Minimizing food waste in grocery store operations: literature review and research agenda

Research on grocery waste in food retailing has recently attracted particular interest. Investigations in this area are relevant to address the problems of wasted resources and ethical concerns, as well as economic aspects from the retailer’s perspective. Reasons for food waste in retail are already well-studied empirically, and based on this, proposals for reduction are discussed. However, comprehensive approaches for preventing food waste in store operations using analytics and modeling methods are scarce. No work has yet systematized related research in this domain. As a result, there is neither any up-to-date literature review nor any agenda for future research. We contribute with the first structured literature review of analytics and modeling methods dealing with food waste prevention in retail store operations. This work identifies cross-cutting store-related planning areas to mitigate food waste, namely (1) assortment and shelf space planning, (2) replenishment policies, and (3) dynamic pricing policies. We introduce a common classification scheme of literature with regard to the depth of food waste integration and the characteristics of these planning problems. This builds our foundation to review analytics and modeling approaches. Current literature considers food waste mainly as a side effect in costing and often ignores product age dependent demand by customers. Furthermore, approaches are not integrated across planning areas. Future lines of research point to the most promising open questions in this field

Decision models for fast-fashion supply and stocking problems in internet fulfillment warehouses

Internet technology is being widely used to transform all aspects of the modern supply chain. Specifically, accelerated product flows and wide spread information sharing across the supply chain have generated new sets of decision problems. This research addresses two such problems. The first focuses on fast fashion supply chains in which inventory and price are managed in real time to maximize retail cycle revenue. The second is concerned with explosive storage policies in Internet Fulfillment Warehouses (IFW). Fashion products are characterized by short product life cycles and market success uncertainty. An unsuccessful product will often require multiple price discounts to clear the inventory. The first topic proposes a switching solution for fast-fashion retailers who have preordered an initial or block inventory, and plan to use channel switching as opposed to multiple discounting steps. The FFS Multi-Channel Switching (MCS) problem then is to monitor real-time demand and store inventory, such that at the optimal period the remaining store inventory is sold at clearance, and the warehouse inventory is switched to the outlet channel. The objective is to maximize the total revenue. With a linear projection of the moving average demand trend, an estimation of the remaining cycle revenue at any time in the cycle is shown to be a concave function of the switching time. Using a set of conditions the objective is further simplified into cases. The Linear Moving Average Trend (LMAT) heuristic then prescribes whether a channel switch should be made in the next period. The LMAT is compared with the optimal policy and the No-Switch and Beta-Switch rules. The LMAT performs very well and the majority of test problems provide a solution within 0.4% of the optimal. This confirms that LMAT can readily and effectively be applied to real time decision making in a FFS. An IFW is a facility built and operated exclusively for online retail, and a key differentiator is the explosive storage policy. Breaking the single stocking location tradition, in an IFW small batches of the same stock keeping unit (SKU) are dispersed across the warehouse. Order fulfillment time performance is then closely related to the storage location decision, that is, for every incoming bulk, what is the specific storage location for each batch. Faster fulfillment is possible when SKUs are clustered such that narrow band picklists can be efficiently generated. Stock location decisions are therefore a function of the demand arrival behavior and correlations with other SKUs. Faster fulfillment is possible when SKUs are clustered such that narrow band picklists can be efficiently generated. Stock location decisions are therefore a function of the demand behavior and correlations with other SKUs. A Joint Item Correlation and Density Oriented (JICDO) Stocking Algorithm is developed and tested. JICDO is formulated to increase the probability that M pick able order items are stocked in a δ band of storage locations. It scans the current inventory dispersion to identify location bands with low SKU density and combines the storage affinity with correlated items. In small problem testing against a MIP formulation and large scale testing in a simulator the JICDO performance is confirmed

Data Science in Supply Chain Management: Data-Related Influences on Demand Planning

Data-driven decisions have become an important aspect of supply chain management. Demand planners are tasked with analyzing volumes of data that are being collected at a torrential pace from myriad sources in order to translate them into actionable business intelligence. In particular, demand volatilities and planning are vital for effective and efficient decisions. Yet, the accuracy of these metrics is dependent on the proper specification and parameterization of models and measurements. Thus, demand planners need to step away from a black box approach to supply chain data science. Utilizing paired weekly point-of-sale (POS) and order data collected at retail distribution centers, this dissertation attempts to resolve three conflicts in supply chain data science. First, a hierarchical linear model is used to empirically investigate the conflicting observation of the magnitude and prevalence of demand distortion in supply chains. Results corroborate with the theoretical literature and find that data aggregation obscure the true underlying magnitude of demand distortion while seasonality dampens it. Second, a quasi-experiment in forecasting is performed to analyze the effect of temporal aggregation on forecast accuracy using two different sources of demand signals. Results suggest that while temporal aggregation can be used to mitigate demand distortion\u27s harmful effect on forecast accuracy in lieu of shared downstream demand signal, its overall effect is governed by the autocorrelation factor of the forecast input. Lastly, a demand forecast competition is used to investigate the complex interaction among demand distortion, signal and characteristics on seasonal forecasting model selection as well as accuracy. The third essay finds that demand distortion and demand characteristics are important drivers for both signal and model selection. In particular, contrary to conventional wisdom, the multiplicative seasonal model is often outperformed by the additive model. Altogether, this dissertation advances both theory and practice in data science in supply chain management by peeking into the black box to identify several levers that managers may control to improve demand planning. Having greater awareness over model and parameter specifications offers greater control over their influence on statistical outcomes and data-driven decision

Joint Innovation Investment and Pricing Decisions In Retail Supply Chains With Customer Value

In the retail industry, customer value has become the key to maintaining competitive advantages. In the era of new retail, customer value is not only affected by the product price, but it is also closely related to innovations, such as value‐added services and unique business models. In this paper, we study the joint innovation investment and pricing decisions in a retailer–supplier supply chain based on revenue sharing contracts and customer value. We first find that, in the non-cooperative game, equilibrium only exists in the supplier Stackelberg game. However, revenue sharing contracts cannot coordinate the supply chain in the non‐cooperative game. By considering supply chain members’ bargaining power, we find that there exists a unique equilibrium for the Nash bargaining product. In addition, revenue sharing contracts can coordinate the supply chain and achieve the optimal consumer surplus. When the supply chain is coordinated, supply chain profit is allocated to the supply chain members based on their bargaining powers

The Retail Second-hand Clothing Sector in Developing Economy: Case study of Liberia

The proliferation of environmental awareness and the growing recognition of the significance of sustainability has resulted in a new trend of retailers embracing second-hand reselling from unsold or unwanted inventories. Their numbers are increasing while also leveraging various business models, strategies, and procedures. This study uses a survey data of 154 responses from retailers of second-hand clothing across four cities in Liberia to evaluate the used clothing sector in the country. Findings from the survey highlight that while the SHC value chain encourages sustainable consumption, it is also a promoter of economic growth, particularly for developing countries emerging from war. Its retail growth potential is closely tied to the level of economic development geared toward supporting subsistence activities. Findings also show that respondents strongly oppose banning the importation of used clothes and the notion that second-hand undergarments pose health challenges. These findings are essential for providing business owners with understanding of the retailing process and how they can strengthen their foothold in the used clothing sector. They also emphasize the used clothing industry’s significance in Liberia and create avenues for future research

Decentralized and centralized supply chains with trade credit option

The notion of a trade credit period is a common business practice, where a supplier allows a buyer a specified period to make a payment in full for a purchase made. The objective of this thesis is to explore the role of such a credit payment option in supply chain management. Towards this end, a two-echelon supply chain, consisting of a single supplier (e.g. manufacturer) and the cases of both a single and multiple buyers (e.g. retailers) is examined under decentralized (independent) and centralized (coordinated) decision making scenarios. The major emphasis of this research is limited to the case of a single product with price-sensitive deterministic, as well as stochastic market demand.The conditions under which a trade credit period should be offered and its appropriate length are determined from the supplier’s perspective under the decentralized case. Under the centralized decision scenario, the efficacy of a trade credit policy as a supply chain coordination mechanism is thoroughly analyzed and guidelines for pricing, production and delivery decisions are developed. The concepts developed in this study are illustrated via a number of numerical examples, in conjunction with thorough sensitivity analyses involving some selected problem parameters.The major contribution of this thesis is that we incorporate the pricing and inventory issues in supply chains with an endogenous credit payment period. This is the first study that examines the efficacy of trade credit option as a coordination mechanism. We propose a coordination mechanism that coordinates the supply chain, when a trade credit by itself is not sufficient to serve such a purpose, while preserving the benefits of a trade credit option. Also, this study is the first to examine the issues concerning trade credit under price sensitive stochastic demand. Another first for this work is the exploration of the implications of a trade credit policy in supply chains consisting of multiple competing retailers. The effects of the extent of competition and the market size on trade credit policy are evaluated. Our analyses lead to some important practical implications, to serve as managerial guidelines.Ph.D., Decision Sciences -- Drexel University, 201

Zara and Benetton: Comparison of two business models

The project analizes and compares two very important and diferent business models in fast fashion industry: Zara y Benetton models. Their models are so diferent but have been a great success, due to their capacity to respond quickly to demand of the market, then due to their flexibility. In this regard, the project also demonstrates how information sharing have a big role to the success of a company. It improves the efficiency of a company and helps to achieve the customer satisfaction . To achieve a good sharing information, it' s important a good and strenght relationship between manufacturer and retailer

Supply Chain

Traditionally supply chain management has meant factories, assembly lines, warehouses, transportation vehicles, and time sheets. Modern supply chain management is a highly complex, multidimensional problem set with virtually endless number of variables for optimization. An Internet enabled supply chain may have just-in-time delivery, precise inventory visibility, and up-to-the-minute distribution-tracking capabilities. Technology advances have enabled supply chains to become strategic weapons that can help avoid disasters, lower costs, and make money. From internal enterprise processes to external business transactions with suppliers, transporters, channels and end-users marks the wide range of challenges researchers have to handle. The aim of this book is at revealing and illustrating this diversity in terms of scientific and theoretical fundamentals, prevailing concepts as well as current practical applications

Pricing and inventory strategies in dual-channel distribution system

Master'sMASTER OF ENGINEERIN