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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 context of services, inventory refers to all work done prior to sale, including partially process information. There are five basic reasons for keeping an inventory: Time: The time lags present in the supply chain, from supplier to user at every stage, requires that you maintain certain amounts of inventory to use in this lead time. However, in practice, inventory is to be maintained for consumption during 'variations in lead time'. Lead time itself can be addressed by ordering that many days in advance. Seasonal demand: Demands varies periodically, but producers capacity is fixed. This can lead to stock accumulation, consider for example how goods consumed only in holidays can lead to accumulation of large stocks on the anticipation of future consumption. Uncertainty: Inventories are maintained as buffers to meet uncertainties in demand, supply and movements of goods. Economies of scale: Ideal condition of "one unit at a time at a place where a user needs it, when he needs it" principle tends to incur lots of costs in terms of logistics.

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Utilizing additive manufacturing and mass customization under capacity constraints

Ralf Seifert, Anna Timonina-Farkas, Rachel Agnès Laetitia Rosemonde Marie Lacroix

Additive manufacturing (AM), originally used for prototyping, is increasingly adopted for custom final part production across different industries. However, printing speed and production volume are two barriers for the adoption of AM for product customization at large scale. Nevertheless, manufacturers could aim to combine the benefits of AM for product customization with traditional mass customization (MC) technologies over the product life cycle (PLC). This approach is showcased in our paper as a manufacturing opportunity and is addressed via a non convex-concave optimization model that considers a monopolist manufacturer producing horizontally differentiated products at scale. To satisfy individual customer preferences under capacity considerations, the firm jointly decides on the inventory, production quantity, product variety, optimal technology-switching times (between AM and MC) and pricing strategy. Our approach can be implemented by decision-makers to leverage customer-centricity and benefit from this novel hybrid manufacturing practice. By deriving a closed-form solution for the production quantity based on an adaptive inventory policy, the resulting optimization problem is solved using the Sample Average Approximation framework grounded by analytical results. Our results demonstrate that the new usage of AM with MC can benefit a manufacturer for customer-centric driven strategies. Significant profit improvements can be achieved with an AM-MC-AM technology-switching scenario under certain capacity conditions and with an increasing-decreasing pricing strategy. Our results also indicate that the benefits of pricing flexibility are highest when capacity is unlimited or when the firm does not hold inventory. Under capacity constraints, a simple decreasing pricing policy combined with inventory performs very well.

SPRINGER2022

Why Do Companies Need Operational Flexibility to Achieve Sustainability at Source?

Ralf Seifert, Isik Bicer, Yara Kayyali El Alem

We analyze the environmental benefits of operational flexibility that emerge in the form of less product waste during the sourcing process by reducing overproduction. We consider three different options for operational flexibility: (1) lead-time reduction, (2) quantity-flexibility contracts, and (3) multiple sourcing. We use a multiplicative demand process to model the evolutionary dynamics of demand uncertainty. We then quantify the impact of key modeling parameters for each operational-flexibility strategy on the waste ratio, which is measured as the ratio of excess inventory when a certain operational-flexibility strategy is employed to the amount when an offshore supplier is utilized without any operational flexibility. We find that the lead-time reduction strategy has the maximum capability to reduce waste in the sourcing process of buyers, followed by the quantity-flexibility and multiple-sourcing strategies, respectively. Thus, our results indicate that operational-flexibility strategies that rely on the localization of production are key to reducing waste and improving environmental sustainability at source.

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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.

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Historical cost

The historical cost of an asset at the time it is acquired or created is the value of the costs incurred in acquiring or creating the asset, comprising the consideration paid to acquire or create the asset plus transaction costs. Historical cost accounting involves reporting assets and liabilities at their historical costs, which are not updated for changes in the items' values. Consequently, the amounts reported for these balance sheet items often differ from their current economic or market values.

Safety stock

Safety stock is a term used by logisticians to describe a level of extra stock that is maintained to mitigate risk of stockouts (shortfall in raw material or packaging) caused by uncertainties in supply and demand. Adequate safety stock levels permit business operations to proceed according to their plans. Safety stock is held when uncertainty exists in demand, supply, or manufacturing yield, and serves as an insurance against stockouts. Safety stock is an additional quantity of an item held in the inventory to reduce the risk that the item will be out of stock.

Inventory

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Covers the configuration of service offerings in the Cristal platform.

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Covers the process of invoice processing for Metallica Sàrl, including tasks such as invoice approval and inventory management.