QUESTION : It appears that the market research plan for the TruEarth pizza launc

Question

QUESTION :

It appears that the market research plan for the TruEarth pizza launch was in great part influenced by the research done, and results realized for the whole grain pasta launch. Was this the right approach for the team to take?

If so, why do you think this? Support your position with a comparison of the research done and results realized for the pasta and pizza concepts, and the relevant indications

If not, create a modified research proposal for the pizza launch. Include the purpose of the study; type of study recommended; definition of target market population and sample size; and projected managerial benefits. Discuss why you think your proposal gives the company the best chance to correctly assess the market potential for success.

TruEarth Healthy Foods:

Market Research for a New Product Introduction

Isabel Eckstein strode toward her office, inhaling the aroma from the test kitchen where the product development team was trying new recipes for TruEarth, maker of gourmet pastas, sauces, and meals. Her team had been working hard for the past year on a fresh whole grain pizza. The final market research results had arrived, and it was time to make a decision on launching the product.

In 2006, Eckstein, a brand manager, had led the introduction of Cucina Fresca, a fresh whole grain pasta meal kit sold through supermarkets. By the end of 2007, sales had reached $23 million, making it the most successful product launch in company history. However, growth had slowed in 2008 as competitors began offering similar products. Sustaining a competitive edge in the fresh Italian food category required continual innovation and, as a result, TruEarth had invested heavily in its pizza offering. Eckstein observed:

We were aggressive in launching Cucina Fresca. We made a significant investment in new manufacturing equipment and the distribution infrastructure required to get fresh food to shelves quickly. Being a small company competing against Nestle and Kraft is not easy, but we do not make rash decisions. The success of Cucina Fresca was a calculated risk based on significant research. We think we can achieve similar success with pizza, but we will need to take a hard look at the numbers.

Company Background

TruEarth was founded in 1993 in St. Louis, Missouri, by Gareth DeRosa, a young entrepreneur. DeRosa saw an opportunity to market healthier gourmet pastas and sauces made from superior ingredients:

In the 1980s, demand for healthy, gourmet products grew sharply. We saw a segment of the market shift away from mass-produced, highly processed foods toward greater quality and authenticity. It was a special opportunity for us because we knew we could deliver what more and more customers wanted.

DeRosa sourced a particularly high-quality durum wheat from North Dakota and was notoriously selective about ingredients for sauces. The product line featured standard pastas, such as spaghetti, rigatoni, and shells, and specialty pastas with blended ingredients, such as artichoke, spinach, or saffron. In addition, TruEarth was one of the first companies to focus on whole grain products, offering both 60% and 100% whole grain pastas in its line. The tomato-based sauces were made from the highest-quality ingredients—real tomatoes instead of paste or puree, extra virgin olive oil instead of canola or soybean, and no sweeteners or dried spices.

The concept was successful, and TruEarth became a supplier to a number of gourmet groceries throughout the midwestern United States. By 1998, the company had built a loyal regional following. Responding to customer demand, several mainstream supermarket chains began to carry TruEarth products. The company further raised consumer awareness through several promotional programs, using coupons, magazine advertisements, and in-store demonstrations.

Product Development Process

In its early years, TruEarth’s product development was informal and largely driven by intuition. The team enjoyed experimenting with new products and “limited edition” variations on the core recipes, and the batch process used to manufacture most products made it easy to experiment without affecting overall production. The company fostered a freewheeling, entrepreneurial spirit, and despite occasional failures, management believed that regularly trying new products was a low- risk way to identify the next hits. Volume estimates were done “back of the envelope,” using a mix of high-level analysis and intuition. Not infrequently, TruEarth’s projections were significantly different from actual market performance. The company would underestimate the appeal of a hit product and find itself struggling to keep up with demand, or its estimates would be too optimistic and frustrated retailers would seek markdowns and high guaranteed sell-through performance to avoid excess inventory.1

As TruEarth grew, achieved scale, and began serving larger regional accounts, the cost of such missteps increased. In response, the company developed a more formal four-step process for research and development:

Idea generation. As the company grew, the idea generation process became a more systematic evaluation of consumer trends, with formal management brainstorming sessions.

Concept screening. TruEarth administered formal surveys that included an evaluation of interest, probable purchasing behaviors, and willingness to pay.

Product development and testing. The test kitchen and marketing department developed prototype products, which would then be tested through focus groups.

Quantification of volume. TruEarth worked with Nielsen BASES®, a market research firm, to estimate potential sales. A BASES I test gauged consumer awareness and interest. A more extensive BASES II test included a taste test and could also be used as a “line extension” study for any pre-existing product lines.

1 Retailers consider excess inventory “unsaleable” if it has been on the shelf too long and/or expired. To avoid costs of disposal, many force manufacturers to guarantee a certain percentage “sell-through” (i.e., product that sells to the final consumer). Excess inventory below this sell-through is subject to markdowns (e.g. 50% discount) where much of the cost may be borne by the manufacturer.

Explanation / Answer

Product expiration is an important problem in the consumer packaged goods (CPG) industry costing retailers and manufacturers approximately $6 billion annually1 . This cost impacts firms’ profits substantially since the industry operates with tight profit margins (retailers typically have 1-2% gross margin and manufacturers 10%). Chapter 1 provides a detailed discussion on the financial and environmental impact of product waste (i.e., unsaleables) including the category of expired products. In this study, we investigate the root causes of product expiration.

The CPG industry typically uses audits and surveys to diagnose the occurrence of unsaleables [49, 19]. In audit studies, unsaleables are visually inspected at sampled stores or return centers and a reason code is recorded for each instance of an unsaleable. Such a visual inspection usually reveals the cause of damage, such as a packing failure (e.g., weak plastic, case handle, carton burst, nail damage on pallet, etc.). But a visual inspection of an expired product is not informative. A product can expire on the shelf due to causes that have occurred anywhere during the sojourn of the product from the factory to the shelf. Batching in production or transportation, inefficient inventory management at the warehouse, or suboptimal shelf allocation at the retail store could all cause product expiration.

These causes cannot be identified by examining a product on the shelf after it expires. Thus, audits have been successful in addressing the causes of damage and product discontinuation, but not expiration. Surveys, on the other hand, collect information about respondent beliefs on the causes of unsaleables. Not surprisingly, manufacturers and retailers have different views on the leading causes of unsaleables [30]. Manufacturers rank rotation practices at retailers as the major cause of expiration2 , whereas retailers rank code dating3 standards and procedures.

Similarly, manufacturers rank product handling as the leading root cause of damage whereas retailers rank package design. Thus, each associates the main cause of certain types of unsaleable with the other. Our interviews at our collaborator AlphaCo suggest that such differences persist even within the same company—the sales organization identifies operational practices as the driver of product expiration whereas the operations organization argues that sales incentives are the main reason. Thus, due to dependent events and lack of transparency in the supply chain, expiration remains an unsolved problem with cost and waste implications for manufacturers and retailers.

The objective of our study is to propose an econometric model to improve the understanding of the root causes of expiration in the CPG industry. Unlike the methods employed by existing studies, our analysis is based on archival data collected from the entire supply chain to examine the extent to which the occurrence of expiration is associated with store operations, supply chain performance, and product characteristics. Our primary data source is AlphaCo’s archival system which includes deliveries to and returns from 66,867 retail stores, warehouse inventory counts, product deployment at 449 AlphaCo locations, and shelf life and case size information for 768 products. The data set is from the year 2011. Is observed product expiration in a CPG business such as AlphaCo natural to expect due to the randomness of demand, or is there an opportunity to reduce expiration? We first compare observed expiration with a theoretical benchmark constructed by simulating a base stock policy on the sample paths of demand observed in detailed transaction level data for a small subsample of products. We find that the average actual expiration is 95.83 times the simulated expiration, significantly different at ?? < 0.01. This result suggests that expiration occurs due to reasons other than the randomness of demand, and thus, could be reduced by improving operations at manufacturers and retailers.

We identify six potential drivers of product expiration: case size, supply chain aging, sales incentives, forecasting complexity, minimum order rules, and shelf rotation. Here, we define supply chain aging as the elapsing of a product’s life in the supply chain before the product reaches the retail shelf. These variables represent different aspects of practical supply chains, including store execution, back-end supply chain operation, and product characteristics.

We select these variables based on interviews with AlphaCo, industry reports, and models of perishable inventory management. In addition, we control for mean demand rate, store type, and product shelf life in the analysis. Our estimation method is based on count models because expiration is a nonnegative integer and is bounded above by the total shipment quantity.

Using data from 870,493 store-product combinations, we evaluate several econometric specifications: binomial, Poisson, negative binomial, and zero-inflated models. We find that the zero-inflated negative binomial (ZINB) model yields the best fit and the most unbiased residuals by addressing two characteristics of our data: probability mass at zero and overdispersion.

Our main result is that case size, supply chain aging, sales incentives, forecasting complexity, and minimum order rule are all statistically significant determinants of product expiration. The control variables, demand rate, shelf life, and store type also affect expiration significantly. These results are robust to different clustered standard errors. Thus, our study shows that expiration occurring at retail shelves can be caused by both manufacturers and retailers. To refine our results, we analyze the interaction of sales incentives with demand rate—sales incentives can increase demand rate, which can lead to underestimating the effect of sales incentives on expiration. We also examine a potential inverse relationship between supply chain aging and expiration, i.e., that more expiration may result in more inventory upstream in the supply chain, which may increase supply chain aging.

The Hausman test does not reveal any significant evidence of endogeneity. We present counterfactual analysis to assess the performance improvement that can be achieved by addressing various types of causes of product expiration. We find that reducing the case size to 12 units for products that are currently packed in 24 units yields a 41% decrease in expiration volume and a $7.66M decrease in expiration cost. This constitutes an opportunity for CPG companies to reduce waste by reducing case sizes. Reducing the days of supply in the supply chain by one week corresponds to a 0.98% decrease in expiration volume and a $912.4K decrease in expiration cost. Relaxing the minimum order rule can reduce expiration volume by 0.87% and expiration cost by $610.7K.

Accordingly, visit frequencies can be reduced at stores that have a high frequency of orders equal to their minimum order sizes. Last, we find that three sales incentives cost $645K, $2,634K, and $25.7K with more expiration of 14%, 9%, 7% in volume, respectively. Our study is the first descriptive study of product expiration in the inventory management academic literature. While the existing literature on perishables inventory management has focused on inventory policies, our research contributes to the literature by developing an econometric model of expiration, and providing insights into its sources beyond demand uncertainty. Our analysis is distinct in combining sku-level data from stores and supply chain and marketing data from manufacturers. The results of this analysis can facilitate solutions to the problem of expiration by improving the understanding of its root causes

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