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Asset Optimization as a Technique to Reduce Production Cost

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Today manufacturing is more than an industry. It is a global engine of productivity and growth. Yet, like almost every other industry in today's struggling economy, manufacturers are under a great deal of pressure from customers and competitors, as well as partners and suppliers, to increase their capabilities in terms of faster speed to market, customization and addressing emerging business opportunities. That's on top of continually searching for new ways of cutting costs in every aspect of their business operations.For today's manufacturing companies what matters more is that how efficiently their company can compete globally with others as an organization followed by meeting the day today requirements of the customer and exchange of hassle free information while not focusing only on sales of the company [1].The prospect of having to somehow " do more with less " can be discouraging, but this need not be the case. In this paper the implementation of asset optimization technique in manufacturing is discussed
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Asset Optimization as a Technique to Reduce Production Cost
Pulok Ranjan Mohanta, Jigarkumar D. Patel, Jayesh Bhuva, Misal Gandhi
Department of Mechanical Engineering
Laxmi Institute of Technology, Sarigam Gujarat, India
Abstract
Today manufacturing is more than an industry. It is a global engine of productivity and growth.
Yet, like almost every other industry in today’s struggling economy, manufacturers are under a
great deal of pressure from customers and competitors, as well as partners and suppliers, to
increase their capabilities in terms of faster speed to market, customization and addressing
emerging business opportunities. That’s on top of continually searching for new ways of cutting
costs in every aspect of their business operations.For today’s manufacturing companies what
matters more isthat how efficiently their company can compete globallywith others as an
organization followed by meeting the day today requirements of the customer and exchange of
hassle free information while not focusing only on sales of the company [1].The prospect of
having to somehow “do more with less” can bediscouraging, but this need not be the case. In this
paper the implementation of asset optimization technique in manufacturing is discussed
Index TermsProductivity, Asset management, optimization, production
I. Introduction
Asset Optimization is the process of improving the deployment of assets to achieve improved
performance and lower costs of operations with a system based approach. It makes all the
equipment as perfect, as operational and as effective as possible. Asset Optimization is a system of
organizing and applying assets from personnel to machinery, bringing knowledge and technology
together to achieve the greatest return on investment [2].
II. Asset Utilization:
Most manufacturing facilities today are employing a large quantity of assets. A process for
quantifying opportunities for improvement is asset utilization (AU). This process quantifies the
improvement opportunities through root cause analysis of time and material of equipment across an
operation [4]. The different asset utilization parameters are described as under:
1. % ()=  

2. % ()= 

3. % ()=
∗
4. % ()=

5. AU= A * RTE * RSE * Y
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Availability determines the percentage of time the asset is available to run whereas down time
represents the time spent on the scheduled and unscheduled maintenance, no operation and idle
times due to unavailability of the orders. No operation category includes the situation raised due to
problems beyond its control like material flow, lack of supply, substandard material for operation
etc. Run time efficiency examines the percentage of cycle time that is spent actually running
product versus setting up for other products. Here the time spent in changeovers and transitioning is
taken into account. Run speed efficiency is determined by comparing the actual production to the
ideal production that could have been achieved at maximum speed or standard rate. While assessing
yield, the quantity of quality product and the quantity of substandard product are taken into
consideration [5]. The fig. 1 shows the AU parameters and their representation to calendar time.
AU process is applied to individual equipment across a manufacturing operation. This process helps
in identifying the areas where in improvements can be made to reduce the cost of production [4].
Today in manufacturing Sector Companies generally thrive for supplying products at competitive
prices which reflect their overall cost of production. In order to remain competitive or to have larger
market share the cost of production should be as lower as possible. Minimum cost of production can
be achieved by utilizing the fixed assets to the maximum and reduce wastage or improper use to the
minimum level. Thus the asset optimization is gaining popularity in the manufacturing industry
Figure 1. Representation of AU parameters on calendar time.
III. Asset Optimization benefits:
A well-executed asset optimization strategy can reduce unnecessary maintenance and downtime,
track causes of failures, identify “repeat” offenders, provide root cause data and fault diagnosis and
recommend actions. It also detects failure conditions in advance, eliminates manual actions,
handoffs, and paperwork and reduces latent time between problem identification and resolution.
The primary benefits of an asset optimization strategy are that it increases asset availability and
performance, and that it maximizes operations and maintenance effectiveness.
Many think increasing productivity means building more factories. But capitalizing on Overall
Equipment Efficiency (OEE) may be equivalent to setting up of a new factory [2]. To maximize
OEE it becomes necessary to shift from traditional maintenance activity to a proactive process. The
figure below demonstrates the different maintenance practices. While implementing AO the goal, of
course, is to shift from traditional reactive activities to a proactive approach. A business supported
dynamic blend will generate the best result.
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AO
IV. A 5-phase approach for achieving AO:
For achieving asset optimization there may be numerous was. A step by step 5-phase approach is
described here. This approach support high reliability, reduced maintenance costs, and continuous
improvement in a sustainable program. This approach is applicable to optimizing of the existing
programs and developing new programs, whether at existing or new facilities [3].
Figure 2: A5-phase approach to achieve Asset Optimization.
Phase I: Laying the Ground Work:
This phaseof the process lays the groundwork for improvements by preparing the plan for “smart”
improvements. It begins with a site assessment to identify the current situation versus desired
performance, and then the strategy to mitigate the gaps. It is at this stage that all components of the
plant asset optimization program are evaluated and obstacles toward program improvement are
identified. The gap analysis results in a detailed process improvement plan that lays out the
necessary actions for progress of the program. Phase I activities also deal with strategy development
and the identification and control of programmatic and cultural issues. Effecting positive culture
change is one of the most important ingredients required for success and this is generally
overlooked. A comprehensive action plan so developed must be clearly communicated throughout
the organization. Ensuring the entire plant staff understands project intent and the role each
individual will play is extremely vital to project success and sustained performance.
Phase II: Building of Foundation:
In this phase based on the quality of the data, the practice fundamentals are established. Here begins
the transition to a more controlled process, fortifying CMMS and technical information,
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incorporating training on program policies and work management procedures, and setting the table
with tools for Phase III activities. Phase II includes systematic screening of all assets to determine
relative criticality to safety, environment, operations, product quality, and maintenance costs these
criticality rankings will be usedin Phase III to direct development of optimum PM, PdM, and spare
parts strategies. Asset optimization program performance reporting is also set up in Phase II. This
involves the roll out of actions to meet the performance reporting requirements set out in the
policies developed in Phase I.
Figure 3. Transition from reactive maintenance to pro-active maintenance.
Phase III: Setting Frame Work:
In this phase the execution of the core program begins and changes in daily work routines are
carried out. PM and PdM routines are developed and implemented in the CMMS. Critical issues
that are often root cause of failures are identified, and the requirements to address them are
thoroughly documented and utilized during this process. It is also at this point that the maintenance
program will begin transition from reactive maintenance towards proactive maintenance in a
controlled manner as shown by the steps in fig. 3. Here the “Fix It Now” or FIN team strategy is
implemented to assist with this difficult transition. Initially a large portion of the maintenance team
deals with daily work requests or “Fix It Now”items, allowing the balance of the maintenance team
to begin executing preventive and predictive tasks that will drive improved reliability. As the
program matures, the proportion of personnel assigned to the FIN team will eventually be reduced
to about 20% of the total maintenance team, while the remaining 80% of the maintenance team will
be devoted to ongoing proactive maintenance functions. Phase III is also where actual program
reporting begins that will enable ongoing measurement and tracking of overall project impact. The
Table-1 represents how the individual equipment utilization can be optimized by focusing on the
root causes:
HIGHER EFFICIENCY
LOWER EFFICIENCY
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Phase IV: Enclosing Structure:
In this phase the proactive elements are added to support continuous improvement of the program.
The program by now has acquired all the necessary components to support higher level of reliability
initiatives such as Machine Improvement Strategies, Technology Improvements, Reliability
Centered Maintenance (RCM), Root Cause Analysis (RCA), and Spare Parts Optimization. Other
reliability methodologies such as Six Sigma may also be valuable depending on the requirements of
the facility. Some of the initiatives listed in Phase IV may be implemented earlier in the process
depending on individual facility needs and resources available to support the initiatives. For
example, a formal RCA methodology and program may need to be implemented early at a new
facility so that issues associated with initial plant startup can be effectively analyzed and worked to
successful resolutions. Ongoing maintenance training programs are also developed in Phase IV as
part of continuing development of craft skills.
Phase V: Enhancing the Structure.
In the final phase, the program is raised from “great” to World-class. The programmatic
enhancements that occur in Phase V focus more on financial benefits than on reliability
improvements. It is at this stage that energy consumption can be reviewed and areas of
inefficiencies identified and corrected. Also, developing a capital projects prioritization process
provides a structured methodology for comparing costs and benefits of two competing capital
projects. The outcome of such a comparison is selection of the capital project that provides the
highest rate of return to the facility over time. Asset replacement strategies should also be developed
and implemented in Phase V to address aging and obsolescence issues as the plant continues to
operate.
V. Conclusion
Asset Optimizationis the systematic process that enables the dream of Operations Excellence. It
emphasizes a logical approach to best practices through the phases of the program. The primary
benefits of an asset optimization strategy are that it increases asset availability and performance,
and that it maximizes operations and maintenance effectiveness. Functional excellence will never
be enough to be the best. Lead functions are the glue that brings all the pieces together in an
optimized set of systems, especially through the mechanism of the Managing System and Strategic
Plan. Finally, organization can only be as successful as its workers’ endorsement and participation
in these functional excellence practices. They must enable their people to bring them the success
that they desire. An organization can become the best if it starts its journey with the right model.
VI. References
[1] White paper “Making sense of E-Manufacturing: A Road map formanufacturers Industry”
Rockwell Automation.
[2] “The Guide to Asset Efficiency Optimization for Improved Profitability”. SKF
Reliability.Systems.
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[3] GE-ORBIT[Vol.25 No.1, 2005]
[4] Kerkhoff J, Thomas W. E., Utterback J., “A Systematic Strategy for optimizing Manufacturing
Operations”, Production and Operation Management, Vol. 7, No 1.
[5] Stewart D. F., “Asset Utilization a Competitive Weapon” Alcoa Worldwide manufacturing
conference, 1991
[6] Ierapetritou, M., Zukuim, L. 2009. Modeling and Managing Uncertainty in ProcessPlanning and
Scheduling. In: Optimization and Logistics Challenges in theEnterprise 30, 97–144.
[7] Lai, K., Leung, F., Tao, B., Wang, S., 2000. Practices of preventive maintenance andreplacement
for engines: A case study. European Journal of OperationalResearch 124 (2), 294–306
[8] I. Yeoman, A. Ingold, Yield Management Strategies forthe Service Industries, Cassel, London,
ResearchGate has not been able to resolve any citations for this publication.
Article
Full-text available
This paper reports on the application of the sequential method, presented in a previous paper (K.K. Lai, K.N.F. Leung, B. Tao, S.Y. Wang, Journal of the Operational Research Society, submitted), to determine optimal policies of when to carry out a preventive maintenance action for an engine and when to replace an engine in use. The sequential method was run with the real data of engines provided by the Kowloon Motor Bus Company Limited in Hong Kong. First, four functional forms for an engine’s lifetime distribution were proposed, the values of the respective distribution parameters estimated, and the goodness of fit of the distributions assessed. Secondly, the values of the corrective maintenance indicator were estimated and some values assigned to the preventive maintenance indicator. Thirdly, the other input values for the method were assessed. Finally, three optimal maintenance and replacement policies with respect to three different criteria were determined, respectively. This case study shows that the sequential method can be used to solve a maintenance and replacement problem efficiently.
Article
A manufacturing optimization strategy is developed and demonstrated, which combines an asset utilization model and a process optimization framework with multivariate statistical analysis in a systematic manner to focus and drive process improvement activities. Although this manufacturing strategy is broadly applicable, the approach is discussed with respect to a polymer sheet manufacturing operation. The asset utilization (AU) model demonstrates that efficient equipment utilization can be monitored quantitatively and improvement opportunities identified so that the greatest benefit to the operation can be obtained. The process optimization framework, comprised of three parallel activities and a designed experiment, establishes the process-product relationship. The overall strategy of predictive model development provided from the parallel activities comprising the optimization framework is to synthesize a model based on existing data, both qualitative and quantitative, using canonical discriminant analysis, to identify main effect variables affecting the principal efficiency constraints identified using AU, operator knowledge and order-of-magni-tude calculations are then employed to refine this model using designed experiments, where appropriate, to facilitate the development of a quantitative, proactive optimization strategy for eliminating the constraints. Most importantly, this overall strategy plays a significant role in demonstrating, and facilitating employee acceptance, that the manufacturing operation has evolved from an experienced-based process to one based on quantifiable science.
Chapter
Uncertainty appears in all the different levels of the industry from the detailed process description to multisite manufacturing. The successful utilization of process models relies heavily on the ability to handle system variability. Thus modeling and managing uncertainty in process planning and scheduling has received a lot of attention in the open literature in recent years from chemical engineering and operations research communities. The purpose of this chapter is to review the main methodologies that have been developed to address the problem of uncertainty in production planning and scheduling as well as to identify the main challenges in this area. The uncertainties in process operations are first analyzed, and the different mathematical approaches that exist to describe process uncertainties are classified. Based on the different descriptions for the uncertainties, alternative planning and scheduling approaches and relevant optimization models are reviewed and discussed. Further research challenges in the field of planning and scheduling under uncertainty are identified and some new ideas are discussed.
Asset Utilization a Competitive Weapon
  • D F Stewart
Stewart D. F., "Asset Utilization a Competitive Weapon" Alcoa Worldwide manufacturing conference, 1991
Yield Management Strategies forthe Service Industries
  • I Yeoman
  • A Ingold
I. Yeoman, A. Ingold, Yield Management Strategies forthe Service Industries, Cassel, London,
Modeling and Managing Uncertainty in ProcessPlanning and Scheduling. In: Optimization and Logistics Challenges in theEnterprise 30
  • M Ierapetritou
  • L Zukuim
Ierapetritou, M., Zukuim, L. 2009. Modeling and Managing Uncertainty in ProcessPlanning and Scheduling. In: Optimization and Logistics Challenges in theEnterprise 30, 97-144.