Using systems dynamics to evaluate the tradeoff among supply chain aggregate production planning policies

A multi-product and multi-period aggregate production plan: a case of automobile component manufacturing firm

Purpose

The study attempts to develop a multi-product multi-period (MPMP) aggregate production plan (APP) to fulfill the customers' demand in terms of throughput and lead time for achieving market competence.

Design/methodology/approach

This research proposes an integrated Fuzzy analytical hierarchy process (FAHP), multi-objective linear programming (MOLP), and simulation approach. Initially, FAHP is used to select the essential objectives a firm desires to achieve. Adopting the MOLP, an APP is formulated for the firm under study. Later, the simulation model of a firm is created in a discrete-event simulation (DES) software Arena© to evaluate the applicability of the proposed APP. A comparative analysis of the manufacturing performance levels (namely throughput, lead time, and resource utilization) achieved through the implication of an existing production plan and proposed APP is conducted further.

Findings

The findings from the study depict that the proposed MOLP-based APP can satisfy the customers' requirement (namely throughput and lead time) and improve the level of resource utilization compared with the firm's existing production plan.

Research limitations/implications

The proposed research facilitates researchers and practitioners to understand the process of developing MOLP-based MPMP APP and analyzing its applicability through simulation technique to be utilized for developing APP at their firm.

Originality/value

An integrated FAHP-MOLP-simulation framework is the novel contribution to the literature on production planning. It can be extended to solve strategic, tactical, and operational problems in different domains like service, healthcare, supply chain, logistics, and project management.

A model for measuring effectiveness of quality management practices in health care

Purpose Health care is an example of an organization where the needs of potential clients are much greater than the capabilities of the service delivery system. The implementation of any medical procedure, as well as the provision of any service, just like the manufacturing of any product, can be decomposed into a series of tasks. The purpose of this paper is to propose a model for measuring the effectiveness of quality assurance tasks in health-care delivery processes. Design/methodology/approach The authors analyze a system of factors that affect the implementation of tasks in a process. In their considerations, they have focused on four areas of science that describe conditions that are related to the implementation of tasks: Scheduling as a methodology for allocating resources to perform tasks; Capacity planning as a methodology for assigning values to given resources expressed by the number of tasks that can be executed with the resources; Queueing theory, used as a methodology for describing phenomena in which not all planned tasks are performed within the prescribed specification limits; and Quality management, as a methodology to ensure appropriate conditions for completing tasks (CCTs), where CCT is a representation of parameters of casual relationship between variables. Findings The authors show that the effectiveness of executing any scheduled tasks in the process is determined by the difference between the capacity of resources allocated (at a given time interval) and the number of tasks planned to be carried out at that time. The CCT conditions determine the level of capacity of the fixed amount of resources. It is shown that their deviation from the reference CCT specification may cause the nominally correct amount of resources be either too small (causing queue formation and longer wait time in hospitals) or too large to contribute to the waste in the system by creating idle capacity. Practical implications The scope of application of the model is wide. It covers tasks performed with different degrees of uncertainties regarding the capacity of resources. It applies in all areas of health care where unlike manufacturing, the services delivered and the tasks performed in the health-care delivery system are seldom identical. Every patient is treated differently than the one waiting next in line. The workloads are pre-arranged in the order they are needed and completed in accordance with the FI-FO (first in-first out) principle. The model presented in this paper makes it possible to better understand the mechanism of effectiveness and efficiency improvement and the role of humans as a specific carrier of capacity. Originality/value As most of the health-care organizations are still stuck in the soft side of quality assurance, there has been little research conducted to test the applicability of well-known productions/operation management methodologies and theories benefitting health-care systems. The formulation of a reference point of CCT in this study is to serve as a stabilizing control point with the same connotation as that of a central reference line in the statistical process control chart. The correct capacity planning is needed to determine with a high degree of probability of success in implementation of all tasks to assure quality all the time.