Bakery Chain Research Articles

Research on the Marketing Strategy of Holiland Brand in Chinese Market

As the largest bakery chain in China, Holiland has a leading influence on the Chinese market. From the beginning of its establishment to the present, its brand positioning and brand image have been continuously optimized, and the visibility of the brand itself has been greatly improved across a variety of baked goods categories. The current marketing situation of Holiland has some problems, such as the brand positioning is not clear enough, the lack of Chinese characteristics and the lack of brand marketing precision. The suggestions are to clarify the brand positioning, optimize the brand image, improve online and offline services, reduce the capital investment of uncompetitive products, continue to develop new products, optimize the membership system, make reasonable use of star effect and use reasonable promotion strategies. This paper makes clear the present situation of Holiland enterprise in the market pattern, explores the causes of related marketing difficulties, and summarizes the specific implementation plan and effect evaluation system of marketing. It is of great guiding significance to Holiland's new media marketing strategy. It is hoped that this can provide some meaningful enlightenment for the development of China's baking industry marketing.

39 Bakers: roadmap to profitability through customer management

Learning outcomes 1. To analyze the various business models and choose the best model to contribute maximum profit to the company.2. To understand the importance of customer management with the help of the Net Promoter Score in the food retail context.3. To develop customer loyalty strategies and implement them to improve customer management? Case overview/synopsis 39 Bakers, a multi-outlet bakery chain in Jammu, India, is run by its founder Gagan. 39 Bakers, through its retail outlets across the Jammu region, offers a variety of products, ranging from bakery items that include blends of Indian and Italian cuisines, offering more than 1000 stock keeping units (SKUs). Through its high-quality offerings at an affordable price range, the brand has carved a niche in the hyper-competitive bakery market of the Jammu region. Gagan, has closely seen the Jammu market and customer preferences and strongly perceived that the customers in the Jammu region are very price sensitive. Thus, he has always been very reluctant to increase the prices of his product offerings at 39 Bakers. He has always believed that any drastic price rise may lead to immediate dissatisfaction and customer churn and therefore has not increased the prices at 39 Bakers for two years in a row. While this decision of Gagan paid off in terms of its popularity and recognition as one of the highly recommended bakery chains among customers, it drastically impacted the bottom line (i.e. profitability) at 39 Bakers, especially in the year 2020–21. Getting popularity at the cost of dipping profitability made Gagan rethink his decision to be protective of price increases at 39 bakers. How can he measure customer satisfaction and loyalty? Which loyalty strategies will work for the huge customer base of Jammu? Should he change his business model from B2C to B2B? How can loyalty be established? How can he manage his existing and loyal customers through price increases? Complexity academic level The case study is suitable for undergraduate and postgraduate courses in Marketing Management and Retail Marketing. The case study’s focus can be on the importance of pricing, business model evaluation, customer management analysis, customer loyalty, Customer Loyalty analysis, and net promoters score. The case can also be useful to entrepreneurs and regulators. Supplementary materials Teaching notes are available for educators only. Subject code CSS 3: Entrepreneurship.

Product Development using SWOT Analysis

The objective of this research is to give recommendation for a bakery chain operator called Greggs in terms of improving their breakfast menu selection to generate more sales. The research method uses SWOT analysis to produce a strategic development plan. Greggs is one of the largest United Kingdom bakery stores and performs well in generating revenue as the turnover. The uncertainty of Brexit, the minimum wage set by the government, the highest proportion of people who usually eat breakfast on the go, development of a mobile app, breakfast preference to a healthier fresh meal and reduce sugar consumption, competitor, extreme winter weather, and the government policy have an impact on Gregg revenue. Based on the SWOT analysis result it can be concluded that Gregg needs to utilize the position as the market leader to promote the benefit of high-quality healthy food, use the revenue to start on research for healthy products innovation, use the healthy breakfast trend to introduce vegan breakfast menu, add more drink option for the breakfast deal to make it more convenient for people to commute, keep focusing on the quality and taste development with a competitive price, add more menu that correspondent with consumer demand, and try to efficiently spend the revenue based on priority to cut operating cost.

Impacting the bottom line: Exploring the effect of a self‐efficacy oriented training intervention on unit‐level sales growth

AbstractThis study examines the impact of a specific training intervention on both individual‐ and unit‐level outcomes. We sought to examine the extent to which a training intervention incorporating key elements of error management training: (1) positively impacted sales specific self‐efficacy beliefs of trainees; and (2) positively impacted unit‐level sales growth over time. Results of an 11‐week longitudinal field experiment across 19 stores in a national bakery chain indicated that the sales self‐efficacy of trainees significantly increased between the levels they had 2 weeks before the intervention started and 4 weeks after it was initiated. Results based on a repeated measures ANOVA also indicated significantly higher sales performance in the intervention group compared with a non‐intervention control group. We also sought to address the extent to which individual‐level effects may be linked to the organizational level. We also provide evidence with respect to the extent to which changes in individual self‐efficacy were associated with unit‐level sales performance. Results confirmed this multi‐level effect as evidenced by a moderate significant correlation between the average self‐efficacy of the staff of each store and its sales performance across the weeks the intervention was in effect. The study contributes to the existing literature by providing direct evidence of the impact of an HRD intervention at multiple organizational levels.

Market Competition and the Effectiveness of Performance Pay

It is well established that the effectiveness of pay-for-performance (PfP) schemes depends on employee- and organization-specific factors. However, less is known about the moderating role of external forces such as market competition. Our theory posits that competition generates two counteracting effects—the residual market and competitor response effects—that vary with competition and jointly generate a curvilinear relationship between PfP effectiveness and competition. Weak competition discourages effort response to PfP because there is little residual market to gain from rivals, whereas strong competition weakens incentives because an offsetting response from competitors becomes more likely. PfP hence has the strongest effect under moderate competition. Field data from a bakery chain and its competitive environment confirm our theory and let us refute several alternative interpretations.

From member creativity to team creativity? Team information elaboration as moderator of the additive and disjunctive models.

One of the most fundamental questions in team creativity research is the relationship between individual member creativity and team creativity. The two answers that team creativity research has advanced–teams are more creative when their average member creativity is higher (the additive model) and teams are more creative when their most creative member is more creative (the disjunctive model) are straightforward. Surprising, however, is that neither the additive model nor the disjunctive model is consistently supported, begging the question of what moderates the predictive power of these models. We address this question by integrating individual-to-team creativity models with team process research. We propose that team information elaboration is a key moderating variable, such that average member creativity is more positively related to team creativity with higher information elaboration, and the highest member creativity is more positively related to team creativity with lower information elaboration. A multi-source study of 60 sales teams (483 employees) in a Chinese bakery chain supported these hypotheses. In addition, the study did not support the prediction that the most creative member’s outgoing advice ties (as a conduit for the dissemination of ideas) would further moderate the joint effect of the highest individual creativity and team information elaboration on team creativity.

Choice of performance measures for bonus plan: implications for firm performance and promotions

ABSTRACT In this study, we examine the impact of a change in the bonus-based incentives on firm performance and promotions. We use both a proprietary dataset and a field-based survey of a leading bakery chain in Japan that changed its bonus determination from focusing on nonfinancial measures to financial measures. We find that firm performance declines under the new bonus plan due to a misalignment between the choice of performance measures and the bakery’s customer-focused strategy. Moreover, supervisors consider performance measures other than what is intended by the firm for promotions i.e., financial measures) when they perceive these measures to be underweighted from the perspective of temporal alignment of the incentive system. This study contributes to prior literature by documenting the impact of a change in bonus plan on firm performance and promotions decisions, where empirical evidence is limited.

Rotiboy to Turkey

Rotiboy is a Malaysian bakery chain based in Kuala Lumpur owned by Rotiboy Bakeshoppe Sdn Bhd. Rotiboy was founded in Bukit Mertajam, Penang in 1998. The founder of Rotiboy- Hiro Tan, studied economics in Universiti Malaya and he used to work as an insurance agent, property agent, airline station manager, tuition teacher and even tried mushroom farming before starting Rotiboy Bakeshoppe. He was inspired by his family to start Rotiboy and his siblings supported him in the business venture.

Daily retail demand forecasting using machine learning with emphasis on calendric special days

Demand forecasting is an important task for retailers as it is required for various operational decisions. One key challenge is to forecast demand on special days that are subject to vastly different demand patterns than on regular days. We present the case of a bakery chain with an emphasis on special calendar days, for which we address the problem of forecasting the daily demand for different product categories at the store level. Such forecasts are an input for production and ordering decisions. We treat the forecasting problem as a supervised machine learning task and provide an evaluation of different methods, including artificial neural networks and gradient-boosted decision trees. In particular, we outline and discuss the possibility of formulating a classification instead of a regression problem. An empirical comparison with established approaches reveals the superiority of machine learning methods, while classification-based approaches outperform regression-based approaches. We also found that machine learning methods not only provide more accurate forecasts but are also more suitable for applications in a large-scale demand forecasting scenario that often occurs in the retail industry.

Data-driven Inventory Management under Customer Substitution

Most retailers that sell perishable goods offer multiple products in a product category (e.g., fresh food or fashion). Managing the inventories of these products is especially challenging due to frequent stock-outs and resulting substitution effects within the category. Furthermore, the true demand distributions of products are usually unknown to the decision maker. New digital technologies have enormously expanded the availability of data, storage capacity, and computing power and may thereby help improve inventory decisions. In this paper, we present a novel solution approach for the multi-product newsvendor problem. Our method is based on modern machine learning techniques that leverage large available datasets (e.g., data on historical sales, weather, store location, and special days) and are able to take complex substitution effects into account. We empirically evaluate our approach on two real-world datasets of a large German bakery chain. We find that our data-driven approach outperforms the model-based benchmark on the first dataset and performs competitively on the second dataset.

Determination of the Optimal State of Dough Fermentation in Bread Production by Using Optical Sensors and Deep Learning

Dough fermentation plays an essential role in the bread production process, and its success is critical to producing high-quality products. In Germany, the number of stores per bakery chain has increased within the last years as well as the trend to finish the bakery products local at the stores. There is an unsatisfied demand for skilled workers, which leads to an increasing number of untrained and inexperienced employees at the stores. This paper proposes a method for the automatic monitoring of the fermentation process based on optical techniques. By using a combination of machine learning and superellipsoid model fitting, we have developed an instance segmentation and parameter estimation method for dough objects that are positioned inside a fermentation chamber. In our method we measure the given topography at discrete points in time using a movable laser sensor system that is located at the back of the fermentation chamber. By applying the superellipsoid model fitting method, we estimated the volume of each object and achieved results with a deviation of approximately 10% on average. Thereby, the volume gradient is monitored continuously and represents the progress of the fermentation state. Exploratory tests show the reliability and the potential of our method, which is particularly suitable for local stores but also for high volume production in bakery plants.

A data-driven newsvendor problem: From data to decision

Retailers that offer perishable items are required to make ordering decisions for hundreds of products on a daily basis. This task is non-trivial because the risk of ordering too much or too little is associated with overstocking costs and unsatisfied customers. The well-known newsvendor model captures the essence of this trade-off. Traditionally, this newsvendor problem is solved based on a demand distribution assumption. However, in reality, the true demand distribution is hardly ever known to the decision maker. Instead, large datasets are available that enable the use of empirical distributions. In this paper, we investigate how to exploit this data for making better decisions. We identify three levels on which data can generate value, and we assess their potential. To this end, we present data-driven solution methods based on Machine Learning and Quantile Regression that do not require the assumption of a specific demand distribution. We provide an empirical evaluation of these methods with point-of-sales data for a large German bakery chain. We find that Machine Learning approaches substantially outperform traditional methods if the dataset is large enough. We also find that the benefit of improved forecasting dominates other potential benefits of data-driven solution methods.

A Data-Driven Newsvendor Problem: From Data to Decision

Retailers that offer perishable items are required to make ordering decisions for hundreds of products on a daily basis. This task is non-trivial because the risk of ordering too much or too little is associated with overstocking costs and unsatisfied customers. The well-known newsvendor model captures the essence of this trade-off. Traditionally, this newsvendor problem is solved based on a demand distribution assumption. However, in reality, the true demand distribution is hardly ever known to the decision maker. Instead, large datasets are available that enable the use of empirical distributions. In this paper, we investigate how to exploit this data for making better decisions. We identify three levels on which data can generate value, and we assess their potential. To this end, we present data-driven solution methods based on Machine Learning and Quantile Regression that do not require the assumption of a specific demand distribution. We provide an empirical evaluation of these methods with point-of-sales data for a large German bakery chain. We find that Machine Learning approaches substantially outperform traditional methods if the dataset is large enough. We also find that the benefit of improved forecasting dominates other potential benefits of data-driven solution methods.

Heterogeneous Effects of Performance Pay with Market Competition: Evidence from a Randomized Field Experiment

It is well-established that the effectiveness of pay-for-performance (PfP) schemes depends on employee- and organization-specific factors. However, less is known about the role of external forces. Investigating the role of market competition on the effectiveness of PfP, we theorize that there are two counteracting effects – business stealing and competitor response – that jointly generate an inverted U-shape relationship between PfP effectiveness and competition. Weak competition discourages effort response to PfP because there is little extra market to gain, while strong competition creates low incentives because competitors are more likely to respond. PfP hence has the strongest effect under moderate competition. Field data from a bakery chain and its competitive environment confirm our theory, allow us to empirically separate the business stealing and competitor response effects, and refute alternative explanations.

Daily bread – a gourmet pursuit

Synopsis Arjun Sekri, a professional-turned-entrepreneur, set out to establish the first branded gourmet industrial bakery in Bengaluru, India in late 2002. His lack of experience in the unorganized food and beverages industry did not deter him from establishing a highly popular industrial and retail bakery chain called “Daily Bread”. The case is about his roller coaster ride in establishing a premium retail food brand in India. Though many of the stores did reasonably well, many things went wrong predominantly on the operations front due to the severe impact of global price inflation, manifested by high real estate rentals, raw material, packing and logistics costs and wage costs. After two years of rapid expansion, in order to curb the bleeding bottom line, Arjun decided to shut down all the newly created stores and production units except the one in Bengaluru which was doing well. By late 2009, the dilemma Arjun faced was what should he do next? Research methodology The case study is based on primary data collected from the protagonist and a few other stakeholders involved in the case along with secondary data from published sources. Relevant courses and levels Entrepreneurship courses at MBA level or executive programs. Theoretical bases The case deals with the life cycle management of a venture with special emphasis on opportunity evaluation, setting up, funding and stakeholders selection.

Cluster-based hierarchical demand forecasting for perishable goods

Demand forecasting is of particular importance for retailers in the context of supply chains of perishable goods and fresh food. Such goods are daily produced and delivered as they need to be provided as fresh as possible and quickly deteriorate. Demand underestimation and overestimation negatively affect the revenues of the retailer. Stock-outs have an undesired impact on consumers while unsold items need to be discarded at the end of the day. We propose a DSS that supports day-to-operations by providing hierarchical forecasts at different organizational levels based on most recent point-of-sales data. It identifies article clusters that are used to extend the hierarchy based on intra-day sales pattern. We apply multivariate ARIMA models to forecast the daily demand to support operational decisions. We evaluate the approach with point-of-sales data of an industrialized bakery chain and show that it is possible to increase the availability while limiting the loss at the same time. The cluster analysis reveals that substitutable items have similar intra-day sales pattern which makes it reasonable to forecast the demand at an aggregated level. The accuracy of top-down forecasts is comparable to direct forecasts which allows reducing the computational costs.

The effect of announced downsizing on worker teams performance - Evidence from a retail chain

We estimate the effect of downsizing announcement on workplace performance using data from a German bakery chain of 193 shops. Faced with intensified competition, it decided to sell or close down 5...

The Effect of Announced Downsizing on Workplace Performance: Evidence from a Retail Chain

We estimate the effect of downsizing announcement on workplace performance using data from a German bakery chain of 193 shops. Faced with intensified competition, the firm decided to sell or close down 57 of its worst performing shops. We identify the effect of downsizing from a plausibly exogenous variation in the timing of the sale or closure announcement in the individual shops. We find that the announcements of the shop being sold to a new owner and being closed down reduce sales by six and 21 percent, respectively. The negative effect of downsizing increases with the share of workers with a permanent contract, even though permanent workers faced a much lower unemployment risk. We relate our findings to the literatures on downsizing and psychological contract.

Polycyclic aromatic hydrocarbons in the bakery chain

The level of polycyclic aromatic hydrocarbons occurrence and the possibility of their formation in the bakery chain, its raw materials and final products, were examined. Experimental bread baking, with different baking temperatures, was performed in the Warsaw bakery, using cyclothermic deck ovens. PAHs determination was performed by high-pressure liquid chromatography with fluorescent and diode array detectors (HPLC–FLD/DAD) and confirmed by gas chromatography coupled with mass spectrometry (GC/MS). Total content of 19 PAHs in the grain, flour and bran varied from 1.07 to 3.65μg/kg and, in bread, from 1.59 to 13.6μg/kg depending on the part of bread and baking temperature. Based on the dough’s contamination level and the influence of the baking temperature on the bread’s PAHs content, it was confirmed that PAHs are formed during baking. Considering the results of the average dietary exposure to PAHs and the MOE (Margin of Exposure) analysis, it could be concluded that analysed bread and cereal products constitute little concern for consumer health.

Simit: Turkey's National Bread

The simit, a sesame-covered bread resembling an oversized bagel, is Turkey's unofficial national food. Traditionally sold and eaten on the street, it has been around since at least the Ottoman times. It has been documented in writings and illustrations for centuries; more recently, politicians have used simits as a measure of basic subsistence. Turkey has been cracking down on street food vendors though. And the simit has been facing competition from international snacks like pizza. And companies including Starbucks and the home-grown bakery chain Simit Sarayi have also been making it harder for more traditional simit-bakers and simit-sellers to survive.