After reading the article, you will compose an article critique. Your critique will be at least two

  

After reading the article, you will compose an article critique. Your
critique will be at least two pages in length, and in the critique, you will
include the following: Key points: Identify three significant/key points from the article.
Summary: Write a section summarizing the article. Do not simply use
information from the article. The summary must be in your own words. You
must display your thoughts, opinion, and analysis. Analysis: Identify how the article aligns with and relates to concepts
learned in this unit. In this section, be sure to: Discuss the staffing planning process as well as the
workforce planning process and how it impacts future business
activities. Explain steps taken for an organization to forecast its
workforce supply and demand. Contrast internal and external forecasting decisions. Personal Evaluation: What do you find to be valid or invalid in
the article? Do you agree with the author’s assertion(s)? Explain why or
why not. Your article critique must be at least two pages in length. At a minimum,
you must use your chosen article as a reference, but other resources may be
used if needed. Be sure to cite and reference any sources used in APA format.
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International Journal of Industrial Engineering, 22(5), 631-644, 2015
UNISON DECISION ANALYSIS FRAMEWORK FOR WORKFORCE
PLANNING FOR SEMICONDUCTOR FABS AND AN EMPIRICAL STUDY
Yun-Hsuan Lin, Chen-Fu Chien*, Chih-Min Yu
Department of Industrial Engineering and Engineering Management,
National Tsing Hua University, Hsinchu 30013, Taiwan
*
Corresponding author’s e-mail: cfchien@mx.nthu.edu.tw
With the increase in the scale of semiconductor manufacturing, the number of knowledge-workers has also increased
tremendously. The cost of automation and manpower is increasing annually, and engineers and technical operators are
playing an increasingly crucial role in factories. The optimal workforce plan for manufacturing and the improvement of
productivity have become key topics. In semiconductor manufacturing, numerous factors affect the workforce plan for
manufacturing. The problem of determining the actual manpower demand, given the different preference structures of various
decision makers, is difficult to solve. This study employed the UNISON decision analysis framework for constructing a
workforce planning decision model for semiconductor manufacturing. We also held discussions with domain experts to
identify key performance indices for human capital management. An empirical study was conducted in a semiconductor
company, and the results showed that the proposed framework could assist the company in developing an operation
workforce planning model and an associated management mechanism for improving the decision quality and decision
rationality. Thus, the company could enhance human capital and productivity to maintain corporate competitiveness.
Keywords: human capital, decision analysis, UNISON decision analysis framework, semiconductor, workforce planning
(Received on March 12, 2015; Accepted on June 17, 2015)
1. INTRODUCTION
Driven by Moore’s Law (Moore, 1965) that the number of transistors fabricated in the same size area will be doubled every 12
to 24 months to provide more capability at equal or less cost, the semiconductor industry has strived for continuous
technology migration via capital investments and cost reduction to maintain competiveness. With continuous technology
migrations, the semiconductor industry is capital and knowledge intensive. Semiconductor industry is one of the most
complicated industries in which productivity enhancement, yield enhancement, continual cost reduction, fast ramp-up,
on-time delivery, and cycle time reduction are the important ways for operational excellence (Chien and Wu, 2003;
Leachman et al., 2007; Wu and Chien, 2008; Wu, 2013). As the range of applications of semiconductor components has
increased, the life cycle of products has become shorter. Knowledge workers including engineers and technical staff are
increasingly important assets of modern semiconductor companies to maintain competitive advantages, since they are
operated with highly automated and intelligent manufacturing facilities. Most studies on human productivity enhancement
focused on increasing the throughput and overall equipment effectiveness (Chien and Hsu, 2006; Chien et al., 2007). Little
research has been conducted on enhancing workforce planning and staff productivity.
This study aims to construct a workforce planning decision model and the associated management mechanism for
reasonable workforce planning and people productivity planning for increasing operational efficiency and enhancing the
competitiveness of companies. This research focused on the manufacturing manpower by considering direct labor (DL) and
indirect labor (IDL). The results have shown practical viability of the proposed approach. Indeed, the proposed approach is
implemented in real settings.
The remaining of this paper is organized as follows: Section 2 reviews related studies to construct theoretical
fundamental. Section 3 introduces the proposed framework for workforce planning based on the UNISON decision
framework. Section 4 describes an empirical study conducted in a leading semiconductor manufacturing company in Taiwan
for validation. Section 5 concludes this study with discussions of contributions and future research directions.
2. LITERATURE REVIEW
Workforce planning involves matching the supply of and demand for employees from a strategic level to an operational level.
Strategic workforce planning involves the determination of the workforce size over a long period of time (Koutsopoulos et al.,
1987). Huselid (1995) supported predictions indicating that the impact of high-performance work practices on a firm’s
ISSN 1943-670X
INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING
Lin et al.
UNISON Framework for Workforce Planning
performance is partially contingent on the employer-employee interrelationship and the link between these practices and
competitive strategy being limited.
A number of studies have addressed the task assignment and the re-planning problem (Fischetti et al., 1992; Dowling et
al., 1997; Ernst et al., 2004; Moz and Pato, 2007; Abdelghany et al., 2008). In particular, Stolletz (2010) focused on the
operational planning task of scheduling fortnightly tours. The study observed that restrictions were highly flexible for shift
building and combining shifts into tours, i.e., preparing a valid duty roster. The flexibility in terms of the length and positions
of the working periods facilitated to the generation of workforce schedules covering the dynamic demand closely. Chien and
Chen (2007, 2008) developed a data mining framework based on decision tree and association rules for generating useful
rules for personnel selection. Their result provided decision rules relating personnel information to work performance and
personnel retention. These authors conducted an empirical study in a semiconductor company for supporting the company’s
hiring decision for IDL, which included engineers and managers with different job functions.
Chen and Chien (2011) developed a manufacturing intelligence framework in which the rough set theory, support vector
machine, and a decision tree were integrated. Their purpose was to extract useful patterns and intelligence from a large
amount of human resource data and production data for enhancing the quality of decisions made by human resource
departments, including the identification of high-potential talents fitting the company culture and allocating jobs such that the
functional nature of the jobs matches the characteristics of the talents. Semiconductor companies, as well as other
high-technology companies, often suffer from high turnover rates and encounter difficulties in recruiting appropriate talents
because of poor matching of jobs and talents. Furthermore, the work behavior and value propositions of knowledge workers
are changing, thereby affecting personnel selection and recruitment practices (Robertson and Smith, 2001; Hough and
Oswald, 2000). Kuo et al. (2011) proposed a manufacturing intelligence approach to analyze semiconductor manufacturing
big data to derive patterns to reduce cycle time.
The actual requirements of the workforce, especially the indirect workforce, for fabs may be indeterminate due to the
lack of clearly defined workforce-output relationship. Chien et al. (2010) proposed a non-parametric frontier approach for
estimating the requirement for indirect workforce in semiconductor fabs based on the best past performance adjusted to
reflect the expected productivity growth. Furthermore, they proposed a reallocation approach that can provide an explicit
decision support mechanism for balancing workloads according to various production environments, thereby providing an
equitable basis for performance evaluation to foster constructive competition among fabs. Chien et al. (2014) developed an
operator-machine assignment approach to assign the optimal number of machines to the operators to minimize machine
interference time and labor cost that was validated with an empirical study in semiconductor testing facility. In addition, Hsu
(2014) proposed an approach integrating data envelopment analysis and neural network for evaluating the performance of
wafer fabrication operations. Existing personnel selection approaches based on static job characteristics no longer suffice
(Lievens et al., 2002).Therefore, selecting the right talents capable of demonstrating the strongest performance and staying
with the company long term is imperative for every high-technology company.
Figure 1. UNISON framework for workforce planning
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UNISON Framework for Workforce Planning
3. RESEARCH FRAMEWORK
On the basis of the UNISON decision framework (Chien, 2005), we proposed a framework for workforce planning that
includes six phases: (1) understand and define the problem, (2) identify the niche for decision quality improvement, (3)
structure the objective hierarchy, generate alternatives, and clarify influence relationships among uncertain events, (4)
identify and describe expected outcomes, (5) make overall judgments and value assessments, and (6) reach a decision through
trade-off by using decision analysis, as illustrated in Figure 1. Indeed, the UNISON decision framework has been validated in
various decision problems including the evaluation of alternative strategies of IC final testing under risk (Chien et al., 2007),
semiconductor assembly outsourcing vendor selection and order allocation (Wu and Chien, 2008), determining the inspection
frequency for wafer bumping process (Chien et al., 2008), and modeling and reduction of overlay errors for wafer fabrication
(Chien and Hsu, 2011).
3.1 Problem definition
The proposed framework starts with problem structuring and definition. To understand and define the present problem, a
decision analyst must inquire thoroughly by asking numerous questions to understand the problem. The decision analyst
should make a checklist enumerating the decision elements such as customers, competitors, the industry, available resources,
execution cost, planning horizon, the decision maker, the stakeholders affected by the decision, objectives, attributes,
uncertain events, consequences, strategies, alternatives, and the expected outcomes. Table 1 shows a decision element
checklist for reviewing and analyzing the decision elements via decomposing a large and complex decision problem into
small and basic elements systematically.
Category
Context
Constraint
Decision
makers
Table 1. Decision element checklist for workforce planning
Decision Element
External and internal customers
Cost of the decision marking
Deadline of the decision marking
Timing of the decision marking
Semiconductor operations and productions related units
Semiconductor industry to increase knowledge worker
and factory automation level continuously upgrade
Expenditure budget for workforce planning
The fourth quarter of each year
Non-economic slowdown or forced increased demand
Stakeholders
Engineers and operators
Business needs
Decision makers
influence
Evaluation criteria
Domain
Decision Element Description
Objectives
Attributes
Uncertain events
Consequences
Associated strategies for
implementation
Chief executive officer, vice president of operation and
vice president of human
Factory chief and director of operations
Revenue earned by each engineer or wafer output
contribution of each engineer
The industry’s first people productivity
People productivity
Demand boom, output and investment in machines
Workforce planning outcomes
Taking into account the contents of the reasonableness
of factory work and enhance people productivity and
competitiveness with industry giant
3.2 Strategy formulation and objective
The niche for problem-solving and decision-quality improvement is identified to formulate the strategy. Clemen (1996)
classified the objective into four categories: strategic objectives that denote the ultimate objectives of the decision maker,
fundamental objectives that the decision maker will strive to achieve, means objectives that can help achieve fundamental
objectives, and generic objectives that the decision makers consider in similar problems. The strategic objectives are
structured for identifying the fundamental objectives and construct the objective hierarchy.
Indeed, a strategy comprises a series of decisions for handling the potential niches to resolve the problems. These
interrelated decisions help achieve the ultimate strategic objectives. Three major decision elements must be defined in the
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Lin et al.
UNISON Framework for Workforce Planning
domain of the decision problem: objective, plan, and uncertain factors. The objective represents the direction of efforts. The
plan indicates a method by which to achieve the ultimate objectives. The non-determining factor affects the level and
expected result of the plan. These decisions help determine the niche and define the decision objective. The research involves
defining the decision objective by conducting an interview with the decision maker and holding discussions with a focus
group. Based on literature review (Leachman and Hodges, 1996; Leachman et al., 2007), the fundamental objectives of
semiconductor manufacturing fabs productivity and the associated indices are defined in Table 2.
Table 2. Fundamental objectives of semiconductor productivity
Semiconductor
productivity index
Net profit of
each labor
Financial index
Revenue of each
labor
Labor
productivity
Output index
Direct-labor
productivity
Indirect-labor
productivity
Definition
Objectives
Profit earned by each labor
Profit maximization of each labor
Revenue earned by each labor
Each labor working day can produce photo
layer in manufacturing production index
1. Each direct-labor working day can
produce photo layer in manufacturing
production index
2. Each direct-labor each working hour can
complete photo layer in manufacturing
production index
Each indirect-labor working day can
produce photo layer in manufacturing
production index
Revenue maximization of each
labor
Photo layer in manufacturing
production index maximization
of each labor
3.3 Alternatives generation and objective hierarchy
Thirdly, the objectives are structured to generate alternatives through value-focused thinking, and influence relationships
among uncertain factors are clarified. This study used a strategy generation table (Howard, 1988). This table can be used to
generate a plan that is derived from a constituent objective of the solution. A nominal group technique (Delbecq et al., 1986)
was also used. In this technique, a possible plan is finalized by conducting more than one meeting with experts, and
subsequently, strategic planning and program designing are performed.
To construct the hierarchy of the objectives, the decision analyst may ask questions such as “What do you mean by that?”
or “How could you achieve this?” when moving downward in the hierarchy or away from the fundamental objectives, and
“Why is that important?” when moving up the hierarchy or toward fundamental objectives (Clemen, 1996). The means
objectives can help the decision maker achieve the fundamental objectives.
Average
Selling Price
Wafer
Output
Demand
Forecast
Operating
Productivity
Production
Required
Planning
Revenue
Competitive
value of the
contribution
Figure 2. Influence diagrams of workforce planning
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Lin et al.
UNISON Framework for Workforce Planning
After the construction of the objective, the relationship between the uncertain factors and factors is checked using an
influence diagram. The uncertain factors are affected by the outside environment, which includes new applications such as
system on chip or radio frequency identification, and new technology such as nanotechnology. Changes in the uncertain
factors will bring new business opportunities and increase market demand, which will affect investment in the production of
machinery and equipment because the estimation of human resource partly involves the number of machines. Moreover, the
economic situation and the needs of emerging industries, such as the rise of the thin film transistor–liquid crystal display
industry, will affect labor turnover rates in semiconductor factories. All these factors should be considered when a planner
estimates human resource requirements to prevent manpower shortage.
Being aware of and understanding the uncertainty is useful for formulating a backup solution and reducing the negative
impact. An influence diagram was constructed to formulate the relationship between uncertain events and their impacts
involved in complex workforce planning, as shown in Figure 2.
3.4 Expected outcomes
Fourthly, the corresponding attributes for evaluating each alternative for achieving the fundamental objectives are specified,
the expected outcomes of alternative strategies, and the possible states of uncertain events are described and accessed. The
value of a workforce plan depends on business performance, and the decision maker must consider and describe the expected
results objectively. Therefore, it is necessary to let the decision maker project future situations to estimate the expected
outcomes.
Structured interviews were conducted with experts such as factory directors and brainstorming team sessions were held
according to three layers (Figure 3): input, process, and output. The set of objectives should be complete, measurable,
decomposable, non-redundant, and minimal to ensure the validity of the objective identification (Keeney and Raiffa, 1993).
Figure 3. Fundamental objective hierarchy and means objective network for a semiconductor factory
The decision maker can check a workforce plan via the created “checklist” to present the objective description of the
expected results under different decision contexts to access the overall situation before making subjective judgments. Table 1
listed a decision table with expected outcomes.
Once the resources of the workforce planning model have been established, the next step is to conduct an annual human
performance assessment. Therefore, it is necessary to identify the main key performance indices (KPIs) and supporting
management mechanism. Appropriate assessment indices are required to validate the proposed approach and enhance the
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Lin et al.
UNISON Framework for Workforce Planning
productivity of factory staff through benchmarking. They can help deal with a lack of or unevenly distributed human
resources in a factory promptly and can increase workforce planning flexibility for creating a management mechanism.
3.5 Value assessment
Fifth, on the basis of judgments and value assessments, the preference structure and risk attitude of the decision maker can be
accessed. For example, if workforce planning for every property is performed on a relatively small scale, the multi-property
evaluation model can be used to evaluate the value in every alternative.
3.6 Creation of management mechanism
Finally, using the UNISON decision analysis framework and decision model, the decision maker can select the optimal
workforce plan. The plan-do-check-action (PDCA) cycle is used in the basic procedure for total quality management (TQM).
The PDCA cycle represents the sequence of planning, implementing the plan, checking the implementation, and processing
the results. It is a process in which the quality improves in each phase. The process is repeated several times, and finally, the
overall quality is improved progressively.
The PDCA cycle provides a conceptual framework for continuous and progressive improvement that has been
extensively used for total quality management.
A workforce planning management is created to obtain uncertainty information and identify information gaps. This
research follows the PDCA cycle, which consists of the following four parts: plan, do, check, and action. Figure 4 shows a
circular mechanism for improving the decision quality.
Plan: This step involves creating a human resource decisi …
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