Industrial Engineer's Role in Managing Human Capital	August 12                                       2012
Industrial engineers have long used engineering economy and related fields to measure and project various returns on financial capital. As the relative importance of human capital has increased, IE's need to expand their approaches to include more knowledge related to Human Performance Technology (HPT) and measures of merit that examine the return on both financial and human capital. HPT employs a design approach to achieving desired performance from people. HPT uses proven methods and techniques to solve costly people-performance issues caused by organizational or process breakdowns. This paper summarizes some of the key issues associated with human capital, outlines major human capital concepts IE's should be familiar with, and provides a mini-case example on how the use of human capital concepts in the practice of industrial engineering can make a difference.		Shivangi - 93 Institute of industrial Engineering 2004 Author-Coleman,garry D,stetar,Bill,Costa Joe

     

      Industrial Engineer's Role in Managing Human Capital 

   

    1.Introduction

  Industrial engineers have long used engineering economy and related fields to measure and project various        returns on financial capital. As the relative importance of human capital has increased, IE's need to expand their approaches to include more knowledge related to Human Performance Technology (HPT) and   measures of merit that examine the return on both financial and human capital. HPT employs a design approach to achieving desired performance from people. HPT uses proven methods and techniques to solve costly people-performance issues caused by organizational or process breakdowns. This paper summarizes some of the key issues associated with human capital, outlines major human capital concepts IE's should be familiar with, and provides a mini-case example on how the use of human capital concepts in the practice of industrial engineering can make a difference

2. The importance of human capital

While human capital has always been a necessary input for business enterprises, the shift to a knowledge-based economy has increased the relative importance of human capital (Boudreau and Ramstad, 1997). Measurement practices in capitalist organizations have traditionally focused on the measurement and evaluation of the use of capital and capital goods (e.g. materials, facilities, equipment). As the economy has transitioned from agrarian to industrial to information and service, the relative importance of the human element in creating value has increased. Or one might argue that the human element has always been important to creating value, and this value is simply being increasingly recognized.

Issues associated with hiring, placing, developing, and retaining an educated and skilled work force are more critical than ever to the long-term survival of for-profit and not-for-profit organizations alike. Business organizations have traditionally emphasized growing and protecting assets such as financial capital, treating labor as an operating expense to be controlled.

Few would argue that people are truly valuable assets. This is true at the societal, national, community, family, individual, and organizational levels.

3. Key issues associated with human capital

Our deficiencies in understanding human capital are significant. We lack a wide body of applied research, and we do not have norms available for comparison. Managing human capital is much more than the sum of recruitment, training, performance management, and compensation, the traditional processes of the human resource function. Measuring human capital is much more than assessing the effectiveness and efficiency of these key human resource processes. Recognizing human capital as an asset adds a new dimension of assessing total value and looking at the big picture. There are a number of key issues associated with human capital, some represent societal challenges that are prompting the increased attention on managing and measuring our human assets. Others are issues raised as we recognize our shortcomings in managing and measuring human capital. These issues include:

* The shift to a knowledge-based economy and the increasing need for highly educated knowledge workers.

* The global availability of capital goods and goods producing equipment has reduced the competitive advantage of traditional assets. Increasingly, the most sustainable competitive advantage is more productive human capital.

* An aging workforce in industrialized nations, leading to future worker shortages particularly among positions requiring college-level skills (Schaffner & Van Horn, 2003).

* Multiculturalism and increasing diversity in the work force. Recruitment, retention, development, and motivation practices will likely be more varied to be effective.

* The changing nature of the employee-employer relationship. Ubiquitous downsizing, reengineering, temping, and outsourcing have eliminated loyalty, as it was once known.

* Organizational strategy, performance measures, and traditional financial reporting and controls must be integrated with approaches from human performance technology, industrial engineering, quality management, information technology, and related change management disciplines to address human capital at the enterprise level.

* Continual change in the enterprise, its environment and technology prompts the need for ongoing retooling and retraining of the workforce.

* Improvements must be made in the development and deployment of practices to measure the return-on-investment in human capital. Better decision supporting tools are needed to prioritize alternative investments and identify which initiatives and drivers must be emphasized to produce business results.

4. Human performance technology                                                                 

Human Performance Technology (HPT) is a systematic multidisciplinary approach that "stresses rigorous analysis of present and desired levels of performance, identifies causes of performance gaps, offers a wide range of interventions, guides the change management process, and evaluates the results" (ISPI, n.d.). HPT employs a design approach with proven methods and techniques to solve costly people-performance problems . HPT models typically include a process (loosely following the PDSA cycle) and a list of potential interventions available to address the problems identified. Like the basic engineering process, HPT models start with analysis of the situation and design of potential solutions before deciding upon which interventions (techniques) to employ.

5. Industrial Engineer's (IE's) role?

One obvious place for IE's to contribute to the field of human performance technology and improve our approaches for managing and measuring human capital is performance measurement. For human capital, effective performance measurement needs to bridge the gap between individual performance measures and strategic performance measures. Effective measurement of human capital must support strategy implementation, as well as efficient operations. As high value creation opportunities shift from physical products to services and knowledge, IE's should transfer our traditional practices regarding measurement of physical products and processes to the information economy. Establishing cause and effect relationships among the many factors affecting human capital offers opportunities for modeling, simulation, and testing.

6.Surveys:

Information derived from the analysis was found to be actionable, based on sound Industrial Engineering principles (particularly those found within the Engineering Management specialty area dealing with motivation and leadership of technical people). Principles such as treating people like the true resources they are, providing rewards and recognition consistent with the organization's goals and objectives, providing feedback so personnel know where they stand, and providing them with the tools, time, knowledge, and training necessary to get the job done. The organization's leadership was provided with ten specific recommendations for interventions, taking these principles and the data collected into consideration. Observed implementation of the recommendations three years later was then recorded to verify their effectiveness.

7. Recent Developments

Our next steps are to sign-up charter members to THCI, execute the member agreements, and setup the data collection procedures to fit the needs of each member. As consortia level data, practices, and results become available, we will disseminate our findings in forums like the annual IIE conference to further the art and science of managing human capital.

8. Conclusions

Utilizing the various tools of Industrial Engineering, including structured problem solving and a scientific approach to work, combined with a focus on people, brings bottom line results. Theories and practices taught in Engineering Management specifically form a foundation around people that can provide an organization's leadership with a better perspective on which to build their structure and process for executing work. Industrial Engineers can therefore positively affect the organizational goals of increased motivation, morale, job satisfaction, and ultimately performance, by ensuring the Human Capital aspects of work are addressed.

Posted 12th August by Shivangi Goyal

Modelling the machine loading problem of FMSs and its solution using a tabu-search-based heuristic	August 13 2012
In this Paper, the FMS loading problem is discussed with the bi-criterion objective to minimize the system unbalance and throughput by the use of tabu-search heuristic. Manufacturing technology focuses primarily on flexibility and productivity. With the product variety and product life being the characterizing standards it is important that flexibility of the job shop is maintained as its efficiency is increased. in the presence of available machine time and tool slots as constraints The complexity of basic Machine Loading Problem in FMS is very high due to the different flexibility criteria as part selection, Operation allocation and various constraint involved on availability of tool slot and time on machine. In this paper, initially 2 heuristic  are used. In heuristic 1,sequece is generated using fixed part sequencing rule and then  in heuristic 2,Tabu search methodology is used to perturb the sequence obtained from previous heuristic The proposed methodology has been tested on ten problems representing three types of FMSs: small, medium and large		Shivangi-93 International Journal of  Computer Integrated manufacturing 2005 Author U. M. B. S. Sarma, Suman Kant, Rahul Rai & M. K Tiwari

Explaining Flexible manufacturing System.

      A flexible manufacturing system (FMS) is an integrated computer-controlled configuration which consists of numerical control (NC) machine tools, auxiliary production equipment and a material handling system (MHS).It is designed to simultaneously manufacture a low to medium volumes of a wide variety of high quality products at low cost.

objectives

The objectives of FMS mainly includes in achieving efficiency in a balanced automated high volume mass production and in low volume job-shop production .  It  developes a method of grouping part operations and tools on the principle of conjoint measurement and solved the machine loading problem by minimizing the maximum difference between machine utilization called system over utilization time. These features permit economic production of a large variety of parts in low volumes. . A supervisory computer is used to control the whole system.

Design problem

 There are two types of decision problems associated with FMSs: Design problems (selection of machines, robot layout decisions, AGVs, path selection etc) and Operational problems (part type selection, resource grouping, production ratio determination, allocation of resources and loading)

.

                                      Heuristic

It  refers to experience-based techniques for problem solving, learning, and discovery. Where an exhaustive search is impractical, heuristic methods are used to speed up the process of finding a satisfactory solution. Examples of this method include using a rule of thumb, an educated guess, an intuitive judgment, or common sense.

                               Tabu Search Heuristic

      It  is a local search method used for mathematical optimization.

Local searches take a potential solution to a problem and check its immediate neighbors (that is, solutions that are similar except for one or two minor details) in the hope of finding an improved solution

The tabu-search-based heuristic is regarded as a ‘higher level’ iterative improvement/procedure that is

used for solving various computationally complex problems. Many successful implementations of tabu

search, for obtaining optimal or near optimal solution of problems pertaining to process planning, scheduling, set partitioning and loading etc, It begins with an initial feasible solution and attempts to find a better solution by investigation among a large pool of neighbourhood solutions. This method is characterized by its inherent simplicity, high adaptability, a short term memory process via a tabu

list, to escape local optima and avoid cycling. The process also allows backtracking to previous solutions,

which may lead to a better solution, known as aspiration. These features of a tabu list and aspiration

make tabu search a powerful optimization tool for solving the machine loading problem

Problem Description

FMS in which six part types are to be processed on four machines, each having three tool slots and different processing times for every operation. Each part type consists of two operations, which can be performed, on any of the machines without altering the sequence of operations. The adaptability of each machine to perform many different operations allows several operation assignment possibilities generating alternative part routes.

Thus, there can be a fairly large number of combinations in which operations of the part type can be assigned on the different machines while satisfying all the technological and capacity constraints. Further consideration of flexibilities such as: tooling flexibilities, part movement flexibilities, etc., along with the constraints of the system configuration and operational feasibility make the problem even more complex.

These operation–machines allocation combinations are evaluated using two common performance measures: system unbalance and throughput. System unbalance is the sum of unutilized or over-utilized time on all machines available in the system. Maximization of machine utilization is identical to minimization of system unbalance, whereas _throughput_ refers to the units of part types produced.

    Problem Statement

Developmemt of Objective Function

Objective functions used here are the minimization of system unbalance and the maximization of throughput for the following reasons:

(1) Minimization of system idle time leads to higher machine utilization,

(2) One of the most important goals of any loading policy is enhancing total system output, which is the same as throughput,

(3)  throughput maximization by balancing the workloads on the machine often results in limiting the tardiness

Mathematically

The first objective function is to minimize the system unbalance equivalently, and to maximize

the system utilization. 

The second objective function is to maximize the throughput or, equivalently, to maximize the

system efficiency.

overall objective function is to maximize F

   Constraints

System Unbalance

System unbalance. The system unbalance deals with the ideal time remained on machines after allocation of all feasible part types: the constraint can be expressed as XM

Tool Slot Constraint

The tool slots constraint ensures that the number of slots required to perform the operations of the part types on a machine should always be less than or equal to the tool slots capacity of that machine.

The constraint can be expressed as

Solving Approaches to the Loading Problem.

Machine-loading problem can be addressed mainly by four approaches:

a) Heuristic oriented methods.

b) Optimization-based methods or Mathematical programming approaches.

c) Multi-criteria decision-making approaches.

d) Simulation based approaches.

a) Mathematical programming approaches

In this Paper, they  have applied Heuristic oriented methods

Initial feasible solution. The initial feasible solution is obtained from heuristic 1. In this research, the ‘shortest processing time’ (SPT) based part sequencing rule has initially been considered to resolve the part ordering/part input sequencing problems. SPT as a dispatching rule

for part types performs better on average for the loading problem of a random FMS as far as the objective of balancing the workload is concerned. The SPT as a part sequencing rule attempts to maximize the throughput in comparison to LIFO, FIFO, LPT etc. In light of the above facts, the initial part sequencing is

here considered according to the SPT sequencing rule.

 Generation of neighbourhood. Generation of neighbourhood is carried out by the following steps.

Step 1. For a given part sequence enlist the number of unassigned part types.

Step 2. Interchange the unassigned part types among the assigned types in the given sequence only.

This procedure is illustrated with an example.

Let us assume that S is a feasible part sequence, and

UA is a set of unassigned part types.

Consider a problem having seven jobs. Say, for a specific case, with a given number of operations and

other details, the part sequence and unassigned part types are

S={6,1,4,7,3,5,2}

UA={2,5}

Tabu list size. The size of tabu list assumed in this article is three. A lower value of tabu list size will

result in insufficient memory for matching of the current best candidate solution with those of previous

iterations. A higher tabu list size did not improve the quality of the result, hence the tabu list size of three. In the beginning, the tabu list is a null set, which means that the tabu list does not have any sequence.

Methodology of Tabu search heuristic

Replacing assigned Part with Unassigned Part

Tabu list = {f,f,f} 
S_b= S_C= S_O= {6,1,4,7,3,5,2}  F_b= F_C= F_O= 0.7250

Iterating in similar manner 4 times

Objective function value 0.7835

Finding from the study

The proposed tabu search-based heuristic solution methodology is characterized by it inherent simplicity and high adaptability and short-term memory process. While performing the computational experiments with the proposed tabu-search-based heuristic, the following parameters were chosen to solve :size of tabu list = 3, maximum iteration = 4 and perturbation policy, in which ‘each assigned part is replaced by an unassigned in one in a sequential manner’ is adopted to generate the neighborhood solution.

The proposed heuristic attempts to optimize an objective function, which is a combination of system unbalance and throughput   Where as  in  majority of earlier researchers treated system unbalance and throughput separately as their objective functions