Key Terms

  • Buffer

    A buffer is an excess resource placed at a point in a project’s workstream that corrects for the misalignment between demand for resources at that point in the work stream and the available supply of resources. It can take on of three forms:

    1. Inventory (extra materials or stocks)
    2. Time (a delay between a demand and the satisfaction of that demand)
    3. Capacity (extra capability needed to satisfy irregular or unpredictable demand rate)
    Sources: “Factory Physics”, W. J. Hopp and M. L. Spearman, Third Edition, Waveland Pr. Inc. p 202
  • Capacity (Base)

    The capacity of a process is the maximum average rate at which items, units, information can flow through the process. The base capacity of a process refers to the maximum rate at which the process can handle items/units/information under ideal conditions. Equivalently the maximum amount of customer demand that can be satisfied over a certain period of time, where “customer” might be the next station or operation in a production system.

    Sources: Supply Chain Science, First Edition, Wallace J. Hopp, 2008, Waveland Pr. Inc., p 18, Maynard’s Industrial Engineering Handbook, Fifth Edition, Kjell B. Zandin (ed.), McGraw-Hill 2001, p G.3
  • Constraint

    In Project Production Control, a constraint is a predecessor task that must be completed in order for work to flow through the process.

    Sources: PPI
  • Inventory (Also known as Stock

    Working stock: Inventory that is actively being processed or moved Congestion stock: Inventory that builds up unintentionally as a consequence of variability in the system Cycle stock: Inventory that results from batch operations Safety stock: Inventory that exists intentionally to buffer variability Anticipation stock: Inventory that is built up in expectation of future demand.

    Sources: Supply Chain Science, First Edition, Wallace J. Hopp, 2008, Waveland Pr. Inc., (Page 117)
  • Kingman’s Equation (VUT equation)

    In queuing theory, a discipline within the mathematical theory of probability, Kingman's formula is an approximation for the mean waiting time in a queue in a system with a single server where arrival times have a general (meaning arbitrary) distribution and service times have a (different) general distribution. It is generally represented as CT = VUT, where CT is Cycle Time, V is a factor representing variability, U is a factor representing utilization, and T represents the mean effective process time.

    Sources: Factory Physics, Third Edition, Wallace J. Hopp and Mark L. Spearman, Page 288
  • Line of Balance

    The "Line of Balance" (LOB) is a graphic device that enables a manager to see at a single glance which of many activities comprising a complex operation are "in balance" - i.e., whether those which should have been completed at the time of the review actually are completed and whether any activities scheduled for future completion are lagging behind schedule. History: LOB was devised by the members of a group headed by George E. Fouch. During 1941, the Goodyear Tire & Rubber Company monitored production with LOB. It was successfully applied to the production planning and scheduling of the huge Navy mobilization program of World War ll. LOB proved to be a valuable tool for expediting production visibility during the Korean hostilities. During this period, defense suppliers used LOB

    Sources: Line of Balance Technology: A graphic method of industrial programming, US Department of Navy, April 1962
  • Little’s Law

    Little's Law defines the relationship between system throughput (TH), cycle time (CT), and work-in-process (WIP). TH = WIP/CT.

    Sources: Factory Physics, Third Edition, Wallace J. Hopp and Mark L. Spearman, Waveland Pr. Inc., p 698, Queues, Inventories and Maintenance: The Analysis of Operational Systems with Variable Demand and Supply, P. M. Morse, Dover, 2004, p 22, A Proof for the Queuing Formula: L = lW, J. D. C. Little, Operations Research, Volume 9, Issue 3, pp 383-387, Little’s Law as viewed on its 50th Anniversary, J. D. C. Little, Operations Research, Volume 59, Issue 3, pp 536-549
  • Operations Management

    Also known as Production Management
. The control of activities involved in producing goods and providing services – processes and supply chains - and the study of the best ways to do this.
 Operations Management is a science that emerged from the modern manufacturing industry and focuses on modeling and controlling actual work processes. When associated with manufacturing it was known as Production Management. As it become recognized that the theory and methods were also applicable to planning and execution of services, the discipline became known as Operations and/or Production management. The practice is based upon defining and controlling production systems, which typically consist of a series of inputs, transformational activities, inventory, and outputs. 
In the last 50 years, project management (conventional project management as defined by the Project Management Institute) and operations management have been viewed as distinct disciplines. Project Production Management applies operations management to the control of projects, as PPI’s working papers demonstrate.

    Sources: Schmenner, R. W. (1993), Production/operations management: from the inside out. Macmillan College, Production and Operations Management, 6th Edition, A. Muhlemann, J. Okland and K. Lockyer, Pitman, London, 1992, Operations Management, R. A. Johnson, W. T. Newell and R. C. Vergin, Houghton Mifflin, 1972, “Contrasting Project Controls with Project Production Control”, PPI technical report, Jan 2015
  • Operations Research (OR)

    The application of mathematical (quantitative) techniques to the scientific study and analysis of complex systems and optimization problems of organization and coordination of activities in business operations and decision making.

    Historically the discipline emerged as the confluence of several different threads of scientific application. Charles Babbage conducted research into the cost of transportation and sorting of mail which were applied in the UK’s first postal system. To understand the best choice of railway gauge, he conducted studies into dynamical behavior of railway vehicles on a railway network. Military planners during World War 1 were concerned with the scientific planning and organization of logistics of supplies for troops (convoy theory and Lanchester’s laws). The field expanded with the Second World War conferring several problems for US and UK military planners resulting in the development (among other things) of linear and dynamic programming techniques, and other mathematical tools. Since then, the field has found application in many areas in transportation, finance, logistics and government.

    Sources: Introduction to Operations Research, F. S. Hillier and G. J. Lieberman, Eight Edition, McGraw- Hill, p3., Maynard’s Industrial Engineering Handbook, Fifth Edition, Kjell B. Zandin (ed.), McGraw-Hill 2001, p G.2
  • Production Control

    Policies, protocols and mechanisms to control transformational processes, the use of resources and variability.

    Sources: PPI
  • Production System

    Any of the methods used in industry to create goods and services from various resources.

    Sources: Encyclopedia Britannica

  • Project Production Management

    The application of the scientific techniques in operations research, queueing theory and industrial engineering to the optimization and execution of projects. In short, as Factory Physics formed a scientific framework for manufacturing management, PPM is the application of the same Factory Physics principles to the execution of projects.

    Sources: PPI
  • Station Cycle Time Formula

    The Station Cycle time formula states that the average cycle time at a station in a production system includes actual Processing Time (PT), as well as Move Time (MT), Setup Time (ST), Queuing Time (QT), Batch Time (BT) (CT=PT + MT + QT + ST + BT) Cycle time for an end-to-end process is CT = PT + MT + SDT + BT + QT where SDT represents the shift differential time.

    Sources: Factory Physics, Third Edition, Wallace J. Hopp and Mark L. Spearman, Waveland Pr. Inc., p 327
  • Variability

    In the context of Project Production Management, variability refers to the non-uniformity or variation, perhaps through statistical randomness or otherwise, in operating parameters that occur in the course of executing the work activities in a project. For example, the cycle time to complete a given task in general is variable. The availability of equipment (capacity) at a given time is typically variable. The arrival times of raw materials for processing are variable. Examples of key operating parameters that can be affected by variability are capacity, inventory, cycle times, arrival times, completion times.

    Sources: PPI, Factory Physics, W. J. Hopp and M. L. Spearman, Third Edition, Waveland Pr. Inc., p265
  • Work in Process (WIP)

    The total inventory between the start and end point of a process. Also refers to the inventory between intermediate steps. WIP is related to Cycle Time (CT) and Throughput (TH) by Little’s Law.

    Sources: Factory Physics, Third Edition, Wallace J. Hopp and Mark L. Spearman, Waveland Pr. Inc., p 230