Logistics Management: An Analytics-Based Approach
LOGISTICSMANAGEMENTAnAn Analytics-BasedAnalytics-Based J.LiberatoreLiberatore
Logistics Management
Logistics ManagementAn Analytics-Based ApproachTan MillerMatthew J. Liberatore
Logistics Management: An Analytics-Based ApproachCopyright Business Expert Press, LLC, 2020.All rights reserved. No part of this publication may be reproduced, storedin a retrieval system, or transmitted in any form or by any means—electronic, mechanical, photocopy, recording, or any other except forbrief quotations, not to exceed 250 words, without the prior permissionof the publisher.First published in 2020 byBusiness Expert Press, LLC222 East 46th Street, New York, NY 10017www.businessexpertpress.comISBN-13: 978-1-94944-384-4 (paperback)ISBN-13: 978-1-94944-385-1 (e-book)Business Expert Press Supply and Operations Management CollectionCollection ISSN: 2156-8189 (print)Collection ISSN: 2156-8200 (electronic)Cover image licensed by Ingram Image, StockPhotoSecrets.comCover and interior design by S4Carlisle Publishing Services Private Ltd.,Chennai, IndiaFirst edition: 202010 9 8 7 6 5 4 3 2 1Printed in the United States of America.
DedicationThis book is dedicated to my wife Jeanne and sons Lucas and Natewho have been so supportive and put up with me for all these years.—Tan MillerThis book is dedicated to my wife Mary and my daughtersKathryn, Michelle and Christine for always being there for me.—Matt Liberatore
AbstractAs we head into the ever-more globalized world of the 2020s, the critical role that logistics planning and operations plays in ensuring a firm’sfinancial well-being escalates in importance almost daily. Furthermore,the role of analytics in guiding both logistics planning and operationalactivities has spiked dramatically in the last decade, and this exponentialgrowth shows no sign of slackening. As the phenomenon of Big Datahas taken hold in the private sector, firms that as recently as 10 years agodevoted minimal resources to large-scale data mining and analytics havereversed course and invested heavily in data analytics.In this environment, logistics professionals must have at their disposal,and must understand how to utilize, a broad array of analytic techniquesand approaches to logistics decision making. Effective use of analyticsrequires a strong understanding of both fundamental and advanced logistics decision-making techniques and methodologies. Further, logisticsprofessionals must organize and view these analytics-based decision support tools through well-structured planning frameworks.In this book, based on more than 25 years of logistics industry practice, we illustrate and explain a wide range of practical logistics strategies and analytic techniques to facilitate decision making across functionssuch as manufacturing, warehousing, transportation, and inventory management. Further, we also describe how to organize these analytics-basedtools and strategies through logistics frameworks that span strategic, tactical, and operational planning and scheduling decisions.This book is intended for logistics professionals to use as a referencedocument that offers ideas and guidance for addressing specific logisticsmanagement decisions and challenges. In particular, it provides explanatory and “how to implement” guidance on foundational analytics that logistics professionals can employ to generate practical insights to facilitatetheir daily and longer-term logistics management activities. This bookcan also serve as a valuable resource or secondary text for graduate and advanced undergraduate students. Students will develop an understandingof leading edge, real-world approaches for logistics planning and scheduling, decision support, performance measurement, and other key logisticsactivities.
viiiABSTRACTKeywordslogistics planning; logistics analytics; logistics planning frameworks; logistics management; operations management; logistics performance measurement; logistics metrics; transportation planning; transportation modechoice decision making; integrated inventory and transportation planning; strategic planning; production planning and scheduling; distribution planning; logistics; hierarchical planning; decision support systems;activity-based costing; collaborative planning, forecasting, and replenishment; the analytic hierarchy process; feedback loops
ContentsChapter 1Introduction.1Part IIntegrated Inventory and TransportationMode Decision Making. 13Integrating Long-Term Transport ModeSelection into Overall Network Planning.15Logistics Transport Mode Decision Making.19Sensitivity Analysis.35Chapter 2Chapter 3Chapter 4Part IIChapter 5Chapter 6Chapter 7Chapter 8Chapter 9Part IIIChapter 10Chapter 11Chapter 12Chapter 13Logistics Decision Support. 47Introduction.49Manufacturing and Distribution Decision Support.55Inventory Management Decision Support.83Incorporating Feedback Loops into LogisticsDecision Support.103Using Activity-Based Costing in LogisticsDecision Support.117Metrics and Techniques for LogisticsMonitoring and Control. 131Introduction.133Illustrative Logistics Performance Frameworks.135Creating Customized Indices to MonitorLogistics Operations.145Techniques for Monitoring Day-to-DayTransportation Decisions.151Chapter 14 Final Thoughts on Logistics Analytics andDecision Support.161References.163About the Authors.167Index.169
CHAPTER 1IntroductionTo introduce a previous book published in 2012, we stated that “in today’scompetitive global economy, a firm’s market position and bottom-line financial performance is closely linked to its logistics performance.” Today,as we head into the ever-more globalized world of the 2020s, the criticalrole that logistics planning and operations plays in ensuring a firm’s financial well-being escalates in importance almost daily. Furthermore, the roleof analytics in guiding both logistics planning and operational activitieshas spiked dramatically since 2012, and this exponential growth showsno sign of slackening for the foreseeable future. Examples of this aboundas in recent years many firms, both large and small, have launched newData Science or similarly named groups. As the phenomenon of Big Datahas taken hold in the private sector, firms that as recently as 10 years agodevoted minimal resources to large-scale data mining and analytics havereversed course. The fear of competitive disadvantage and the promise ofgaining competitive advantages through data analytics have stimulatedenormous internal investments in labor (i.e., data scientists), hardware,and software across industry.In this environment, logistics professionals must have at their disposal,and must understand how to utilize, a broad array of analytic techniquesand approaches to logistics decision making. Effective use of analyticsrequires a strong understanding of both fundamental and advanced logistics decision-making techniques and methodologies. Further, logisticsprofessionals must organize and view these analytics-based decision support tools through well-structured frameworks.In this book, we illustrate and explain a wide range of practical logistics strategies and analytic techniques to facilitate decision making acrossfunctions such as manufacturing, warehousing, transportation, and
2LOGISTICS MANAGEMENT: AN ANALYTICS-BASED APPROACHinventory management. Further, we also describe how to organize theseanalytics-based tools and strategies through logistics frameworks that spanstrategic, tactical, and operational planning and scheduling decisions. Logistics professionals can use this text as a reference document that offersideas and guidance for addressing specific logistics management decisionsand challenges. In particular, this book provides explanatory and “how toimplement” guidance on foundational analytics that logistics professionals can employ to generate practical insights to facilitate their daily andlonger-term logistics management activities.Objectives of the BookIn over 25 years of private industry and consulting experience, we haveimplemented numerous management decision support and performancemeasurement systems to manage logistics functions. Further, we have implemented these systems with a keen eye focused on how each system andtechnique fits into an overall framework for analytics-based logistics decision making in a firm. The implementation of one system or techniquetypically leads to additional related implementations over time, particularly if the initial implementation generates benefits highly valued by anorganization. For this reason, it is critical that one view the developmentand installation of logistics decision support systems (DSS) within thecontext of the logistics organization’s overall long-term and short-termneeds. Our experience has taught us that firms that take this approachmake themselves significantly more competitive and agile relative to firmsthat bounce from one implementation to the next without an overallframework and vision for their logistics decision making processes.We have several objectives in writing this book. First, we wish to communicate to other logistics practitioners and executives the value of investing in the logistics analytics tools that we describe. These methodsserved us well in practice, and we strongly recommend them. And as wewill illustrate, all the analytics techniques we present can be readily implemented. Our second objective is to raise the visibility and, ultimately, theutilization of these methodologies. In this new age of analytics, one mayquestion whether there remains a need to illuminate further the value oflogistics analytics. However, despite the “buzz” about “big data” and the
Introduction3“digital age,” and despite the rapid growth in this area during the last decade, many current surveys of practitioners find major logistics decisionsstill being made without appropriate decision support tools. For example,Supply Chain News recently conducted a survey across 23 countries andmany industries ranging from “chemicals to manufacturing, metals, telecom, cosmetics, consumer goods, transportation and food.” This surveyfound that:Most respondents rely on a combination of spreadsheets (nearly60%), gut feel (15%) and previous experience (45%) to make supply chain network decisions. Only about 22% use network designsoftware and more than half of the professionals assessed indicated that they didn’t use some form of advanced analytics to supporttheir network design process. (Further) none of these respondentsshare data across multiple applications in an integrated way.1This leads to our third objective for this book, namely, by presentinganalytics tools in easy-to-follow illustrations, we hope to facilitate the implementation of these methodologies by others who wish to utilize them.A Hierarchical Framework for Logistics PlanningThe planning activities and decisions that management must make for thelogistics function range from the extremely long run to the short run dayto day.2 Further, the characteristics of these activities and decisions rangefrom those requiring vast resources and managerial time (as measured bycost, required planning inputs, level of risk, and other attributes) versusthose requiring relatively minimal time and resources. For example, consider the vast differences in the required inputs for, and implications of,a plant location and sizing decision versus a one-week production linescheduling decision. To effectively address this broad spectrum of management and operational control activities and decisions required in any1Supply Chain News (November 7, 2018)To be clear, this statement applies to supply chain management and all of its functionalareas. Because the logistics function, a major subcomponent of supply chain management, is the subject of this book, we focus on this activity and its subcomponents.2
4LOGISTICS MANAGEMENT: AN ANALYTICS-BASED APPROACHmajor logistics function (e.g., manufacturing), it is necessary to separatethe future planning horizon into three buckets:1. Strategic Planning,2. Tactical Planning, and3. Operational PlanningThese three planning horizons must be closely and hierarchicallylinked to ensure aligned decision making, and we will discuss analytictechniques and strategies that facilitate this alignment throughout thebook. The interested reader is also referred to Liberatore and Miller(2012) to learn more about the theory and process of hierarchical planning, the types of decisions made at each level of the planning process,and techniques to facilitate the critical linkages required between the logistics management function and a firm’s business mission, objectives,and strategies.Figure 1.1 displays a generic logistics planning framework. We describe this framework as generic because it illustrates that the planningactivities of any individual logistics function can (and should) be linkedto the overall business and logistics strategic planning process of an organization. Examples of significant individual logistics functions include: ManufacturingDistributionCustomer serviceInventoryTransportation3The definition of what constitutes a major individual logistics function will vary by firm. For example, some firms may consider manufacturing as being separate from logistics. Some firms consider customer serviceas a component of their logistics organization, while other firms do not.Regardless of how many functions within a logistics organization a firmchooses to define as major individual units, the framework in Figure 1.1provides a well-defined, holistic organizational approach.3The analytics decision support methods and strategies presented in this book willconcentrate primarily on these five functional areas.
nningConstraintsFigure 1.1 A logistics planning ogistics Strategic Planning To support firm/business unit: Mission Goals and Objectives entoryPlanningSYSTEMSMEASUREMENTPERFORMANCE“1 to 18 months”Operational“12 to 24 mos”Tactical“2 yrs. ”Strategic
6LOGISTICS MANAGEMENT: AN ANALYTICS-BASED APPROACHThe logistics planning framework is driven by a firm’s business strategic planning process. The goals and objectives developed at the businessunit level establish requirements and define capabilities that the logisticsorganization must provide to support business objectives. This facilitatesthe next strategic planning process, where the logistics organization formulates its overall mission, goals, objectives, and strategies. The outputsof this process generate high-level requirements and define capabilitiesthat the individual functions within logistics must then deliver. At thispoint, individual functions such as manufacturing and distribution mustinitiate their own planning processes to map out the contributions thatthey will each make in support of the overall logistics plan.The hierarchical planning framework requires that each individual function delineate the future planning horizon into strategic, tactical, and operational planning buckets. Thus, each logistics function has its own strategic,tactical, and operational planning processes. To illustrate the different typesof decisions and management controls exercised at each planning level, notein Figure 1.1 that at the tactical level we use “scheduling” as a function descriptor while at the operational level “execution” is the function descriptor.In practice, at the tactical level one observes both planning and schedulingactivities, while at the operational level, planning, scheduling and executionactivities all occur. Finally, also note the following in the figure:1. There are bidirectional vertical lines between the strategic, tactical, andoperational planning levels of each logistics function (e.g., manufacturing). The lines flowing from higher to lower levels illustrate that decisions made at higher levels impose constraints or boundaries on decisionsmade at lower levels. Conversely, lines emanating from lower to higherlevels are known as a “feedback loops” in a hierarchical planning systemand illustrate the critical need for upward input and communicationsfrom lower to upper levels. We will discuss and provide examples of bothtop-down constraints and bottom-up feedback loops later in this book.2. There are dashed horizontal lines between the individual functions.These lines illustrate that in practice, interactions in many formsshould (and do) occur between individual logistics functions. Theseinteractions can be both formal (e.g., joint planning sessions) andinformal (e.g., day-to-day communications).
Introduction7In summary, the generic logistics planning framework depicted inFigure 1.1 facilitates aligned planning, scheduling, and execution activities allthe way down to the operational levels of each individual logistics function.Analytics Decision Support and Performance MetricsNow that we have introduced a framework for logistics planning, we turnto the central focus of this book, namely, the role of analytics-based DSS.Analytics decision support methods and systems for logistics planning spana broad array of methodologies and techniques ranging from spreadsheetbased analyses and statistical analyses, to database analyses and data mining,to sophisticated mathematical optimization and simulation models.Performance measurement systems (PMS) provide managers with indicators of how efficiently and effectively their logistics network is operating. Additionally, good PMS also offer advance warnings or indicationsof potential future problems. A good PMS is also an absolute necessity tosupport the planning frameworks of a logistics organization.Figure 1.1 depicts the integral role that analytics-based decision support and PMS play in a logistics planning framework.As illustrated, each individual logistics function must have appropriate analytics tools at each level of its planning process. Similarly, eachfunction must also have pertinent performance metrics to monitor itsactivities. And collectively, the logistics organization must have the DSSand PMS tools required to manage the entire process. A firm with stronganalytics/DSS tools and PMS capabilities, but that lacks the appropriatelogistics frameworks to organize and utilize these tools cannot succeed.Similarly, a firm with good logistics frameworks, but that lacks the properDSS and PMS tools cannot succeed. However, a firm with all of thesecomponents in place positions itself to conduct effective logistics planning and successful operations.Logistics Planning: Analytics Methods andTime HorizonsAnalytics techniques to support logistics planning and operations providesu
LOGISTICS MANAGEMENT LOGISTICS MANAGEMENT MILLER LIBERATORE Logistics Management An Analytics-Based Approach Tan Miller Matthew J. Liberatore Logistics professionals must utilize a broad array of analytic techniques and approach-es for decision-making. Effective use of analytics requires an understanding of both
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