Two Industial engineers(IE) manage two adjacent subassembly lines separated by a

Question

Two Industial engineers(IE) manage two adjacent subassembly lines separated by a buffer with a fixed capacity as shown in the figure below. They are good friends but they like messing with each other.

Both lines A and B are paced and currently running in a steady state. The new plant manager puts high emphasis on maintaining low WIP levels.

a. What can the IE managing line A do in order to increase WIP levels on line B?

b. What can the IE managing line B do in order to increase WIP levels on line A?

Explanation / Answer

Advances in technology and changes in the nature of competition have affected the structure of manufacturing and service operations. Yet, we have seen no concomitant change in the paradigms for modeling and managing operations, despite acknowledged dissatisfaction with them. The paradigm that remains in use can be crudely characterized as "static optimization of a known objective function, subject to known and stationary constraints." This paradigm holds manufacturing separate from knowledge-creating activities, such as product design, process design, and scientific research. Thus research on R&D and engineering have traditionally been a separate subdiscipline from research on manufacturing.
We propose adynamicapproach that explicitly treats important elements of modern manufacturing operations that are ignored by static paradigms-- specifically, knowledge, learning, problem solving, and contingencies. We show that static and dynamic perspectives are effective in different manufacturing contexts and offer evidence that the domain in which a dynamic approach is applicable is growing. It is no longer sufficient to ask "How can we make this operation more efficient at its existing tasks?" Research must also be directed at "How can we become better at recognizing and dealing with contingencies, learning from their resolution, and accumulating a broader base of knowledge?" Some managers are asking this question, but the use of dynamic and knowledge oriented approaches remains an ad hoc pursuit, without much theoretical foundation.
We begin by articulating some of the implicit assumptions in operations management research. Section 3 describes our suggested alternative, a "dynamic approach" that emphasizes the importance of knowledge in production systems. Section 4 discusses the design and operation of an automated assembly line. We show that traditional static models of assembly lines ignore key activities of managers and workers, which center on dynamic rather than static issues. In Section 5, we consider other applications of a dynamic approach. We conclude, in Section 6, with a reexamination of the comparison between static and dynamic approaches, and a discussion of the research that will be needed to build a useful and rigorous dynamic view of manufacturing.
A single paper cannot fully establish the validity or usefulness of a dynamic approach. We emphasize operations research, the manufacturing domain, and the academic study of operations management. Management practice in some industries is well ahead of research in incorporating dynamic issues into day to day manufacturing activities and is the key motivation for seeking new academic paradigms. We discuss some practical techniques in the penultimate section of the paper, but defer a general dynamic analysis of present day operations management.

2.OPERATIONS MANAGEMENT IN A STATIC WORLD
It is useful to review standard production and operations management before discussing alternatives. When he coined the term "scientific management" in the 1900's, Frederick W. Taylor[1]started the field of industrial engineering (Taylor, 1947). The assumptions, models, and thought patterns of Taylorism, so influential in the tremendously successful development of American mass production, persist to this day. One of those assumptions was that there exists "one best way" to undertake each task.
The principal assumptions of the static paradigm are that: production technology is known, that labor's role is solely to perform procedures, that the environment is known and stationary, that inputs are homogenous, and that there is a single, known goal. Working under these assumptions, the manager or analyst selects the type, amount, or use of assets. Lower level management or workers, such as manufacturing engineers, then implement the decision. Choice and execution are separate stages, and neither feeds back to the other. The job of the manager is once-and-for-all decision making, as opposed to incremental problem solving or ongoing learning.
Taylor was himself an avid experimenter, with a strong belief in the importance of knowledge about technological processes. For example, his model of cutting speeds versus tool wear, developed through exhaustive experimentation, is still used today. However in Taylor's paradigm, knowledge was developed off-line by specialists and then passed in a one way information flow to workers. The role of feedback (actual performance compared with expected performance) was limited to a punitive one, not as a source of knowledge. This view that R&D can and should be completely separate from execution is a crucial difference between dynamic and static approaches.
We will now review each of the major implicit assumptions in the static operations management paradigm. We do not claim that managers or modelers defend the literal truth of these assumptions, but lack of appropriate dynamic frameworks leads them to make decisions and build models as if the assumptions were approximately correct.
Assumption 1: Known Production Technology
This assumption states that manufacturing knowledge is complete. Each realistically possible production technique is known, well defined, and fixed over time. This includes hardware (equipment), operating procedures, and other aspects of the technology. In situations involving a new plant or new equipment, the relevant knowledge is assumed to be available from vendors for a price. Knowledge about the technology is thus not a key issue, and learning is not a goal. Design of production methods is hence a "choice of technique" problem: which of the known available machines and procedures should be used? This technique becomes the "optimal" way to produce.
The only role of learning in this view of the world is as training--the transfer of procedure from manuals or instructors to employees who are taking on an unfamiliar task (e.g., new assembly line workers who need to be taught procedures). There is no need for organizational learning or research.
Assumption 2: Labor's Role Is Solely to Perform Procedures
This assumption holds that the task of each worker is to carry out assigned, fully specified procedures in response to unambiguous signals from machines, other workers, and the environment. By "procedure" we mean a well defined set of actions, analogous to a computer program. Procedures, like computer programs, may contain some conditional instructions, but all contingencies are assumed to have been anticipated and appropriate responses specified. Management's role is to specify these procedures (the "one best way") and monitor their execution. Since tasks are well defined, performance can be monitored simply by "looking over the worker's shoulder," i.e., by observing inputs and whether operator behavior follows the correct procedures. Learning and modification occur external to the production unit, with new procedures communicated back to it.
This assumption is powerful and useful when the necessary tasks have been reduced to appropriate procedures. However, reduction to procedure is not desirable for some environments and tasks characterized by large and important contingencies, high complexity, or ambiguity about key variables and their relationships. This includes many aspects of product design and problem solving.
Assumption 3: Known and Stationary Environment
This assumption holds that the environment, like the technology, is known and stationary. Usually it is static (i.e., deterministic and constant). If not static, it is stationary (i.e. drawn from a stationary probability distribution with known parameters). For example, when capital investment choices are being made, demand and factor costs are assumed to be known for the life of the equipment. So is the nature and performance of the production technology. At the extreme of this assumption, product markets, input markets, workers, and machines are all deterministic and unchanging. Choice is easy under such a strong assumption: just optimize for the current environment. If it is not deterministic, the environment is assumed to be stationary.
Assumption 4: Homogeneous Inputs
Factors of production such as labor, raw materials, machinery, and energy, are assumed to be homogeneous, with exogenously determined standardized characteristics, and available in complete markets. The markets are usually assumed to be efficient.
Assumption 5: Known Goal
This assumption holds that the purpose or goal (objective function) of the organization is known and well defined, and is uniform throughout the organization. Typically this goal is profit maximization or a temporary sub-goal such as maximizing output.
These five assumptions lead to a consistent view of a world that may be complex, but is fully specified. The task of operations management research is static optimization to select the best way to produce despite complexity. The task of managers is to select a rigid procedure for workers, then monitor and ensure their compliance.
This static paradigm has no place for explicit consideration of dynamic issues such as knowledge, learning, or problem solving. It deals with contingencies only by invoking predetermined conditional procedures and by using stationary stochastic models which treat the contingencies as exogenous.

Previous Literature
We are not the first to argue that standard academic approaches to manufacturing management are deficient for the modern

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