El Blog de Smart

Siguiendo las mejores prácticas en la planificación de la demanda,

previsión y optimización de inventario

A readable, well-organized textbook could be invaluable to “help corporate forecasters-in-training understand the basics of time series forecasting,” as Tom Willemain notes in the conclusion to this review, originally published in Foresight: The International Journal of Applied Forecasting. Principally written for an academic audience, the review also serves inexperienced demand planning professionals by pointing them to an in-depth resource.

This neat little book aims to “introduce the reader to quantitative forecasting of time series in a practical, hands-on fashion.” For a certain kind of reader, it will doubtless succeed, and do so in a stylish way.

The author, Dr. Galit Shmueli, is the SRITNE Chaired Professor of Data Analytics and Associate Professor of Statistics and Information Systems at the Indian School of Business, Hyderabad. She has authored or coauthored several other books on applied statistics and business analytics.

The book is meant to be a text for a “mini-semester” course for graduate or upper-level undergraduate students. I think it would be a stretch to believe there is enough technical material here to serve as the basis for a graduate course, but I could see it working well for undergraduates in industrial engineering or management who have had a prior statistics course (and therefore will indeed be able to “recall that a 95% prediction interval for normally distributed errors is…”).

There are end-of-chapter exercises of appropriate size and even setups for three real-world semester projects, so instructors could use the book as envisioned by the author. The book illustrates its points using XLMiner, an Excel add-in, and students can use the free demo version for almost all the exercises. Text datasets are available from the book’s web site, which also provides a free time series analysis “dashboard” application. The author notes that other software can be used in place of XLMiner and mentions Minitab, JMP, and Rob Hyndman’s forecast library in R.

While reading this book, I was delighted by its clarity. Having spent time recently correcting the technical prose of two otherwise good graduate students, I found the writing in this book to be a refreshing contrast, making technical concepts understandable.

Another virtue of this book is its selection of topics. The technical ones are reasonably standard (smoothing methods, regression using polynomial trends, and dummy variables) but also range a bit toward the more exotic (logistic regression, neural nets, a bit of ARIMA). More impressive is the inclusion of what might be called “meta-topics” relevant to forecasting: performance assessment, an overview of alternative technical approaches, and one on the forecasting process, from definition of goals to ways to gear reports differently for managerial and technical audiences. This is the kind of forecasting wisdom we find in Chris Chatfield’s book (2004), though presented rather less tartly and with less mathematical exposition. I typically recommend Chatfield’s introductory book for more technical readers interested in getting into time series; I would recommend Shmueli’s book for a more general audience.

No review is complete without quibbles. Here are a few—too few to undo my very positive view of this impressive little book:

• The text makes a good case for “well formatted and easily readable” charts (p. 179). But I found many of the screen shots to be poorly printed and difficult to see. The book is otherwise so visually pleasing that these defects seem very out of character. It uses luxurious amounts of white space and whimsical marginal art to great effect, producing a very “light” feel that must surely help comprehension.

• The author claims (p. 115) that smoothing methods (e.g., moving averages, exponential smoothing) cannot be fully automated because “the user must specify smoothing constants.” Of course, this is not so, since there are several software packages that do this, and the text later contradicts itself on this point on page 127.

• The otherwise good discussion of autocorrelation misleads when it claims (p. 88) that negative lag-1 autocorrelation means that “high values are immediately followed by low values and vice versa.” Well, usually, but not always.

When I finished reading this book, I realized immediately that there is another target audience outside the classroom. My company often conducts training sessions on the use of our software, and these include some general background on forecasting methods and processes. If we could excise the material on XLMiner, and even if we couldn’t, this text would make a wonderful “leave behind” to help corporate forecasters-in-training understand the basics of time series forecasting. The book is so well written, well organized and well designed that it might even be read. We can certainly use it to help our new programmers understand the applications they are developing. And this book might even serve as guilty reading for a graduate student who wants to really “get” what’s going on in Box, Jenkins and Reinsel (2008).

Thomas Willemain, PhD, co-founded Smart Software and currently serves as Senior Vice President for Research. Dr. Willemain also serves as Professor Emeritus of Industrial and Systems Engineering at Rensselaer Polytechnic Institute and as a member of the research staff at the Center for Computing Sciences, Institute for Defense Analyses.

Deja un comentario

Artículos Relacionados

Mensajes recientes

  • Epicor Prophet 21 with Forecasting Inventory PlanningExtend Epicor Prophet 21 with Smart IP&O’s Forecasting & Dynamic Reorder Point Planning
    Smart Inventory Planning & Optimization (Smart IP&O) can help with inventory ordering functionality in Epicor P21, reduce inventory, minimize stockouts and restore your organization’s trust by providing robust predictive analytics, consensus-based forecasting, and what-if scenario planning. […]
  • Supply Chain Math large-scale decision-making analyticsSupply Chain Math: Don’t Bring a Knife to a Gunfight
    Math and the supply chain go hand and hand. As supply chains grow, increasing complexity will drive companies to look for ways to manage large-scale decision-making. Math is a fact of life for anyone in inventory management and demand forecasting who is hoping to remain competitive in the modern world. Read our article to learn more. […]
  • Mecánico barbudo maduro en uniforme examinando la máquina y reparándola en fábricaService Parts Planning: Planning for consumable parts vs. Repairable Parts
    When deciding on the right stocking parameters for spare and replacement parts, it is important to distinguish between consumable and repairable servoce parts. These differences are often overlooked by inventory planning software and can result in incorrect estimates of what to stock. Different approaches are required when planning for consumables vs. repairable service parts. […]
  • Cuatro errores comunes al planificar los objetivos de reposiciónCuatro errores comunes al planificar los objetivos de reposición
    ¿Con qué frecuencia recalibra sus políticas de almacenamiento? ¿Por qué? Aprenda a evitar errores clave al planificar objetivos de reabastecimiento mediante la automatización del proceso, la recalibración de piezas, el uso de métodos de previsión de objetivos y la revisión de excepciones. […]
  • Smart Software se complace en presentar nuestra serie de seminarios web, ofrecidos exclusivamente para usuarios de Epicor.Amplíe el pronóstico y la planificación mínima/máxima de Epicor Kinetic con Smart IP&O
    Epicor Kinetic puede administrar el reabastecimiento al sugerir qué ordenar y cuándo a través de políticas de inventario basadas en puntos de reorden. El problema es que el sistema ERP requiere que el usuario especifique manualmente estos puntos de pedido o use un enfoque rudimentario de "regla general" basado en promedios diarios. En este artículo, revisaremos la funcionalidad de pedido de inventario en Epicor Kinetic, explicaremos sus limitaciones y resumiremos cómo reducir el inventario y minimizar los desabastecimientos al proporcionar la sólida funcionalidad predictiva que falta en Epicor. […]

    Optimización de inventario para fabricantes, distribuidores y MRO

    • Pedidos generales Software inteligente Demanda y planificación de inventario HDÓrdenes generales
      Nuestros clientes son grandes maestros que siempre nos han ayudado a cerrar la brecha entre la teoría de los libros de texto y la aplicación práctica. Un excelente ejemplo sucedió hace más de veinte años, cuando nos presentaron el fenómeno de la demanda intermitente, que es común entre las piezas de repuesto pero poco común entre los productos terminados administrados por nuestros clientes originales que trabajan en ventas y marketing. Esta revelación pronto llevó a nuestra posición preeminente como proveedores de software para la gestión de inventarios de piezas de repuesto. Nuestra última parte de la educación se refiere a las "órdenes generales". […]
    • Mano colocando piezas para construir una flechaPronóstico Probabilístico para Demanda Intermitente
      La nueva tecnología de pronóstico se deriva del pronóstico probabilístico, un método estadístico que pronostica con precisión tanto la demanda promedio de productos por período como los requisitos de inventario del nivel de servicio al cliente. […]
    • Ingeniería bajo pedido en Kratos Space: hacer que la disponibilidad de piezas sea una ventaja estratégica
      El grupo Kratos Space dentro del innovador en tecnología de seguridad nacional Kratos Defense & Security Solutions, Inc., produce el software COTS y los productos de componentes para las comunicaciones espaciales: hacer de la disponibilidad de piezas una ventaja estratégica […]
    • figuras-de-madera-de-personas-y-un-iman-equipo-gestion-almacen inventarioGestión del inventario de artículos promocionados
      En una publicación anterior, analicé uno de los problemas más espinosos que a veces enfrentan los planificadores de demanda: trabajar con datos de demanda de productos caracterizados por lo que los estadísticos llaman asimetría, una situación que puede requerir costosas inversiones en inventario. Este tipo de datos problemáticos se encuentran en varios escenarios diferentes. En al menos uno, la combinación de demanda intermitente y promociones de ventas muy efectivas, el problema se presta a una solución efectiva. […]