms from the consumer-packaged goods and pharmaceutical industries, and three metrics of innovation potential, the authors show that firms’ innovation potential is (1) positively associated with the initial value of the IPO and with the first-day IPO returns and (2) negatively associated with the extent to which insiders sell their shares at the time of the IPO. The effectiveness of the three metrics of innovation potential as a signal of firm quality varies: patents have a stronger impact on insider sales than preannouncements and generic references to future innovation, while preannouncements have the strongest impact on first-day IPO returns. The article presents a nuanced view of the extent to which various firm stakeholders consider information about firms’ innovation potential to be a credible signal that reduces the adverse selection present in IPO deals with insider sales.

When do established CPG product brands benefit from a corporate-brand endorsement? The moderating role of consumer needs and brand familiarity

PurposeMany consumer packaged goods (CPG) companies have increased the visibility of their corporate brand to consumers by adding a corporate logo (e.g. Unilever “U”) on the packaging of their product brands (e.g. Dove, Domestos, Lipton, Knorr) and showcasing their product-brand portfolio in additional corporate-brand advertising. This paper aims to investigate consumer needs and product-brand familiarity as moderators of the impact of such a strong form of corporate-brand endorsement on the evaluation of established CPG product brands.Design/methodology/approachTwo experiments with n = 670 and n = 118 respondents compare consumer evaluations of eight (Study 2: ten) product brands of a major German (Study 2: Anglo-Dutch) CPG company with versus without corporate-brand endorsement. The authors measure (Study 2: manipulate) the importance a consumer attaches to symbolic, experiential, relational and functional needs in brand choice.FindingsCorporate-brand endorsement improves (decreases) the evaluation of established CGP product brands among consumer segments who base their brand choice in a CPG category predominantly on functional or relational (experiential or symbolic) needs. Furthermore, it exerts a negative effect on product brands among heavy category users due to their higher product-brand familiarity.Practical implicationsRather than endorsing all its product brands with the corporate brand, a CPG company should do so selectively, depending on the positioning and target audience of each product brand.Originality/valueTo the best of the authors’ knowledge, this paper is the first to demonstrate the importance of consumer needs and product-brand familiarity in corporate-brand endorsement.

Brand Equity in Good and Bad Times: What Distinguishes Winners from Losers in Consumer Packaged Goods Industries?

This article examines why some brands are able to ride the wave of macroeconomic expansions, whereas other brands are better able to successfully weather contractions. Using a utility-based framework, the authors develop hypotheses on how the impact of these shocks on brand equity is moderated by six strategic brand factors: price positioning, advertising spending, product line length, distribution breadth, brand architecture, and market position. The authors utilize monthly data on 325 national consumer packaged goods brands in 35 categories across 17 years from the United Kingdom to obtain quarterly sales-based brand equity estimates. The two preeminent brand factors are distribution and assortment. Distribution is by far the most important factor in contractions. It is also the most important factor in expansions. In short, in good times and bad times, extensively distributed brands win. A wide assortment is also a strong contributor to brand equity in expansions, while it does not destroy brand equity in contractions. The authors further find that advertising spending, premium price positioning, umbrella branding structure, and market leadership matter in expansions and/or contractions, and the magnitude of their effects on brand equity is relatively modest. The discussion concludes with managerial implications.