Inventory Analytics provides a comprehensive and accessible introduction to the theory and practice of inventory control – a significant research area central to supply chain planning. The book outlines the foundations of inventory systems and surveys prescriptive analytics models for deterministic inventory control. It further discusses predictive analytics techniques for demand forecasting in inventory control and also examines prescriptive analytics models for stochastic inventory control. Inventory Analytics is the first book of its kind to adopt a practicable, Python-driven approach to illustrating theories and concepts via computational examples, with each model covered in the book accompanied by its Python code. Originating as a collection of self-contained lectures, Inventory Analytics will be an indispensable resource for practitioners, researchers, teachers, and students alike.

Life Cycle Inventory (LCI) Analysis is the second phase in the Life Cycle Assessment (LCA) framework. Since the first attempts to formalize life cycle assessment in the early 1970, life cycle inventory analysis has been a central part. Chapter 1 “Introduction to Life Cycle Inventory Analysis“ discusses the history of inventory analysis from the 1970s through SETAC and the ISO standard. In Chapter 2 “Principles of Life Cycle Inventory Modeling”, the general principles of setting up an LCI model and LCI analysis are described by introducing the core LCI model and extensions that allow addressing reality better. Chapter 3 “Development of Unit Process Datasets” shows that developing unit processes of high quality and transparency is not a trivial task, but is crucial for high-quality LCA studies. Chapter 4 “Multi-functionality in Life Cycle Inventory Analysis: Approaches and Solutions” describes how multi-functional processes can be identified. In Chapter 5 “Data Quality in Life Cycle Inventories”, the quality of data gathered and used in LCI analysis is discussed. State-of-the-art indicators to assess data quality in LCA are described and the fitness for purpose concept is introduced. Chapter 6 “Life Cycle Inventory Data and Databases“ follows up on the topic of LCI data and provides a state-of-the-art description of LCI databases. It describes differences between foreground and background data, recommendations for starting a database, data exchange and quality assurance concepts for databases, as well as the scientific basis of LCI databases. Chapter 7 “Algorithms of Life Cycle Inventory Analysis“ provides the mathematical models underpinning the LCI. Since Heijungs and Suh (2002), this is the first time that this aspect of LCA has been fundamentally presented. In Chapter 8 “Inventory Indicators in Life Cycle Assessment”, the use of LCI data to create aggregated environmental and resource indicators is described. Such indicators include the cumulative energy demand and various water use indicators. Chapter 9 “The Link Between Life Cycle Inventory Analysis and Life Cycle Impact Assessment” uses four examples to discuss the link between LCI analysis and LCIA. A clear and relevant link between these phases is crucial.

The Forest Inventory and Analysis (FIA) Program of the U.S. Department of Agriculture, Forest Service is in the process of moving from a system of quasi-independent, regional, periodic inventories to an enhanced program featuring greater national consistency, a complete and annual sample of each State, new reporting requirements, and integration with the ground sampling component of the Forest Health Monitoring Program. This documentation presents an overview of the conceptual design, describes the sampling frame and plot configuration, presents the estimators that form the basis of FIA's National Information Management System (NIMS), and shows how annual data are combined for analysis. It also references a number of Web-based supplementary documents that provide greater detail about some of the more obscure aspects of the sampling and estimation system, as well as examples of calculations for most of the common estimators produced by FIA.

Lichens are one of several forest health indicators sampled every year for a subset of plots on the permanent grid established by the Forest Inventory and Analysis (FIA) Program of the U.S. Department of Agriculture Forest Service. This report reviews analysis procedures for standard FIA lichen indicator data. Analyses of lichen data contribute to state, regional, and national reports that evaluate spatial pattern and temporal trends in forest biodiversity, air quality, and climate. Data collection and management follow standard national protocols. A lichen species richness index (the number of species per FIA plot) is available for all areas soon after data collection. Air quality and climate indexes (for defined regional gradients and based on lichen species composition at plots) are developed from an FIA lichen gradient model. Critical steps in standard data analysis include screening plots to exclude biased data, selection of appropriate populations, then analysis, presentation, and interpretation of data.

Does inventory management sometimes feel like a waste of time? Learn how to maximize your inventory management process to use it as a tool for making important business decisions.