Abstracts:Mixed-model assembly lines are product-oriented production layouts often employed for large scale manufacturing of similar products. The unpaced variant of these lines employs a conveyor to discretely move pieces between stations either synchronously or asynchronously. Workload balancing and product sequencing are common optimization problems associated with these lines. While many works detail balancing and sequencing separately, few explicitly combine these degrees of freedom. Furthermore, hybrid (i.e. partly synchronous and partly asynchronous) lines are not explicitly described by previous optimization models. This paper presents a mixed-integer linear programming model capable of representing such unpaced lines and explicitly combine balancing, sequencing and cyclical scheduling. The application of the proposed method to a new dataset demonstrates the advantages of simultaneously balancing and sequencing lines, generating more efficient solutions than previously described models for 238 out of 240 instances. These results implied, however, in greater computational costs required to combine the degrees of freedom. Furthermore, a direct performance comparison study between synchronous, asynchronous, and hybrid lines is conducted. This allows line designers and managers to explicitly evaluate economical trade-offs between these line types.

Abstracts:Many works in the area of Supply Chain Network Design (SCND) have integrated production costs as one lumped cost. However, production costs embody indirect (overhead) and direct costs. This work brings a model that integrates these two production costs along with inventory and transportation costs. In particular, the work looks at the tradeoff between the shortening of the production duration (Pd) and the consequent increase in direct production costs. The work implicitly incorporates direct production costs that are the result of applying additional production resources to shorten Pd. Integrating both costs results in a nonlinear mathematical model. The overall objective function also includes binary variables to govern the selection of production plants resulting in a nonlinear mixed integer mathematical model. In this work, we prove the convexity of the objective function and we introduce a combination of a gradient and a local search heuristic to solve the resulting model. For practical implications, the paper presents four different scenarios to cover a wide range of supply chain settings. Principally, our results demonstrate promising benefits of reduced overall production costs, reduced production lead time, freed-up production capacity, and improved supply chain throughput.

Abstracts:The relationship between lean management and resilience in the supply chain, whether negative or positive, is still not clear from the existing literature. This paper aims to investigate the relationship and links between lean and resilient supply chain (SC) practices and their impact on SC performance. To achieve this objective, the aerospace manufacturing sector (AMS) is chosen as the study sector because of the importance of both paradigms. Interpretive Structural Modeling (ISM) approach is used in order to identify linkages among various lean and resilience practices and SC performance metrics through a single systemic framework. ISM is an interactive learning process based on graph theory where experts' knowledge is extracted and converted into a powerful well-structured model. For that purpose, a heterogeneous panel of experts in the AMS was formed, providing a complete view of all SC levels in the sector. The final ISM model revealed that lean SC practices act as drivers for resilient SC practices, since implementing the former in isolation could lead to a more vulnerable SC. The findings also show that lean SC practices lead to a higher performance improvement than resilient SC practices. This is due to the fact that resilient SC practices do not exert influence over all SC performance metrics as it occurs with lean SC practices. In addition, several managerial implications regarding the most convenient practices in terms of the company's objectives are drawn from this study.

Abstracts:Brazilian agribusiness is responsible for almost 25% of the country gross domestic product, and companies from this economic sector may have strategies to control their actions in a competitive market. In this way, models to properly predict variations in the price of products and services could be one of the keys to the success in agribusiness. Consistent models are being adopted by companies as part of a decision making process when important choices are based on short or long-term forecasting. This work aims to evaluate Wavelet Neural Networks (WNNs) performance combined with five optimization techniques in order to obtain the best time series forecasting by considering two case studies in the agribusiness sector. The first one adopts the soybean sack price and the second deals with the demand problem of a distinct groups of products from a food company, where nonlinear trends are the main characteristic on both time series. The optimization techniques adopted in this work are: Differential Evolution, Artificial Bee Colony, Glowworm Swarm Optimization, Gravitational Search Algorithm, and Imperialist Competitive Algorithm. Those were evaluated by considering short-term and long-term forecasting, and a prediction horizon of 30 days ahead was considered for the soybean sack price case, while 12 months ahead was selected for the products demand case. The performance of the optimization techniques in training the WNN were compared to the well-established Backpropagation algorithm and Extreme Learning Machine (ELM) assuming accuracy measures. In long-term forecasting, which is considered more difficult than the short-term case due to the error accumulation, the best combinations in terms of precision was reached by distinct methods according to each case, showing the importance of testing different training strategies. This work also showed that the prediction horizon significantly affected the performance of each optimization method in different ways, and the potential of assuming optimization in WNN learning process.

Abstracts:Prior research on inventory management for imperfect items assumes that such items can be dealt with through salvage or rework. Increased repair costs and decreased production costs arising from modern production processes (e.g. miniaturization, 3D printing), however, have led suppliers to increasingly eschew such solutions in favor of items and components which are discarded upon failure rather than being reworked or scrapped. In this paper, we first determine optimal supplier and buyer inventory policies for items which fail and which cannot be reworked. We then develop a supply chain coordination mechanism which uses a common replenishment time to coordinate a supply chain consisting of a single supplier and n buyers. Our coordination mechanism yields a global minimum for system-wide costs. Numerical examples are provided to illustrate important conditions under which our model is particularly effective at reducing system-wide costs.

Abstracts:Service provider opportunism is a serious concern in third party logistics (3PL) relationships. However, our knowledge about the antecedents of 3PL providers' opportunism is limited. According to transaction cost economics (TCE), increased transaction costs cause opportunism. This study incorporates key TCE constructs (environmental uncertainty, specific assets, and opportunism) and conducts a transaction cost analysis. We argue that environmental uncertainty and specific assets create exchange hazards that result in opportunism. Meanwhile, specific assets reduce coordination costs raised by environmental uncertainty. Building on these arguments, this study tests a model that hypothesizes that environmental uncertainty (demand, supply, and technology uncertainty), and specific assets (user- and provider-specific assets) are positively related to opportunism, and that environmental uncertainty is positively related to specific assets. Structural equation modeling is used to examine data from 247 3PL relationships in China. The results show that demand uncertainty decreases opportunism, supply uncertainty increases opportunism, and technology uncertainty does not have a significant effect. User-specific assets increase opportunism, while provider-specific assets decrease opportunism. Demand and supply uncertainty have positive effects on user-specific assets, but non-significant effects on provider-specific assets, while technology uncertainty does not have a significant impact on user or provider-specific assets. In general, our findings are supported by the rationale of TCE, and industrial or cultural factors can explain several surprising findings. This study contributes to 3PL literature and practice.

Abstracts:Consumers may return a product for a variety of reasons, such as the product having the wrong color or size, having poor functionality, being damaged during shipment, or simply prompting regret for an impulsive purchase. Retailers generally provide lenient return policies not only because they may signal high quality but also because they act as risk relievers for consumers’ purchasing decision processes. However, increasing product returns have become particularly challenging for the efficient management of inventory. As such, at the crux of a holistic inventory model lies the understanding of consumer return behavior. In this study, we introduce a variant of the classical single-period inventory (newsvendor) model with returns, in which heterogeneous consumers decide, based on their post-purchase valuation of the product, whether to return the product after using it. From the perspective of the retailer, such deliberate returns may abuse the return policy, which in turn may exacerbate reverse logistics and environmental costs. To that end, we incorporate demand uncertainty and consumer valuation uncertainty by explicitly gauging return probabilities and differentiated salvage values into a newsvendor model. We derive analytical results for the profit-maximizing order quantity for a single-period product that comes with a retailer return policy and exclusively identify the impact of return type as abused or normal. Also offered are closed-form optimal solutions in the cases where market demand is exponentially or uniformly distributed. Structural and numerical results lend managerial insight into how optimal ordering amount, profit, return rates and salvage values change with the price, return window, and hassle cost of returning the product.

Abstracts:Formulation of right strategies is believed to be able to bring sustainable performance across triple bottom line (TBL), i.e., economic, environmental and social aspects within and across organizations. The purpose of this research is to investigate the role of misaligned collaboration and dynamic capabilities on TBL performance. Misaligned collaboration signifies those configurations of collaboration that deviate from ideal profile of collaboration. The ideal profile of collaboration corresponds to superior performance. Collaboration has been operationalized through joint planning and resource sharing (JPRS) and collaborative culture (CC) which brings relational aspects into collaboration. Specifically, this research provides important extensions to the theory of profile deviation and dynamic capabilities (DC) perspective in the context of sustainable supply chain performance and misaligned collaboration utilizing the empirical evidence. Uniqueness of the proposed model is established by comparing with four other alternate models. We find both JPRSmisalign (misalignment of JPRS from the ideal profile) and CCmisalign (misalignment of CC from the ideal profile) influence all dimensions of TBL through DCs. Only direct influence of CCmisalign on operational and social performance is significant. Results convey the need of building DCs when collaboration is misaligned with its ideal profile, and this misalignment produces detrimental effects on DCs and TBL performance. This research contributes significantly by building unique model to develop and maintain sustainability. Further, theoretical and managerial contributions are highlighted and contested with existing knowledge.

Abstracts:We examine a retailer's Stackelberg supply chain, in which the retailer sells a product in the two brands: its own store brand (SB) and a national brand (NB) supplied by a well-established manufacturer. The two brands both face customer returns, and they differ in product quality. We examine the retailer's decision on returns policies for the two brands (either Money Back Guarantee (MBG) or No Refund) and the effects of returns policies on the competition between the two brands. We identify the condition when the retailer should offer MBGs for both brands and we show that MBGs mitigate price competition between the two brands. MBGs are found to enhance the retailer's profit and reduce the NB manufacturer's profit. We examine coordination mechanisms and find that a centralized supply chain intensifies the competition and pushes the NB to reduce its retail price. A simple coordination contract that can achieve supply chain coordination to ensure a win-win for both the retailer and the NB manufacturer is proposed.

Abstracts:While competitive environment analysis is critical to a global retailing operations strategy, there exist research gaps from perspectives of operational performance, retailing industrial environment, and nondiscretionary factors. Therefore, our research objective is to propose a new approach to conduct competitive environment analysis for a global operations strategy in retailing, by examining relationships between discretionary inputs of the supply chain, nondiscretionary inputs of the environment, and performance of retailing. We develop a nondiscretionary data envelopment analysis model to assess performance in retailing and integrate it with econometric analysis. Using multisource data of 124 organizations in the global retailing industry, it is interesting to find: while nondiscretionary factors significantly influence the operational performance of global retailers, firms in an environment with a higher market concentration, larger consumer spending per capita, and smaller inhabitants’ population are more likely to achieve a higher operational efficiency in retailing. Another interesting finding with practical implication is: inputs relevant to outside environment (e.g., suppliers in upstream and outlets in downstream supply chain) can influence operational efficiency more than inputs in internal supply chain (e.g., warehouses).