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Evaluating Strategic Capacity Decisions: The Use of a Hierarchical Production-Planning System
Abstract
The impact off strategic capacity decisions on production and distribution.
... Capacity strategy is a term applied to an interconnected set of decisions that determine the capacity configuration of an organization (Slack, 2015). The literature that exists on capacity strategies is evidence in itself that long-term capacity planning is indeed a major strategic decision for any organization (Miller and Liberatore, 1993;Stevenson, 2012). ...
... Wide-ranging supply chains and distant markets enhance the uncertainty about capacity needs. Capacity decisions often imply substantial financial and other resources; it becomes necessary to plan for them in advance (Miller and Liberatore, 1993;Stevenson, 2012). ...
Managing the infeasible equilibrium between demand and capacity on the
strategic level is one of the major challenges in service operations. This study aims
to develop a framework of service capacity strategies that provide decisionmakers
with various long‐term strategic options to manage the demand variability
along with the consequences of such decisions on service delivery aspects.
Besides, it seeks to demonstrate the impact of capacity strategies on perceived
service quality and which aspect of service quality would be affected. Personally
administrated questionnaires are conducted with 25 hospital department heads
or their nominees (judgmental sampling) and with 274 patients (convenience
sampling) from three central and public hospitals located in Giza governate. The
findings of this research provide evidence that organization way to deal with
capacity has a reflection on the perceived service quality. The lag capacity strategy
is associated with a lower score of perceived service quality, higher expectation
and lower experience. Meanwhile, the lead capacity strategy is associated with
improved perceived service quality, lower expectation and higher experience
scores. A deliberate decision on capacity strategy helps organizations manage the
consequences of such a decision. If the organization chooses a lead strategy, it
should be aware of the size of the lead capacity, the cost of keeping idle capacity
and how to manage that extra capacity to its benefit. If the organization chooses
a lag strategy, perceived service quality will suffer. Organizations must adopt a
high level of tactical techniques to overcome crowdedness and decreased level of
service quality. It is also crucial to managing this state with the service providers,
not from a capacity point of view but the satisfaction perspective as well. The
amount of discrepancies increases not only on the customer side but also on the
internal staff.
... After 1990 Egbelu (1991) Unit load size, machining parameters, speed, feed, depth Marucheck and McClelland (1992) Demand variation, Small volume, material inventory short, service goals like lead time, delivery speed, lumpy demand Hill et al. (1992) Breakdowns and absenteeism, the arrival of new, high-priority orders, rework, engineering change, material shortages Operator skill Cost, variety Bakke and Hellberg (1993) Lot size fluctuations Parkin and Cutri (1993) Setup times, breakdown times, tool sharpness, operator and supervision Miller and Liberatore (1993) Product-mix, strategic capacity decisions Hill (1994) Customer inputs Kidd (1999) Variety Bonal et al. (1995) Capacity parameters, cycle time Hart (1995) Demand uncertainty, customer inputs between process, organizational knowledge culture, process of design, marketing, manufacturing Robinson and Ginder (1995) Non-availability Tseng et al. (1996) Variety Lee (2014) Aging and wear Szwarc et al. (1997) Material handling percentage Pomorski (1997) OEE elementsavailability, performance, quality Leachman (1997) Non-availability Lee and Pinedo (1997) Setup times, scheduling Gindy and Saad (1998) Internal and external disturbances, tight due date targets Tseng and Jiao (1998) Quick responsiveness, design complexity, product variety Radder and Louw (1999) Volume, timing, location Ahlstrom and Westbrook (1999) Variety, flexibility, difficulty in extending MC to external networks Jonsson and Lesshammar (1999) Overall performance, breakdown, setup and adjustment, idling and minor stoppage, reduced speed, quality, startup, flow orientation Kotcher and Chance (1999) Awareness of demand and product-mix change Jiao and Tseng (1999) Lead time, logistics, variety, volume, design, safety, quality Dal et al. (2000) Breakdown, setup adjustment, idling and minor stop, reduced speed, yield loss for starting up, quality yield, wait, operator break, poor quality raw materials, absenteeism, accident, changeover, maintenance time Alford et al. (2000) Demand uncertainty, supply and logistics, complexity, quality, designrelated issues, volume, variety Swaminathan (2000) Rate of change in products and, technology, uncertain demand, complex product flows, random yields, diverse equipment characteristics, downtimes, shared facilities and data maintenance, new products Liu et al. (2000) Supplier performance Occhino (2000) Product complexity Rajasekharan and Peters (2000) Product-mix, demand uncertainty, quality (continued ) ...
... Before MC came into existence, Skinner (1974) concluded that the focused factory does a better job because of repetition and concentration in one focus area. Miller and Liberatore (1993) described that a narrow product-mix plant will outperform the conventional plant that tries to attempt a broader mission with larger mix. However, definitions of MC by various authors insisted on being able to achieve higher efficiencies while meeting the broader range (Tseng and Jiao, 1998). ...
Purpose
The purpose of this paper is to address the gap between definition and practical aspects of production efficiency in mass customization (MC). The paper summarizes all major issues impacting efficiency in MC. Also, the paper reviews metrics, relationship between various parameters and provides a best practices benchmark toolkit to achieve higher machine efficiencies.
Design/methodology/approach
The paper identified and categorized multiple challenges impacting machine efficiency in MC through a literature review spanning over three decades, and also ranked the identified issue-based parameters. Top issues were found varying across different types of industries identified through the review. Metrics pertaining to efficiency and degree of MC are reviewed in the paper. A chronological review of issues is presented, and a chain diagram is built in the paper. Toolkit of best practices created with solution strategies and tools are summarized through the review.
Findings
The paper found that MC reasonably impacts machine efficiency which needs to be addressed. Major issues through literature review-based ranking are uncovered, and worldwide research trend and comparison are presented. Active research in this area is observed to be at its peak since 2010. The extensive use of strategies and benchmark toolkit for improving efficiency are summarized.
Research limitations/implications
Ranking of issues has been done through a literature review; hence, there can be skewness depending on the frequency of issues researched by various authors in various areas of industries.
Practical implications
This paper is useful for manufacturing managers and companies willing to increase the size of their product portfolio and choices within their available resources without compromising machine efficiencies and, thereby, the cost. The identified issues help in providing a comprehensive issue list to the academia.
Originality/value
This paper describes what is believed to be the first study that explicitly examines the issues faced in achieving machine efficiency while manufacturing in an MC environment.
... Dentro de las aplicaciones en sistemas de producción se pueden comentar las presentadas en las re-ferencias [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26]. Todos estos trabajos, se aplican en ambientes reales de manufactura, y se sustentan especialmente por modelación matemática. ...
El problema de la planificación y control de la producción se reconoce como un problema complejo dentro de las organizaciones dado que involucra la asignación de recursos escasos para lograr la satisfacción del cliente. Por tanto, es importante desarrollar metodologías que permitan alcanzar estos objetivos. El enfoque jerárquico para la planificación y control de la producción es una aproximación a este problema que se caracteriza por su capacidad de disminuir la complejidad y lograr buenas soluciones con economías de tiempo y requerimientos computacionales. En este artículo se presenta una propuesta jerárquica para resolver el problema de planificación y control de la producción en una configuración productiva del tipo job shop flexible. La solución propuesta, sin llegar a ser óptima dada la clasificación NP-hard del problema referido, es una buena solución tal como se demuestra en su validación.
A hierarchical planning system has been developed for American Olean Tile Co. (AO). AO (Lansdale, Pennsylvania) currently operates 8 factories across the US, producing a wide variety of ceramic tile products. The hierarchical planning system was developed to improve the integration of the annual plan, short-term scheduling, and inventory control. Successful implementation of the plan hinged on the interest and support of 2 distinct sets of individuals: 1. key production and distribution personnel at corporate headquarters, and 2. field personnel, such as plant managers. Since several system components were already in place, development costs to date have been 5 months of an analyst's time and time-sharing charges of about $10,000. The principal benefits are: 1. improved coordination and communication between manufacturing and marketing, 2. development of new sales forecasting procedures, and 3. reduced distribution costs of $400,000-$750,000 per year. The hierarchical production planning system offers a modeling approach that integrates decision making and communication across corporate- and plant-level organizations, enabling more consistent medium- and short-term production planning and scheduling.
The development and implementation of an integrated production and distribution planning system requires more than the optimal solution of a set of mathematical models. Success is achieved when the organisation addresses both the technical and human issues in planning. The approach followed by a process manufacturer over the last five years to integrate fully its production and distribution planning and scheduling system is described. Reduced distribution costs and improved co-ordination and communication are the principal benefits of this successful implementation. The major technical strategies adopted to co-ordinate the tactical and short-run planning and scheduling system are discussed, followed by the key organisational and human factors which play a vital role in the functioning of a successful system.
This paper discusses the issue of integrating various decision levels in hierarchical production planning systems. First the theory is briefly reviewed and then two case studies are presented. It is argued that it is not sufficient to have a good decision model at every level of the decision hierarchy. The different models should be carefully integrated. The potential problems resulting from a lack of integration are discussed. These problems are then illustrated in two case studies in order to be able to focus on actual managerial issues. It is shown how different decision levels supported by decision models were integrated in these two applications. Two important features are the crucial role of crossfunctional managerial committees in the integration process and the introduction of slack to avoid disaggregation problems.
We do not claim to be exhaustive in presenting the problems related to integration nor do we claim that the solutions to the cases are the best possible ones. We do, however, hope that this paper motivates production managers to take a serious look at their hierarchical planning procedures.
This paper discusses the issue of integrating various decision levels in hierarchical production planning systems. First the theory is briefly reviewed and then two case studies are presented. It is argued that it is not sufficient to have a good decision model at every level of the decision hierarchy. The different models should be carefully integrated. The potential problems resulting from a lack of integration are discussed. These problems are then illustrated in two case studies in order to be able to focus on actual managerial issues. It is shown how different decision levels supported by decision models were integrated in these two applications. Two important features are the crucial role of crossfunctional managerial committees in the integration process and the introduction of slack to avoid disaggregation problems.
We do not claim to be exhaustive in presenting the problems related to integration nor do we claim that the solutions to the cases are the best possible ones. We do, however, hope that this paper motivates production managers to take a serious look at their hierarchical planning procedures.
A real life case study of the design and implementation of a production planning system in an aluminium rolling mill is presented. A tailor-made computerized production planning system was set up for this company. On the aggregate level, a package was developed for simulating future machine loads. These simulations yield all necessary information for determining a production make span for each individual family of products so as to optimize overall machine capacity utilization. 0n the day to day operational level, another package was developed in order to determine the manufacturing schedule of specific orders on a daily basis. A specific dispatching algorithm was developed taking into account due dates of individual orders, production targets on finishing machines, and external factors. This article includes a description of the system and some comments on the actual implementation and possible future extensions.
The critical importance of integrating production, distribution, and inventory (PDI) operations has long been recognized by top management of many companies. Now, using the latest advances in Management Science modeling and solution technology, an integrated computer-based PDI system has saved approximately $18 million dollars during its first three years of implementation for a major national firm, Agrico Chemical Company. According to the Vice-President of Agrico Supply and Distribution, an additional $25 million savings is anticipated over the next two years.
Brought about by close cooperation between company officials and an outside staff of Management Science consultants, the PDI system has been used extensively to evaluate the benefit/cost impact of alternative capital investments in both short-term and long-term planning decisions. The development of the system underscores the value of recent Management Science innovations that have made it possible to analyze interacting influences too numerous and complex to be analyzed adequately only a few years ago.
Advanced network methodology incorporated into the PDI system required only one one-hundredth of the computer time and cost of methodologies previously used. The power and flexibility of the new Management Science tools have also brought about increased communication and understanding of key company operations. This increased communication and understanding stems from the inherent “pictorial” nature of network-based models, which facilitates interpretation of these models and policy recommendations based upon their solution.