Article

Rocket Science Retailing Is Almost Here–Are You Ready?

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Abstract

Despite all the data that retailers and e-tailers can now gather about point-of-purchase information, buying patterns, and customers' tastes, they still haven't figured out how to offer the right product, in the right place, at the right time, for the right price. Most retailers largely ignore the billions of bytes of customer data stored in their databases-or they handle that information incorrectly. As a result, they don"t adequately supply what consumers demand. But some retailers are moving profitably toward what the authors call "rocket science retailing"- a blend of traditional forecasting systems, which are largely based on the gut feel of employees, with the prowess of information technology. Marshall Fisher, Ananth Raman, and Anna Sheen McClelland recently finished surveying 32 retail companies in which they tracked their practices and progress in four areas critical to rocket science retailing: demand forecasting, supply-chain speed, inventory planning, and data gathering and organization. In this article, the authors look at some companies that have excelled in those four areas and offer some valuable advice for other businesses seeking retailing perfection. In particular, the authors emphasize the need to monitor crucial metrics such as forecast accuracy, early sales data, and stockouts-information that will help retailers determine when to tweak their supply-chain processes to get the right products to stores at just the right time. The authors discuss the information technologies now available for tracking that information. They point out the flaws in some reporting and planning systems and suggest alternate methods for measuring stockouts, inventory, and losses.

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... There are various reasons explaining why the retailer makes price adjustment regularly, and it is incontestable that the retailer gathers more accurate information about demand as the sale season progresses, which is a process of demand identification. For example, Fisher et al. reported an example in the apparel industry where highly accurate demand forecasts were made after a certain review time [2]. With the explosive growth of global information, the data-driven analytics is developing rapidly, which can give business leaders powerful support to make decision [3]. ...
... With the application of big data and artificial intelligence, it is more convenient and possible for e-retailers to forecast and update the future demand information under an E-commerce environment [4]. In fact, the accurate demand forecasts contain two kinds of information at least: (1) target markets and their scales, and (2) demand function about price. The first kind of information describes how many potential consumers will patronize the item when the retailer keeps the same price level. ...
Article
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Consider a retailer selling a seasonal item, new items are stocked at the beginning of sale season and no inventory replenishment is permitted. Assuming the initial price is exogenous and the information about demand becomes more accurate as the sales season progresses, the retailer is allowed to make an in-season price adjustment after conducting a review. After the review time, if the price is adjusted to be lower than the initial price, demand increases more quickly with price decreasing which reflects the promotional effect of discount. Given the initial inventory, an optimal price adjusting model is proposed to maximize the retailer’s revenue. Taking decisions on inventory into account, the proposed model is extended to maximize the retailer’s profit rather than revenue. Numerical examples are also illustrated to test the proposed model. The results show that the optimal in-season price mainly depends on the proportion of the remaining demand, the price sensitivity, and the effect of sales promotion. An important managerial implication is that the retailer should gather the demand information about the price and raise the in-season price as soon as possible to gain more revenue when the price elasticity is small enough. Otherwise, when the price elasticity is larger, the retailer should maintain or decrease the price to gain more revenue.
... newyorker.com/news/news-desk/keeping-the-coronavirus-from-infecting-health-careworkers?itm_content=footer-recirc 2.2 | Impact of COVID-19 on retail operations First, most retail activities are classified based on the nature of the operations. They are based on the demand forecast, supply chain speed, inventory policy, and data availability (Fisher et al., 2000). Here, the challenge is understanding the early sales for better forecasting. ...
... For the development of the evaluation model, seven enablers are identified, followed by 24 criteria and 80 attributes for retail operations in the COVID-19 outbreak (Fisher et al., 2000;Ibidunni et al., 2021;Sreedharan et al., 2019). Seven enablers have measured the retail performance index: consumer analysis, retail shops, government policy, luxury brands, financial performance, supply chain, and post-era COVID-19. ...
Article
Novel coronavirus disease (COVID‐19) and resulting lockdowns have contributed to major retail operational disturbances around the globe, forcing retail organizations to manage their operations effectively. The impact can be measured as a black swan event (BSE). Therefore, to understand its impact on retail operations and enhance operational performance, the study attempts to evaluate retail operations and develop a decision‐making model for disruptive events in Morocco. The study develops a three‐phase evaluation approach. The approach involves fuzzy logic (to measure the current performance of retail operations), graph theory (to develop an exit strategy for retail operations based on different scenarios), and ANN and random forest‐based prediction model with K‐cross validation (to predict customer retention for retail operations). This methodology is preferred to develop a unique decision‐making model for BSE. From the analysis, the current retail performance index has been computed as “Average” level and the graph‐theoretic approach highlighted the critical attributes of retail operations. Further, the study identified triggering attributes for customer retention using machine learning‐based prediction models (MLBPM) and develops a contactless payment system for customers' safety and hygiene. The framework can be used on a periodic basis to help retail managers to improve their operational performance level for disruptive events.
... Forecasting accuracy has a major impact on inventory investments (Bonney 2009;Fisher, Raman & McClelland 2000). Inaccurate forecasts are associated with excess inventory, increased transportation costs and lost sales (Bonney 2009). ...
... To improve forecasting accuracy, organisations have to track and analyse forecasting errors so that it is known when and why these errors are most likely to occur. Ideally, the organisation also needs to know the margin of error so that the potential impact of forecasting errors can be determined and action plans can be developed (Fisher et al. 2000). ...
Article
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Background: An established South African fast-moving consumer goods (FMCG) manufacturer, Company A, sought to address an underperforming supply chain through an attempt to improve demand planning accuracy. A need arose within the organisation for an assessment of the impact of this intervention. A literature gap was also identified, taking into account the limited number of relevant case studies that exist in academic literature. Objectives: The primary objective of this study was to establish whether or not the demand planning interventions of Company A had a positive impact on the performance of the supply chain under investigation. In support of this primary objective, the secondary objectives were to determine and compare the pre- and post-intervention demand planning activities and performance of Company A. Method: A case study research design was used focusing on Company A. The researcher sourced evidence through a secondary quantitative data analysis and primary data collection through interviews with personnel directly involved in the demand planning process at Company A. Results: From mean absolute percentage error calculations, it was found that the focal company’s demand planning process intervention had reduced the errors in demand forecasting for all classifications of stock keeping units analysed. Conclusion: The demand planning interventions applied by Company A had a positive impact on the overall supply chain performance. A positive relationship was found between a well-informed, team-based and technology-assisted approach to demand planning and improved forecasting accuracy within an FMCG company.
...  The individual interests of supply chain members, local views and opportunistic behaviors lead to mismatch between supply and demand ( [35]).  The traditional performance measurement based on individual performance may be consistent with the maximization of supply chain profit. ...
... Effective SCC has many benefits. Including: remove excess inventory, shorten delivery time, increase sales, improve customer service and efficient product development efforts, low manufacturing cost, improve the flexibility of dealing with high demand uncertainty, increase customer retention and increase their income ( [35]; [36]; [39]). ...
... Σα απνηειέζκαηα ηεο κνλνκεξνχο ζηξαηεγηθήο θαηεχζπλζεο πξνο ηε κείσζε ηνπ θφζηνπο παξαγσγήο, ήηαλ ε πψιεζε πεξηζζφηεξεο πνζφηεηαο πξντφληνο ππφ θαζεζηψο έθπησζεο, ρακέλεο πσιήζεηο, ρακειά επίπεδα ηθαλνπνίεζεο ησλ πειαηψλ θαη ιηγφηεξα θέξδε (Lowson et al, 1999;Hunter et al, 2002). χκθσλα κε ηνλ Fisher (2000), πεξίπνπ ην 33% ηνπ εκπνξεχκαηνο πσινχληαλ ζε ρακειφηεξε ηηκή, ελψ ζχκθσλα κε κηα έξεπλα (Fisher & Raman, 1996), ηα ιάζε ζηηο εθηηκήζεηο (ηελ αξρή ηεο πεξηφδνπ) ηεο αλακελφκελεο δήηεζεο θπκαίλνληαλ γχξσ ζην 50% (ηελ ίδηα ζηηγκή πνπ κεηψλνληαλ ζην 8%, φηαλ ε εθηίκεζε βαζηδφηαλ ζην 20% ησλ πξαγκαηηθψλ πσιήζεσλ). ...
... Όπσο είδακε ζε θάπνηεο πεξηπηψζεηο φπνπ ε δήηεζε είλαη ζρεδφλ αδχλαην λα πξνβιεθζεί κε αθξίβεηα, ε γξήγνξε αλαπιήξσζε ησλ πξντφλησλ, είλαη κηα πνιχ απνηειεζκαηηθή κέζνδνο, αθφκα θη αλ θαηλνκεληθά απμάλεη ην ιεηηνπξγηθφ θφζηνο. χκθσλα κε ηνλ Fisher (2000), ε ηαρχηεηα θαη ε εηνηκφηεηα πνπ κπνξεί λα επηδείμεη κηα εθνδηαζηηθή αιπζίδα είλαη θξίζηκεο ζεκαζίαο, γηα ηελ επίηεπμε ηνπ απψηεξνπ ζθνπνχ ησλ logistics, ηε δηάζεζε ησλ θαηάιιεισλ πξντφλησλ, ηελ θαηάιιειε ζηηγκή, ζην θαηάιιειν κέξνο, ζηελ θαηάιιειε ηηκή. χκθσλα κε πνιιέο έξεπλεο πάλησο, παξφιν πνπ ε κέζνδνο ηεο γξήγνξεο αλαπιήξσζεο ζπκβάιιεη ζηε κείσζε ησλ ρακέλσλ πσιήζεσλ θαη ζηελ αχμεζε ηεο πνηφηεηαο ησλ παξερφκελσλ ππεξεζηψλ, νη πεξηζζφηεξνη ιηαλνπσιεηέο ζε θιάδνπο πνπ παξνπζηάδνπλ έληνλε επνρηθφηεηα, πξνηηκνχλ ηελ πξν-παξαγγειία πιηθψλ, ζπλήζσο απφ θνληηλνχο-ηνπηθνχο πξνκεζεπηέο (θαη πην ζπάληα απφ καθξηλνχο-offshore). ...
Thesis
In the modern, global environment, businesses must adopt innovative practices and strategic directions that will give them a long-term competitive advantage, in order to survive and develop. In this context, many traditional approaches and perceptions are increasingly being overcome, placing their place in new, more flexible and more harmonized with the ever-changing business environment. In recent years, in the context of a holistic approach, strategic supply chain management has been increasingly aligned with other key decision-making areas of the business, such as Marketing. In the context of the modern logistic-marketing management philosophy, customer satisfaction derives from the application of the marketing mix combined with the added value in space and time implied by the adoption of supply chain management practices (the better these two functions are harmonized , the better).
... In the standard postponed pricing problem (Van Mieghem and Dada 1999), the retailer first orders the product, and then sets the price after the demand is revealed. Yet, actual practice reflects a more complex process [as discussed by Fisher et al. (2000)]. First, some initial product quantity is ordered to test the market, and then the demand is revealed. ...
... Then, in a third stage, a final price is set to sell off the leftovers. Fisher et al. (2000) report real-life examples of this process in the apparel industry, suggesting that accurate forecasts of demand can indeed be made during the stage of early sales, that is, after a small percentage of the total demand has been observed, and that retailers can adjust their prices accordingly. ...
Article
Full-text available
Postponement strategies are becoming increasingly important in light of a global trend in which products’ life-cycles are decreasing, such that even products that are not traditionally considered seasonal become “obsolete” within a short period of time (e.g., electronic devices, new cars). Our work addresses postponed-pricing and ordering decisions for a retailer who sells a newsvendor-type inventoried product, in a selling season that is divided into two sub-periods. The division of the selling season enables the retailer to on-line adjust her decisions when faced with a scenario (one that is highly prevalent in reality) in which potential demand changes (increases or decreases) following consumers’ experiences of the product in early stages of the selling season. We assume that the retailer has two opportunities for receiving shipments: prior to the first sub-period and prior to the second one. The retailer determines each order quantity (base-stock level) on the basis of the demand distribution for the corresponding sub-period. In each sub-period, after observing additional market signals, the retailer determines the price of the product for that sub-period. With the aid of a stochastic programming approach, we develop optimization problems and solution methods in order to obtain pricing and ordering decisions that maximize the expected profit of the retailer. We present an extensive numerical example that compares the suggested strategy to three alternative strategies, and conclude that price postponement and responsiveness to demand changes can each reduce leftovers and lost sales as well as substantially increase expected profit. © 2018 Springer Science+Business Media, LLC, part of Springer Nature
... Especially nowadays, as a consequence of home shopping, and increasing competition, the percentage of impatient customers has significantly increased even more as the volatility and unpredictably of the demand increases (Poormoaied & Hosseini, 2021). Examples include the American apparel industry (Fisher et al., 2000), the grocery industry (Chiu et al., 2019;Son et al., 2019), the steel industry (Elsa & Bett, 2019), and the fast-moving consumer goods sector (Omoregbe & Ogbeide, 2013). Furthermore, in the manufacturing sector, stockouts can disrupt production schedules, lead to delays, and result in lost business opportunities. ...
... Faster supply chain speed usually results in faster lead times and lower inventory levels. Improving customer satisfaction, and increasing market competitiveness, companies often seek to improve their supply chain to achieve higher efficiency and flexibility in order to meet changing customer demands and stay ahead of competitors (Fisher et al., 2000). Supply chain agility refers to the ability of supply chain to respond quickly to changes in customer demand and market conditions, and supply chain agility facilitates rapid responses to customer demand, leading to improved inventory management and increased supply sales performance (Basu & Wright, 2008, 234). ...
Article
Full-text available
Importance: In the context of globalization and intensifying global competition, organizations must develop strategies to enhance supply chain agility in rapidly evolving markets. Dynamic capabilities theory (DCT) provides a framework for these strategies by emphasizing the importance of the abilities to sense, seize, reconfigure, learn, and integrate. Purpose This study investigates the impact of dynamic capabilities (sensing, seizing, reconfiguring, learning and integration) on the components of supply chain agility (flexibility, responsiveness, speed, and competency) specifically within the services sector. Methodology: Using a descriptive analytical approach and employing SPSS 29 for data analysis, this research involved 86 managers from senior and middle management in service organizations across Jordan, Saudi Arabia, and Qatar. Findings: The findings indicate that organizational managers place high importance on both dynamic capabilities and supply chain agility. Moreover, the study found a significant positive impact of dynamic capabilities on supply chain agility. Recommendation: The study recommends several strategies for organizations to enhance their supply chain agility: investing in advanced data analysis and monitoring systems, fostering a culture of innovation and continuous learning, strengthening cooperation between internal departments and external partners, simplifying processes and implementing automation technologies, providing targeted personnel training, and promoting a culture of continuous improvement. Future Research Directions: Future research should investigate the long-term impacts of dynamic capabilities on supply chain agility in other sectors and regions, and study the role of emerging technologies in enhancing these capabilities.
... In addition, they argue that while analytics empowers decision makers, data-driven decision-making typically empowers the data scientists instead.3 We found few retail analytics papers before 2000-the very yearFisher et al. (2000) first called for such research, making 2000 a logical cutoff. 4 This meant excluding data from lab experiments not validated by field testing, as well as (empirically motivated) synthetic data. ...
Article
We document the evolution of academic research through a bibliometric analysis of 123 retail analytics articles published in top operations management journals from 2000–2020. We isolate nine decision areas via manual coding that we verify using automated text analysis (topic modeling). We track variation across decision areas and method‐usage evolution per analytics type, featuring the degree to which big data (e.g., clickstream, social media, product reviews) and analytics suited for these new data sources (e.g., machine learning) are used. Our analysis reveals a rapidly growing field that is evolving in terms of content (decisions, retail sector), data, and methodology. To determine the state of practice, we interviewed global practitioners on the current use of retail analytics. These interviews shed light on the barriers and enablers of adopting advanced analytics in retail. They also highlight what sets companies on the frontier (e.g., Amazon, Alibaba, Walmart) apart from the rest. Combining the insights from our survey of academic research and interviews with practitioners, we provide directions for future academic research that take advantage of the availability of big data. This article is protected by copyright. All rights reserved
... Retail operation is about offering the right product in the right place at the right time for the right price [1]. On-Shelf-Availability (OSA) of products has been deemed a critical measure of a successful retail business operation due to its impact on the current and future demand [2,3]. ...
Preprint
On-Shelf Availability (OSA) of products in retail stores is a critical business criterion in the fast moving consumer goods and retails sector. When a product is out-of-stock (OOS) and a customer cannot find it on its designed shelf, this causes a negative impact on the customer's behaviors and future demands. Several methods are being adopted by retailers today to detect empty shelves and ensure high OSA of products; however, such methods are generally ineffective and infeasible since they are either manual, expensive or less accurate. Recently machine learning based solutions have been proposed, but they suffer from high computation cost and low accuracy problem due to lack of large annotated datasets of on-shelf products. Here, we present an elegant approach for designing an end-to-end machine learning (ML) pipeline for real-time empty shelf detection. Considering the strong dependency between the quality of ML models and the quality of data, we focus on the importance of proper data collection, cleaning and correct data annotation before delving into modeling. Since an empty-shelf detection solution should be computationally-efficient for real-time predictions, we explore different run-time optimizations to improve the model performance. Our dataset contains 1000 images, collected and annotated by following well-defined guidelines. Our low-latency model achieves a mean average F1-score of 68.5%, and can process up to 67 images/s on Intel Xeon Gold and up to 860 images/s on an A100 GPU. Our annotated dataset is publicly available along with our optimized models.
... It is not applicable for estimating the demand for a new product, it does not reflect the consumer substitution (Mantrala et al., 2009), consumers taste changes(M. L. Fisher, Raman, & McClelland, 2000), and also due to inaccurate data storage by the retailer (Fader & Hardie, 1996). Similarly, the other data sources such as expert opinion data, early sales data, revealed preference data have limitations like subjective error, improper store selection and do not reflect the preference for new products respectively. ...
Article
Full-text available
In fashion retailing, forecasting is determining the inventory needs of the product. An accurate forecast is very important in fashion retailing. Therefore, the forecasted demand is tested for error. An accurate demand forecast will satisfy the retailer and also the customer and an inaccurate demand forecast lead to situations like Over Stocking (OS) and Out of Stock (OOS).
... On the other hand, lack of coordination in supply chain causes mismatch in demand and supply, increased costs of stock out, excess inventory etc. (Horvath, 2001). Lack of coordination in a supply chain has caused US food industry wastage of $30 billion annually (Fisher et al., 1994). ...
Article
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The objective of this study is to identify extent of coordination among the supply stakeholders; measure supply chain performance and to study the impact of supply chain coordination on supply chain performance. The study is based on primary data collected from dairy supply chain stakeholders namely farmer-producers, bulk milk coolers (BMC), processing unit, wholesalers and retailers. The data was collected through combination of personal interviews, telephonic interviews and e-mail. The total sample size of this study is 420 spread across 28 firms with each firm representing a total of 15 respondents. Linear regression was performed to study the impact of supply chain coordination on supply chain performance. The results of this study reveal that supply chain coordination positively impacts all the supply chain performance metrics namely efficiency, responsiveness, flexibility and quality. Supply chain coordination has highest impact on supply chain responsiveness followed by supply chain quality, supply chain flexibility and supply chain efficiency. In case of impact of supply chain coordination on overall supply chain performance, there is an evidence of strong impact of supply chain coordination on supply chain performance.
... Any deficiency in the supply of raw materials or finished goods results in customer dissatisfaction and significant costs for manufacturers and retailers. The loss in sales due to forced markdowns is estimated to be more than 30% in the American apparel industry (Fisher, Raman, & McClelland, 2000). The resulting loss increases as the volatility and unpredictability of the demand increase. ...
Article
Emergency shipments are considered as an efficacious approach for mitigating the risk of shortages and increasing resilience in inventory systems. In this study, we develop a new newsvendor model where the retailer has a single opportunity to trigger an emergency shipment during the selling season. Due to demand uncertainty, it might not be possible to satisfy customer demands during the season. Provision for requesting an emergency shipment within the season can reduce the possibility of imminent shortages. As emergency shipments affect the dynamic of the system over time, the timing of emergency shipments in addition to their sizes is of particular interest. We use the time-weighted holding cost to compute the expected holding cost and assume that any unmet demand is lost. The goal is to determine the pre-season order quantity and both size and time of the emergency shipment such that the expected profit is maximized. We find that the newsvendor problem under time-weighted holding cost has a different structure and provide a different optimal order quantity. Our numerical experiences indicate that triggering an emergency shipment during the selling season can significantly improve cost savings. The results of a set of computational experiments demonstrate the superior performance of our proposed model when compared with the classical newsvendor problem. A sensitivity analysis is conducted showing that in the classical model, the unit lost sale cost is the most effective input parameter on the expected profit value. Moreover, the selling price and unit holding cost significantly affect the size and time of the emergency shipment.
... Established localized profit optimizations for individual suppliers can lead to miscalculations regarding demand and available supplies, transportation capabilities and delays, as well as general logistic problems, such as deliveries to unintended locations, inappropriate packaging, or incorrect labeling [7][8][9]. The costs resulting from these (potentially avoidable) miscalculations and mistakes quadruplicated over a period of 24 years for certain industries [10]. To counteract this development, the joining of business operations across company boundaries as well as a sharing of profits and risks within the complete supply chain has been proven to increase trust between business partners, to accelerate business performance, and, ultimately, to improve customer satisfaction and competitiveness [11,12]. ...
Article
The benefits of information sharing along supply chains are well known for improving productivity and reducing costs. However, with the shift toward more dynamic and flexible supply chains, privacy concerns severely challenge the required information retrieval. A lack of trust between the different involved stakeholders inhibits advanced, multi-hop information flows, as valuable information for tracking and tracing products and parts is either unavailable or only retained locally. Our extensive literature review of previous approaches shows that these needs for cross-company information retrieval are widely acknowledged, but related work currently only addresses them insufficiently. To overcome these concerns, we present PrivAccIChain, a secure, privacy-preserving architecture for improving the multi-hop information retrieval with stakeholder accountability along supply chains. To address use case-specific needs, we particularly introduce an adaptable configuration of transparency and data privacy within our design. Hence, we enable the benefits of information sharing as well as multi-hop tracking and tracing even in supply chains that include mutually distrusting stakeholders. We evaluate the performance of PrivAccIChain and demonstrate its real-world feasibility based on the information of a purchasable automobile, the e.GO Life. We further conduct an in-depth security analysis and propose tunable mitigations against common attacks. As such, we attest PrivAccIChain’s practicability for information management even in complex supply chains with flexible and dynamic business relationships.
... In the recent years, contracting in SC is identified as one of the crucial motivations of progression in performance of supply chain (Hou, Wei, Li, Huang, & Ashley, 2017). Fisher, Raman, and McClelland (1994) investigated a research on food industry of the US, which acknowledges need of contracting tools to prevent loss of 30 billion dollars every year between supply chain (SC) members. This study uses the RSC between members of green perishable foods supply chain to study whether RSC could make progress the perishable supply chain or coordinate the network's members. ...
Article
Full-text available
In the recent times, remanufacturing and recycling processes have been widespread because of hard environmental legislations. The significance of green factors such as uncertainty of return rates in supply networks has been extensively acknowledged in presented studies. A revenue sharing contract improves the performance of green supply chain and has a significant role in the profitability of total supply chain. There are few quantitative studies on revenue sharing contract in green supply chains. In this study, we propose coordination subject matters of a green supply chain with recycling perishable goods, involving suppliers, manufacturers, retailers, together with collection and disposal centers, in a multi-product, multi-period and multi-level basis under Fuzzy conditions. The consequences indicate that in dairy industries, appropriate collected returned products could be used as raw material for another product, which increases the supply chain profits and reduces waste; and also, since perishable goods have a limited shelf life, they can be reusable if they are collected before reaching to a critical time. Furthermore, the proposed revenue sharing contract can share benefits between supply network members and gain coordination of channel.
... Salmon (1989) argued that execution in retailing has become more important than other aspects of retail business (e.g., merchandising). Fisher et al. (2000) found that for short lifecycle products, such as fashion apparel, retailers are most successful if they can work with suppliers who can provide initial shipments of product based on forecasts, but then rapidly increase production to the right style, color, size, etc. based on actual sales. ...
Preprint
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Retailing is the set of activities that markets products or services to final consumers for their own personal or household use. Retailing does activities by organizing availability of products and services on a relatively large scale and supplying them to customers on a relatively small scale. Retail service is a series of activities designed to enhance the level of customer satisfaction i.e., the feeling that a product or service has met the customer expectation. Its importance varies by product, industry and customer; for instance, defective or broken merchandise can be exchanged. In order to find out the correlation between retail customer service and sales of the retail outlet, this study has been undertaken. The objective is to review different retail services offered by retailers in India. The study also attempts to find out customer satisfaction levels with respect to retail services and to find out impact of retail services on sales. Finally there are recommendations for better retail value added services to the customers.
... McGinnis and Vallopra (1999) found that involving suppliers could make new product development a success. For short lifecycle products retailers are most successful if they can work with suppliers who can provide initial shipments of product based on forecasts, but then rapidly increase production based on actual sales (Fisher et al, 2000). These researchers note that fast supply chains can produce products as they sell rather than worrying about accurate forecasts. ...
Article
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This study sought to examine the influence of buyer-supplier relationships on the supplier responsiveness. The population of this study was manufacturing firms in Kenya, forming a total of 90 respondents. The sample size resulted to 48 respondents. Questionnaires were issued out to the respondents in their respective departmental functions. Stratified random sampling technique was used. Primary data was collected using the questionnaires and analyzed using both descriptive analysis and correlation techniques. Findings from the study revealed that buyer and suppliers in the manufacturing sector are in collaborative relationships which have enhanced the ability to respond fast and in conformance to the products and service requirements of the buyer. The study recommended that all firms in other sectors should embrace this nature of relationships. Also all firms should try to optimize their information systems in order to enjoy the full benefits of sharing information and hence improved supplier's ability to respond to needs.
... Despite the challenges arising from their complex manufacturing supply chain systems, such as inventory management, cost reduction, continuously planning and scheduling orders while adapting each order to their customers' needs (Voudouris et al., 2008), Company A reaped the results of coordinating its supply chain in terms of increased sales, improved customer service, coping with high demand uncertainty, increased customer retention, and revenue enhancements (Fisher et al., 1994;Lee et al., 1997). This is the fundamental reason it was able to become a leading food manufacturing company in Jordan. ...
Article
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In the globalised economy supply chains are of paramount importance to international organisations, thus supply chain management has been intensively studied throughout the recent decades to understand the product cycle from the supplier to end customer and within organisations. However, there is a gap in the literature concerning the management of particular parameters such as quality within supply chains. This research investigates methods of measuring quality within existing supply chains using two companies from Jordan as case studies. Two different methodologies were applied in order to be able to conduct this research: direct observation and complete participation. It was noted that quality within the manufacturing supply chain was defined differently than in the service supply chain, thus requiring different actions to be used to measure it.
... Supply chain competence has been defined by Chow et al. (2008, p. 667) as -a portfolio of organizational, managerial, technical and strategic capabilities and skills developed by enterprises over time,‖ composed of quality and service issues, and operation and distribution issues. For Fisher et al. (2000), it is essential to develop the following capacities: foresight, inventory planning, speed of the supply chain, and precision of data. Bowersox et al. (2000) define this competence as a supply chain's ability to attend customer demands with low cost and high-quality products and services. ...
Article
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Purpose The purpose of this paper is to explain how a buying organization’s desorptive capacity relative to its supply network enhances the organization’s supply chain competence. The research also analyzes the contingent role of the balanced and combined dimensions of ambidexterity in this relationship. Design/methodology/approach Empirical results are obtained through analysis of survey data from a sample of 270 European firms. Hierarchical regression analysis is used to test the hypotheses. Findings The results confirm, first, the positive and significant relationship between the buying organization’s desorptive capacity and supply chain competence; and, second, the key moderating role of organizational ambidexterity, especially in its combined dimension, in this relationship. Practical implications The study suggests that desorptive capacity is key to the organization’s contribution to supply chain competitiveness. The authors also provide practitioners with better understanding of the extent to which they should attempt to balance exploration and exploitation or/and to maximize both simultaneously when seeking greater benefit from desorptive capacity. Originality/value This study extends desorptive capacity research to supply chain management. It responds to calls in the desorptive capacity literature for deeper understanding of the benefits of desorptive capacity and of the role organizational ambidexterity plays in the success of desorptive capacity. By analyzing the independent effects of the combined and balanced dimensions of ambidexterity, the authors advance conceptual and operational understanding of the role of ambidexterity needed in the literature.
... Business strategies include innovation, recognising and creating opportunities, developing new organisations, using resources in new opportunities and creating wealth. However, the main challenge of retail distribution activities lies in having the right product at the right location, at the right time and at the right price (Fisher, Raman, & McClelland, 2000). If this result is achieved, it becomes possible to achieve other final objectives such as optimising benefits, maximising the results of investments and satisfying various internal and external pressure groups (Wang, Yang, Song, & Sia, 2014). ...
Article
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This article’s objective is to study the influence of a new hybrid format in the retail distribution sector and this emerging market context is defined as competitive convergence. To attain this objective, 1150 surveys of retail distribution professionals were conducted. These surveys aid in distinguishing retail formats and indicate their competitive position allowing us to generate positioning maps of the Spanish retailing. In addition, Cramer’s coefficient V was used as an association measure between qualitative variables and latent class analysis (LCA) modelling was used to build a segmentation analysis of the competing offer. This analysis shows how retail formats evolve and adapt their competitive variables, even adopting characteristics from different formats (competitive convergence). Supermarkets dominate the Spanish retailing and a hybrid retail format’s better competitive position in aspects commonly associated with other formats supports. Spanish retailing provides an example of the non-static nature of retail formats and business models.
... Despite the challenges arising from their complex manufacturing supply chain systems, such as inventory management, cost reduction, continuously planning and scheduling orders while adapting each order to their customers' needs (Voudouris et al., 2008), Company A reaped the results of coordinating its supply chain in terms of increased sales, improved customer service, coping with high demand uncertainty, increased customer retention, and revenue enhancements (Fisher et al., 1994;Lee et al., 1997). This is the fundamental reason it was able to become a leading food manufacturing company in Jordan. ...
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Despite many years of business-orientated information and communication technology (ICT) deployment, contemporary organisations continue to struggle with customer-centric implementation of new technologies that are profitable and contribute to sustainable service business success. This paper reviews the difficulties inherent in using ICTs to manage customer-related information, and identifies the particular challenges for customer-centric deployment of ICTs. It provides a model of different levels of customer centric information use in organizations. The authors review implications for future research in this emerging area and conclude that the challenges of ICT deployment and use must be addressed with an uncompromising focus on customer value as the central principle of both ICT design and deployment, and of information management in service organizations.
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Retailers are now the dominant partners in most supply systems and have used their positions to re-engineer operations and partnerships with suppliers and other logistic service providers. No longer are retailers the passive recipients of manufacturer allocations, but instead are the active channel controllers organizing supply in anticipation of, and reaction to consumer demand. This paper reflects on the ongoing transformation of retail supply chains and logistics. If considers this transformation through an examination of the fashion, grocery and selected other retail supply chains, drawing on practical illustrations. Current and future challenges are then discussed.
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In a merchandise depth test, a retail chain introduces new products at a small sample of selected stores for a short period prior to the primary selling season and uses the observed sales to forecast demand for the entire chain. We describe a method for resolving two key questions in merchandise testing: (1) which stores to use for the test and (2) how to extrapolate from test sales to create a forecast of total season demand for each product for the chain. Our method uses sales history of products sold in a prior season, similar to those to be tested, to devise a testing program that would have been optimal if it had been applied to this historical sample. Optimality is defined as minimizing the cost of conducting the test, plus the cost of over- and understocking of the products whose supply is to be guided by the test. To determine the best set of test stores, we apply a k-median model to cluster the stores of the chain based on a store similarity measure defined by sales history, and then choose one test store from each cluster. A linear programming model is used to fit a formula that is then used to predict total sales from test sales. We applied our method at a large retailer that specializes in women's apparel and at two major shoe retailers, comparing results in each case to the existing process used by the apparel retailer and to some standard statistical approaches such as forward selection and backward elimination. We also tested a version of our method in which clustering was based on a combination of several store descriptors such as location, type of store, ethnicity of the neighborhood of location, total store sales, and average temperature of the store location. We found that relative to these other methods, our approach could significantly improve forecasts and reduce markdowns that result from excessive inventory, and lost margins resulting from stockouts. At the apparel retailer the improvement was enough to increase profits by more than 100%. We believe that one reason our method outperforms the forward selection and backward elimination methods is that these methods seek to minimize squared errors, while our method optimizes the true cost of forecast errors. In addition, our approach, which is based purely on sales, outperforms descriptor variables because it is not always clear which are the best store descriptors and how best to combine them. However, the sales-based process is completely objective and directly corresponds to the retailer's objective of minimizing the understock and overstock costs of forecast error. We examined the stores within each of the clusters formed by our method to identify common factors that might explain their similar sales patterns. The main factor was the similarity in climate within a cluster. This was followed by the ethnicity of the neighborhood where the store is located, and the type of store. We also found that, contrary to popular belief, store size and location had little impact on sales patterns. In addition, this technique could also be used to determine the inventory allocation to individual stores within a cluster and to minimize lost demand resulting from inaccurate distribution across size. Finally, our method provides a logical framework for implementing micromerchandising, a practice followed by a significant number of retailers in which a unique assortment of merchandise is offered in each store (or a group of similar stores) tuned to maximize the appeal to customers of that store. Each cluster formed by our algorithm could be treated as a "virtual chain" within the larger chain, which is managed separately and in a consistent manner in terms of product mix, timing of delivery, advertising message, and store layout.
A Technique to Estimate Retail Demand and Lost Sales
  • Ananth Raman
  • Giulio Zotteri
Ananth Raman and Giulio Zotteri, "A Technique to Estimate Retail Demand and Lost Sales," Harvard Business School Working Paper, 2000. "Sport Obermeyer Ltd.," HBS Case Study #9-695-022.