Inventory | EPFL Graph Search

Inventory (American English) or stock (British English) refers to the goods and materials that a business holds for the ultimate goal of resale, production or utilisation. Inventory management is a discipline primarily about specifying the shape and placement of stocked goods. It is required at different locations within a facility or within many locations of a supply network to precede the regular and planned course of production and stock of materials. The concept of inventory, stock or work in process (or work in progress) has been extended from manufacturing systems to service businesses and projects, by generalizing the definition to be "all work within the process of production—all work that is or has occurred prior to the completion of production". In the context of a manufacturing production system, inventory refers to all work that has occurred—raw materials, partially finished products, finished products prior to sale and departure from the manufacturing system. In the co

Interrelating operational and financial performance measurements in inventory theory

Margarita Protopappa

Financial flows are often treated in a fragmented and disconnected way from the physical product flow. Managers take decisions from an operational point of view concerning inventory, service level or capacity needs. The implementation of such decisions influences financial performance in terms of profit margin, working capital requirements and return on investment. However, the interdependency of operational and financial objectives is rarely well understood. Such practice has serious implications on the profitability of a company and its responsiveness to market needs. Therefore, companies increasingly acknowledge the importance of financial supply chain management as an effective approach to optimize working capital levels and to direct cash flows. Efficient working capital allocation and visibility of accounts payables and receivables can achieve significant cost savings, enhance cash flow predictability and boost company performance. In this dissertation, we weave together financial and operational decisions. We analyze the impact of working capital targets on the optimal ordering policy and the relationship between product flows and cash flows. We also investigate the impact of trade-credit on the operational and financial performance of a supply chain. In the first part of our dissertation, we develop and analyze a model that interrelates operational and financial aspects. We analyze how the optimal solution depends on the financial characteristics of the supply chain. We elaborate on the interrelation between operational and financial performance measurements, such as inventory level, service level, return on investment, cash flows and working capital requirements. Our results demonstrate the significance of payment delays on profit margin. We motivate a joint consideration of financial and operational objectives because a higher working capital employed in the system decreases the operational cost but increases the financial cost. In the second part of this dissertation, we extend our modeling framework to accommodate multi-product settings. We focus on the interrelation of working capital and stocking decisions for functional, innovative and heterogeneous product portfolios. We analyze the effect of demand correlation, lead time, payment delays, portfolio size and service level constraints for symmetric and asymmetric product portfolios of various sizes. Our results attest the importance of upstream and downstream payment delays on the profitability of the supply chain. Our analysis demonstrates significant risk-pooling benefits for functional and innovative portfolios under specific demand correlation scenarios. In the third part of this dissertation, we extend our modeling framework and investigate the impact of working capital allocation in a multi-echelon supply chain. We consider a joint working capital allocation, where all the members of the supply chain share a working capital pool and face working capital restrictions jointly. We compare our findings with a dedicated working capital allocation, where the members of the supply chain each have a separate working capital and face individual working capital restrictions. Our research demonstrates significant cost savings when the members of the supply chain share a joint pool of working capital. We investigate the impact of interest rate, payment delays and lead time on the profitability of the supply chain. We show that extending the payment delays to your supplier increases the cost in the supply chain. We finalize this dissertation with a conclusion and a discussion of potential avenues for future research.

EPFL2009

Industry 4.0 Technologies and Customer-centricity for Digital Supply Chains

Rachel Agnès Laetitia Rosemonde Marie Lacroix

The last few years have experienced the emergence of Industry 4.0 (I4.0), ultra-customization, and the explosion of demand for ethical, fair trade, and sustainable consumption. Organizations have therefore started a digital transformation of their SCs and of their production. They have placed consumers at the center of their strategic agenda. In the context of ultra-customization, mass customization (MC) is gaining momentum, especially with the arrival of digital technologies like additive manufacturing (AM), also known as 3D-printing). Due to technological advances, AM is now very popular for the final part production in series, and on a large scale. SC performance is, among others, driven by operational excellence, information sharing, and trust between the different SC stakeholders. Looking at the second trend, which is focused on new consumption patterns, organizations are now encouraged to evaluate the potential of adopting new digital I4.0 technologies. The combination of Blockchain (BC) with the Internet-of-Things (IoT) seems promising to improve SC performance and meet customer demand. Despite the recognized potential of I4.0 technologies, and the transition toward digital SCs, organizations are struggling to adapt to the two trends. This is mainly due to the lack of decision support tools for the adoption of these new technologies and to the lack of user-centric approaches. In this thesis, we develop user-centric approaches from which we model and analyze the impact of three I4.0 digital technologies on the SC. First, we develop a new demand model, the HLB model, taking into account the individual demand of heterogeneous customers. This model is the first, to our knowledge, to model both the heterogeneity of customers and the evolution of their purchasing behavior over time. It couples the Bass diffusion and the Hotelling-Lancaster models. Then, building on the HLB model, we analyze, across the PLC, marketing and operations decisions that result from technology-switching scenarios. We formulate and solve an optimization problem by jointly deciding on: technology-switching times, inventory, production quantity, pricing, and product variety strategies. The goal is to maximize a manufacturer's profit while addressing the individual and evolving needs of customers. We use a sample average approximation for the numerical solutions of our non-convex optimization problem. Based on an adaptive inventory policy, we derive a closed-form solution for the production quantity decision. Our results demonstrate that the new usage of AM with MC, and a user-centric approach, can benefit a manufacturer. Significant profit improvements can be achieved with a hybrid AM-MC-AM technology-switching production scenario, under certain production capacity conditions. Second, we adopt a three-step approach to discover the BC IoT success conditions for lean and agile SCs: (i) a multivocal literature review (MLR), (ii) a topic modeling to categorize the success factors (SFs), and (iii) associate the categories of SFs to SC macro-processes for lean and agile SCs, respectively. Our findings are summarized into a conceptual framework and research propositions. This last study offers valuable insights into when and how the sweet spots for both SC types would materialize in practice, as well as their impacts with respect to the SC macro-processes performance.

EPFL2021

Supply Chain Disruption Risk Management

Florian Christopher Matthias Lücker

It is well established that firms are exposed to the risk of supply chain disruptions. Many firms build risk mitigation strategies in order to increase the resilience of their supply chains. In this dissertation we present three essays that study operational risk mitigation levers such as inventory, reserve capacity and dual sourcing. The first essay outlines an application in the pharmaceutical industry where the risk mitigation levers inventory, dual sourcing and agility capacity are analyzed. We study the relationship between these three levers by modeling a drug manufacturing firm that is exposed to disruptions in its supply chain. The firm determines optimal inventory levels for assumed dual sourcing and agility capacity. We quantify the decrease in inventory levels in the presence of dual sourcing and agility capacity. Furthermore, using an example, we analyze inventory, dual sourcing and agility capacity decisions jointly. It turns out that inventory and agility capacity can be substitutes as long as no dual source is available. Once the dual source is available, agility capacity and dual sourcing appear to be substitutes. We further show that for long disruption times, the optimal dual source production rate may decrease in the disruption time. Within our modeling framework, we introduce an operational metric as a measure for resilience under deterministic demand. In the second essay we study the joint role of inventory and reserve capacity in mitigating supply disruptions. A reserve capacity can be used for production in a reactive fashion when a disruption occurs. We first determine optimal inventory levels and reserve capacity production rates jointly under stochastic customer demand. This allows us to fully characterize three main risk mitigation strategies: inventory strategy, reserve capacity strategy, and mixed strategy. Furthermore, we provide structural insights of optimal risk mitigation strategies. We then combine our analytical results with the well-established (Q,R) policy to perform numerical experiments using Conditional Value at Risk (CVaR) as a resilience measure. Our results suggest that the resilience measure depends on both the reorder point R and the batch size Q. Comparing the resilience measure with the service level, we find that a high service level and high resilience can be conflicting objectives. The third essay is concerned with the role of inventory in mitigating disruptions in two-echelon supply chains under stochastic demand. The research problem is to determine the optimal backup inventories for a serial, assembly and distribution supply chain respectively. For the three supply chain types, disruptions at each production site are described by an infinite-state discrete-time Markov process. We derive structural insights on the optimal inventory levels for the serial supply chain. For the assembly and distribution supply chain we show that under mild assumptions an early commitment to finished goods inventories is optimal. This finding is different from optimal safety inventory policies where often a delayed differentiation in assembly supply chains or risk pooling in distribution supply chains is optimal.

EPFL2016