International Journal of Engineering Trends and Technology- Volume4Issue2- 2013
Process Planning - new process plans for agile
manufacturing
Abdelouahhab Jabri, Abdellah El barkany, and Ahmed El Khalfi
Department of Mechanical Engineering
Sidi Mohammed Ben Abdellah University,
Route d’imouzzer, BP. 2202, Fès, Morocco
Abstract— The agile manufacturing system should be
simple, flexible, reconfigurable, reliable and responsive to
market changes. The objective of this paper is to develop a
new approach that aims to achieve this goal by adapting
manufacturing resources to customer requirements in
terms of lead time and reducing manufacturing costs.
Based on Design for Production (DFP) and Activity Based
Costing (ABC) methods, Process Planning for Agile
Manufacturing (PPAM) approach is used by process
planners to compare different process plans and select
which could respond to the objective of the company. DFP
method is employed in this approach to estimate
manufacturing cycle time and resource capacity, as for
ABC tool, it is used to estimate manufacturing cost based
on information generated by the former method. Optimal
batch size is firstly determined in this new approach and
used as input data to DFP method. A case study and a
software application program are presented to illustrate
this approach and support process planners successively.
Keywords— ABC, DFP, Lead-time, Manufacturing cost, Agility,
CAD/CAM system, Batch-sizing.
I. INTRODUCTION
Manufacturing systems should be able to produce a variety
of components at low cost and in a short time period. In
another hand, process planning is an activity for designers to
evaluate manufacturability and the manufacturing cost in the
early design stage for mechanical parts production. Since
major manufacturing costs of a product are committed in early
product development, it is critical to be able to assess
manufacturability and cost as early as possible in the design
process [1].
Agility has been defined, in terms of outcomes, as dynamic,
context specific, aggressively change embracing and growth
oriented succeeding winning profits, market share and
customers. In other words, agility is the ability of a business to
grow in a competitive market of continuous and unanticipated
change, to respond quickly to rapidly changing markets driven
by customer-based valuing of products and services. By
focusing on the output, Yusuf [2] asserted that an agile
organization can quickly satisfy customer orders; can
introduce new products frequently in a timely manner; and
can even get in and out of its strategic alliances speedily.
ISSN: 2231-5381
However, further insights into agility could be gained by
looking at the specific and operational issues. In response to
the challenge of agile manufacturing, this study focuses on:
Lower costs and short manufacturing lead times for
productions of a variety of components. Manufacturing lead
time is defined as the time interval from the starting time to
the completion time of the first product in production. Lead
time of manufacturing each product is reduced.
To date, many researchers have focused on process
planning and its impact on the manufacturing processes. In his
work, Lee [3] considered agile manufacturing in the early
design of components and manufacturing systems. A design
for agility rule is formulated. The design rule reduces
manufacturing lead times in consecutive changes of product
models. Hundal [4] discussed design rules aiming at the
reduction of production costs. Jianxin [5] proposes a
pragmatic approach to product costing involving two stages:
the preparatory stage and production stage, this approach uses
the ABC method to estimate manufacturing cost. Ciurana [6]
presented a model integrating process planning and
scheduling tool in metal removal processes. Alaa [7]
presented an approach of a quality/cost-based conceptual
process planning. The manufacturing cost was estimated by
taking into account the risk cost associated with the process
plan. Quality function deployment (QFD) method was used to
select the process alternatives by incorporating a capability
function for process elements.
Kusiak and Weihua [8] suggested rules that designers can
follow to reduce a product's manufacturing cycle time. These
rules attempt to simplify the production scheduling problems
that plague most production systems. For example, the rules
state that one should minimize the number of machines
needed to manufacture a product (which yields fewer moves
and less queue time) and allow the use of substitute
manufacturing processes (which gives the production system
the flexibility to route an order to another operation to avoid a
long queue at a bottleneck resource or unavailable machine.
Jeffrey [9] proposed a systematic approach based on the DFP
method to reduce manufacturing cycle time during the product
development stage. Feng [1] developed a prototype for the
preliminary assessment of manufacturability in the early
stages of product design.
The literature on agile manufacturing is abundant; however
it seems that very few works have considered process plans in
http://www.internationaljournalssrg.org
Page 92
International Journal of Engineering Trends and Technology- Volume4Issue2- 2013
the concept of agile manufacturing. This has motivated the
present work aiming to develop a new approach based on DFP
and ABC methods that aims to roughly estimate
manufacturing costs and help process planner to establish
process plans that meets customer requirements in terms of
lead-time.
As shown in Fig. 1, the role of the Process Planning for
Agile Manufacturing (PPAM) approach is to carry out an
evaluation of the resources selected during the conceptual
process planning.
These resources include machines involved in the
processing the part, cutting tools, fixtures, etc. Cutting
conditions are also among the input data of the PPAM method,
and then gives feedback about the performance of the
manufacturing system such resource utilization and
manufacturing cycle time and cost. These data are useful to
validate the manufacturing process and avoid any problem
related to the capacity of the workshops.
This paper is organized as follows: the following subparagraphs present an overview DFP and ABC methods. The
second section presents the PPAM approach. A prototype
system is presented in section 3. A case study of the
application of the proposed approach is presented in section 4.
Finally Section 5 concludes this paper.
A. Activity Based Costing (ABC)
ABC assumes that cost objects (e.g., products) create the
need for activities, and activities create the need for resources.
Accordingly, ABC uses a two-stage procedure to assign
resource costs to cost objects [10]. In the first stage, costs of
resources are allocated to activities to form Activity Cost
Pools.
Tolerance
standards
Product
Design
CAD
Engineering
requirements
techniques
Selected
resources
Preliminary
Process
planning
Raw
Material
Resource
availability
Process
cost
Tools &
Fixtures
These activities are allocated in the second stage to cost
objects based on these object’s use of the different activities.
In order to differentiate between the different allocations at
the two stages, the first-stage allocation bases are termed
‘‘resource cost drivers’’ and the second-stage bases ‘‘activity
cost drivers’’ [11] - [12] - [13]. Fig. 2 illustrates the concept of
the ABC method.
Consume
Consume
Resources
Activities
Resource
drivers
Activity
drivers
Fig. 2. The concept of ABC
B. Design For Production (DFP)
DFP refers to methods that determine if a manufacturing
system has sufficient capacity to achieve the desired
throughput and methods that estimate the manufacturing cycle
time of a new product. DFP can also suggest improvements
that decrease capacity requirements (which can increase the
maximum possible output), reduce the manufacturing cycle
time, or otherwise simplify production.
These methods require information about a product’s
design, process plans of existing products, and production
quantity along with information about the manufacturing
system that will manufacture the product [14]-[15].
The manufacturing system is characterized by the
machines performance like the mean time to failure (mfj) and
mean time to repair a machine (mr j). The products are
characterized by the job size (number of parts) and the desired
throughput (D i) (number of parts per hour of factory
operation).
Time
methods
Standard
process
alternatives
Manufacturing
cycle time
Resource
utilization
Process Planning for Agile
Manufacturing
Manufacturing
resources
database
Product
Cost
methods
Manufacturing
cost
Batch size
Detailed Process
Planning
Process plans
Fig. 1.The role of PPAM
ISSN: 2231-5381
http://www.internationaljournalssrg.org
Page 93
International Journal of Engineering Trends and Technology- Volume4Issue2- 2013
The sequence of machines that each job must visit; the
mean setup time (per job) at each machine (sij) and its
variance (cs ij); the mean processing time (per part) at each
machine (tij) and its variance(ctij); the yield at each machine
that a job must visit (yij) (the ratio of good parts produced to
parts that undergo processing).
The squared coefficient of variation (SCV) of a random
variable equals its variance divided by the square of its mean.
Other notations used are as follow:
I
set of all products
Ri
sequence of machines that product i must visit
Rij
subsequence that precedes machine j
Yij cumulative yield of product i through Rij
Yij cumulative yield of product i through Ri
xi
release rate of product i (jobs per hour)
Aj
availability of a machine j
Vj
set of products that visit machine j
cri
SCV of batch inter-arrival time of part type i
t+ij
total process time of product i at machine j
c+ij SCV of the total process time
t+j
aggregate process time at machine j
c+j
SCV of the aggregate process time
t* j
modified aggregate process time at machine j
c*j
SCV of the modified aggregate process time
The cumulative yield is the product of the yields at each
machine that the product visits is calculated using the
following equations.
(1)
(7)
2) Processing time aggregation
The mean processing time (per part), the mean setup time
(per batch), batch size, desired throughput and machine
availability are used at this stage to calculate the aggregated
processing time.
 Batch processing time
The mean batch process time is the sum of the mean batch
setup time and the mean total processing time. The mean total
processing time is the mean single-part processing time
multiplied by the mean number of parts in the arrived batch,
equation (8).
(8)
Assuming the batch setup time and single-part process
time are independent, the variance of batch processing time is
the sum of the variance of setup time and total processing time.
The variance of total processing time is contributed by the
variance in single-part processing time and the variance in the
arrived batch-size, equation (9).
(9)
 Aggregation
The aggregate process time of jobs at machine j is the
weighted average of all the jobs that visit machine j. Each
product is weighted by its release rate, equation (10).
Equation (11) calculates the mean of the square aggregate
process time, which can be used to determine the SCV (c+j).
(10)
(2)
1) Arrival aggregation
The batch arrival rate of a part type is its demand divided
by the average batch-size arriving at the first machine, and
adjusted by the overall yield rate to fulfil the demand,
equation (3).
(11)
The aggregated batch arrival rate at the machine j is the
sum of the batch arrival rates of all part types is calculated
with equation (4).
 Downtime adjustment
Equation (10) gives the SCV of aggregated processing
time at the machine without considering machine
unavailability. However, due to the machine
failures or
(3)
downtime (e.g., scheduled maintenance), the actual processing
time will take longer thus needs to be adjusted. The
percentage of time that a machine is available is Aj, equation
(12).
(4)
(12)
(5)
The adjusted mean aggregated time and SCV of
aggregated time become:
Assuming the mean batch arrival rates for all part types are
of the same order of magnitude, the SCV of aggregated interarrival time at the first machine can be approximated by the
weighted average of the SCV of batch inter-arrival time of all
part types, equation (6) [14].
(6)
ISSN: 2231-5381
(13)
(14)
3) Flow variability propagation and cycle time calculation
http://www.internationaljournalssrg.org
Page 94
International Journal of Engineering Trends and Technology- Volume4Issue2- 2013
At this step, three factors are determined, which are:
Machine utilization, cycle time at station j multiple.
It can be seen that the queuing time is composed of three
factors: variability, utilization, and processing time. The mean
system cycle time is the sum of the machine cycle time:
 Machine utilization
The average utilization rate uj at a machine j is the
percentage of time that it is busy. It is calculated by the
following equation.
(18)
(19)
(15)
The variability of inter-departure time at each machine is
propagated from the variability of inter-arrival and processing
time. It can be approximated by the following equation.
II. PPAM APPROACH
The proposed PPAM approach is a decision support tool
for the process planner which aims at estimating
manufacturing cycle time and cost. It is based on the methods
previously presented ABC and DFP. As shown in Fig. 3, the
steps for PPAM process are:
(16)
(17)
 Generation and simulation of tool paths;
 Approximation for cycle time calculation
With all the information about (caj), (c*j), xi and (t*j)
through the manufacturing system, they can be used calculate
the cycle time at each machine.
The first term on the right hand side of equation (18) is the
approximated queuing time.
 Determination of optimal batch size;
 Manufacturing cycle time estimating;
 Manufacturing cost estimating.
Process plans of
existing products
Product design
CAM Systems
Generation and simulation of
tool paths
Machining
operations
times
Process Planning for Agile
Manufacturing
Lead time
Consideration
Optimal batch size
determination
Optimal batch
size routine
Optimal
batch size
Manufacturing cycle time
estimating
Manufacturing
Cycle-time
Manufacturing cost estimating
Resource utilization
DFP method
Cost
consideration
ABC method
Manufacturing
process cost
Detailed process planning
Fig. 3. The process of AMPP
ISSN: 2231-5381
http://www.internationaljournalssrg.org
Page 95
International Journal of Engineering Trends and Technology- Volume4Issue2- 2013
A. Generation and simulation of tool paths
The first step of this approach uses the CAM systems such
as CATIA V5, SolidWorks, TopSolid or ProEng, to generate
and simulate machining tool paths. The process planner have
to take into account at this stage the customer requirements
like dimensional and geometrical requirements and roughness,
creates a sequence operations to carry out in the same machine,
selects cutting tools and cutting conditions and establishes
cutting speed and feed rate values. At this stage all machines,
fixtures and tools involved in the manufacturing process are
gathered and each operation is finally provided and serves as
input data for the following steps.
B. Determination of optimal batch-size
The number of units produced per order (the lot size) is an
important managerial decision, which affects both the lead
times and the throughput [16]. In our approach we adopt the
model of the queuing behaviour of a multi-item multi-machine
job shop developed by Karmarkar [16]. This model is based
on the node decomposition technique for approximate analysis
of open queuing networks. Each node is described by a
simpler queuing model with a single stream of arrivals and the
characteristics of the departing stream are deduced from this
model.
The arrival stream at each node is the superposition of
departure streams from the other nodes in the network that
feed it. It is assumed (heuristically) that the arrival process of
batches at machines is Markovian. Since processing times at
machines vary by item, a general service time distribution is
assumed and machines (nodes) are modelled as M/G/1 queues
with first come first served (FCFS) service discipline, [17][18]. The mean time spent in queue (Wj) at machine j is given
in equation (22).
(22)
Where, E [t2j] is the second moment of processing time
at machine j.
(23)
The mean time spent at machine j by batches of item i (Tij)
is the sum of the second moment of processing time and the
mean time spent in queue at machine j, equation (24).
The objective of this step is to evaluate the capacity of
workshops and estimate manufacturing cycle time using DFP
method based on the results of previous steps. DFP method
takes into account data generated by previous steps, namely:
processing times, optimal batch size. Consider a flow line
manufacturing system consisting of n machines (1< j < n)
which manufacture m types of parts and all these parts go
through every machine in the system without skipping.
Part type i (1< i < m) has a desired throughput (Di), and
arrives in batches randomly with predetermined batch-size
(Bi), and a SCV for its batch arrival (caj). Each batch of part i
is processed on one machine j with mean setup time, (sij),
mean single-part process time, (tij), and mean yield rate yij.
The average cycle time for a batch of any part type
spending in machine j, equation (18), is TTj. The total
manufacturing cycle time is estimated by equation (19), and
the resource utilization using equation (15) taking into account
the availability and the yield of each machine.
D. manufacturing cost estimating
The total manufacturing cost is estimated using the ABC
method. The manufacturing process is broken down into
activities based on a decomposition of Feng and Song [1],
[19]. The total manufacturing cost is the sum of activity costs,
equation (26).
(25)
(26)
Cma is the cost of manufacturing activities; N is the total
number of activities involved in the manufacture of the part.
Cimachining is the machining cost of activity i, it is calculated by
equation (27).
The first term of this equation is the cost related to
machines involved in the manufacturing process, as for the
second term it is the cost related to the tools performing each
operation, the formula is as follows:
(27)
Where:
(28)
(24)
The lot-sizing problem is formulated as Optimization
problem, where the objective is to minimize the inventory
costs. Let hii be the holding costs for units of item i waiting or
being processed at the machine and let hif, be the holding cost
in finished goods. The expected total cost function is
formulated as follows.
(p)
(i)
(ii)
uj(B) < 1 , j=1, …, n
Bi>1 , i=1, …, m.
C. Manufacturing cycle time estimating
ISSN: 2231-5381
Tk1 and Tk0 are the cutting time and tool life time
respectively of the tool k. Ck1 and Ck2 are the cost per hour
related to cutting labour and tool labour respectively of the
tool k, [6].
Ciload_unload is the load and unload activity. Cisetup is the
setup cost of activity i. Cihandling the handling cost of activity i.
Handling is a batch-level activity.
Ciprogramming_testing the programming and testing cost of
activity i. Programming-testing is a product-level activity.
Cioverhead the overhead cost of activity i. It is a facility-level
activity.
http://www.internationaljournalssrg.org
Page 96
International Journal of Engineering Trends and Technology- Volume4Issue2- 2013
III. A PROTOTYPE SYSTEM
Based on the methodology described above, a prototype
has been developed to support the process of estimating
manufacturing cycle time and costs, resource utilization with
optimal batch size.
The machining time of each operation, cutting tools involved
in this operation are imported from the CAMWorks module of
SolidWorks. Indeed, this information which is stored in XML
files is automatically extracted and displayed on the
corresponding tables. The user introduces the different
information about the manufacturing system like the desired
throughput (units/day) of item i to be manufactured in the
workshop, holding costs, etc.
The Solver Module of Microsoft Excel is then called to
determine the optimal lot-size of items, and then resource
utilization manufacturing cycle time and costs are estimated.
Fig. 4 presents the structure of this prototype.
IV. CASE STUDY
To illustrate the PPAM approach we present in this section
an example of a machined part to be manufactured on a CNC
machines, Fig. 5. The raw material of this part is low alloy
steel preformed bar 110x110mm² and cut into 35mm.
This part will be manufactured in a work shop which
manufactures another product.
The objective of this section is to illustrate with this
example the methodology followed to perform an analysis of
the conceptual process planning. Two processes are evaluated
using the PPAM approach.
Fig. 5. Drawing part
A. Generation and simulation of tool paths
The machining of this part has been simulated with the
CamWorks module of SolidWorks. For the two Alternative
Groups AG1 and AG2, two machine centres are selected to
perform all operations of a part machining: NC milling
machine and NC lathe centre. Operations to be performed and
fixtures are the same for these two Alternative Groups.
However, Cutting Conditions, and cutting tools are not the
same, therefore processing times are not the same. Table 1
summarizes the different machining operations on these two
machines selected to manufacture the part.
Generate XML files
Product design
CAD/CAM
Operations &
Machining-time
(XML files)
Generate and
simulate tool paths
SolidWorks
CamWorks
Extract data
MS Excel Solver
Newton method
Determinate
optimal batch-size
Extracted
data
Manufacturing cycle
time estimating
Manufacturing cost
Estimating
Fig. 4. A structure of the ABC-DFP prototype
TABLE 1
Conceptual process planning
Alternative Group 1
Alternative Group 2
ISSN: 2231-5381
CNC Machines
Processing time (min)
Setup time (min/lot)
Lathe centre
Milling machine centre
Lathe centre
Milling machine centre
7.50
6
8.57
6.66
30
20
30
20
http://www.internationaljournalssrg.org
Page 97
International Journal of Engineering Trends and Technology- Volume4Issue2- 2013
We suppose that the setup time for this part is 30 minutes
on the lathe centre and 20 minutes on the milling machine
centre. Fig. 6 is the “products” screen sheet, which contains:
Processing time table, Setup-processing time table, products
table and process plans table.
The processing time are extracted from XML files and
displayed in the processing time table. The setup-processing
time table contains the processing rate (parts / day), the setup
time and the SCV of setup time and processing time which are
equal to 1.The desired throughput for the product to be
manufactured is 24parts/day. Information related to the
products to be manufactured is gathered in the same layer.
B. Optimal batch size
The second step of this method is to determine the optimal
batch size that minimizes the holding costs of batches to be
manufactured. Processing times determined in the previous
step are used at this stage. Assuming that the holding cost for
units of items i waiting or being processed at machine j is 0.2
€ and the holding cost in finished goods is 0.1 €. In order to
find an optimal solution, the optimization problem was solved
using Solver, a built-in optimization routine of Microsoft
Excel, it is called from the application layer. Optimal batch
size of the two items found is: B1 =13 and B2 =21 for AG1 and
B1 =15 and B2 =25 for AG2.
C. Manufacturing cycle time estimating
Manufacturing cycle time of each product is estimated with
DFP method.
Optimal batch size of the two products and processing
times determined in the previous step are used to estimate
manufacturing cycle time at this stage of PPAM approach. In
the next, we present an example of calculation for the first
alternative group AG1. Cycle time estimating of AG2 is
similar to AG1.
D. Manufacturing cycle time estimating
Manufacturing cycle time of each product is estimated with
DFP method. Optimal batch size of the two products and
processing times determined in the previous step are used to
estimate manufacturing cycle time at this stage of PPAM
approach. In the next, we present an example of calculation
for the first alternative group AG1. Cycle time estimating of
AG2 is similar to AG1.
 Arrival Aggregation
Release rate is firstly calculated. Desired throughput of the
items is 24 (unit/day) for the first item and 40 (unit/day) for
the second one.
Using equation (3), for AG1, the release rate is 1.85
(batch/day) and 1.9 (batch/day) of the first and the second
product, respectively.
The SCV of aggregated inter-arrival time at the first
machine is determined using equation (6). We need to
determine the SCV of inter-arrival times for each product (cr i)
using equation (7).
Fig. 6. Processing time and aggregate process time.
ISSN: 2231-5381
http://www.internationaljournalssrg.org
Page 98
International Journal of Engineering Trends and Technology- Volume4Issue2- 2013
For the AG1:
 Processing time aggregation
The batch processing time is the sum of the mean batch
setup time and the mean total processing time (t+ij). For the
AG1. the mean batch processing time of the first product (i=1)
at the lathe centre (j=1) and the milling machine centre (j=2)
are:
Hours
For the AG2:
Resource utilization table in the manufacturing system
sheet, fig. 7, contains the values u1 and u2 of the two
alternative groups, and the chart is automatically updated.
The SCV of inter-arrival times at the second machine is:
Hours
The mean of the batch processing time for each item on
each machine are then obtained. Since the part processing
times on each machine are exponentially distributed. SCV (ctij)
and (csij) are equal to 1. The SCV of total processing time on
each machine is calculated using equation (8).
Manufacturing cycle time is approximated by equation (18).
For the lathe centre:
Hours
-
And manufacturing cycle time at the milling machine is:
Hours
-
Since all machines are perfectly reliable (Aj = 1), c*j = c+j
and t*j = t+j.
The adjusted aggregate process times of jobs on the lathe
centre and the milling machine are calculated as follows:
Finally the multiple for the two machines and the time
spent in the queue are calculated using equation (20) and (22).
-
-
Hours
Hours
And the SCV of the adjusted aggregate process times are
calculated using equation (14).
The adjusted aggregate process times are gathered in the
process plans table in fig. 6.
 Flow variability propagation and cycle time
Machine utilization is calculated using equation (15). The
aggregate process time calculated previously is key
information to estimate machine utilization, for the two
machines:
Hours
Hours
E. Manufacturing cost estimating
In the final step of this approach, manufacturing cost is
estimated using the ABC method, the activities involved in the
manufacturing process are firstly identified. These activities
are: programming and testing, machining, load/unload, setup,
handling, inspection and Material.
The machining cost is calculated using equation (27). The
previously estimated processing time by DFP method is
incorporated in equation (27) used to estimate manufacturing
cost on each machine. Fig. 8 is cost estimating screen sheet
which contains tooling cost and activity tables.
The first table summarizes costs related to the cutting tools
involved in the manufacturing process; it provides data needed
to estimate tooling cost using equation (28). We assume that
the hourly cost of cutting tool is 1.3 € / h and the tool life time
is 3 hours and the tool cost is equal to 1.5€.
Machining cost is estimated with equation (27) and it
equals to 9.49€. Finally, manufacturing cost is the sum of
activities costs Cma=15.16€. For the AG2 manufacturing cost
is 16.03€.
Fig. 7. Performance measure.
ISSN: 2231-5381
http://www.internationaljournalssrg.org
Page 99
International Journal of Engineering Trends and Technology- Volume4Issue2- 2013
Called Process Planning for Agile Manufacturing (PPAM),
This approach presents efficiently reduced machining and
setup costs early in the product development stage.
Fig. 8. Tool cost and total manufacturing cost.
F. Process alternative selection
Finally, results of the previous steps are gathered in a
selection table, manufacturing cycle time, resource utilization,
time spent in queue, tool cost and manufacturing cost are the
evaluation criteria to be assessed at this stage. The purpose of
this stage is to select the best alternative which compromises
multiple evaluation criteria is carried out. In the literature,
there are a lot of methods of multiple evaluation criteria Saaty
[20], Yannou [21], Blanc [22]. In our approach we use the
method used bay Almannai [23] and Allaa [7].
The output data of the precedent steps are gathered in the
decision table shown in Fig. 9.
The process planner is now able to, to prioritize the
evaluation criteria, and to score the process alternatives
against them. He can evaluate the alternative options in 1–10
range and then calculate the final score in order to identify the
most suitable alternative which replies to multiple objectives.
This score is not a subjective quantification but it depends on
the numerical data results from the PPAM approach. As
shown in Fig. 7, the final score of process alternative groups
give the advantage to AG1 (52.9%), and rank AG2 in the
second place (47.1%).
It includes an optimization routine which is responsible of
searching the optimal batch-size in order to minimize the
number of setups and therefore reduce manufacturing cost.
DFP method is used to estimate manufacturing cycle time and
helps the process planner to determine how manufacturing a
new product affects the performance of the manufacturing
system. Information generated by this method is then used to
estimate manufacturing cost with the ABC method.
A computer programme is developed which serves as an
effective tool to estimate manufacturing cycle-time and cost.
To illustrate this approach an example of a manufacturing cell
with tow machines (a Lathe Centre and a Milling Machine
Centre) and tow items is presented. Information generated by
ABC and DFP methods are gathered in a selection table and
helps the process planner to select the most suitable combined
alternatives towards cycle time reducing and cost minimizing.
Further works are required to develop a new tool including
quality method in order to evaluate the process quality and
give useful information about the manufacturing system.
REFERENCES
Fig. 9. Process alternatives selection
V. CONCLUSION
This paper presents a new approach to develop a decision
support in which the DFP and ABC methods are incorporated.
ISSN: 2231-5381
[1] Feng, S. and Zhang, Y. “Conceptual Process Planning: a definition and
functional decomposition”, Manufacturing Science and Engineering,
Proceedings of the 1999 international mechanical engineering congress
and exposition. 10-1999, p. 97-106.
[2] Yusuf, Y., Sarhadi, M., & Gunasekaran, A. Agile manufacturing: The
drivers, concepts and attributes International Journal of Production
Research Economics, vol. 62, pp. 33–43, 1999.
[3] G. H. LEE, Designs of components and manufacturing systems for agile
manufacturing, International Journal of Production Research, vol. 36, pp.
1023-1044, 1998.
[4] Hundal, M. S. Rules and models for low-cost design. In Design for
Manufacturability, P. J. Guichelaar (ed.) (New York: ASME), vol. 52, pp.
75- 84, 1993.
http://www.internationaljournalssrg.org
Page 100
International Journal of Engineering Trends and Technology- Volume4Issue2- 2013
[5] Jixian, J. and Mitchell, M. A pragmatic approach to product costing
based on standard time estimation, International Journal of Operations &
Production Management, vol.17, pp. 738–755, 1999
[6] Ciurana, and Garcia-Romeu, M. L. A model for integrating process
planning and production planning and control in machining processes ,
Robotics and Computer-Integrated Manufacturing, vol.24, pp. 532–544,
2008.
[7] Alaa, H. and Ali, S. Conceptual process planning: an improvement
approach using QFD, FMEA, and ABC methods, Robotics and
Computer-Integrated Manufacturing, vol.26, pp. 392–401, 2010.
[8] Kusiak, A. and Weihua H. Design of components for schedulability,
European Journal of Operational Research, 76 (1994) 49-59.
[9] Jeffrey, W. and Mandar, C., Reducing Manufacturing Cycle time during
Product Design, Journal of Manufacturing Systems, vol. 20, pp. 416-428,
2001.
[10] Cooper, R. and Kaplan, R. How cost accounting distorts product costs,
Management Accounting, vol. 69 (1988) 20-27
[11] Turney, P Common cents: the ABC performance break-through, how to
succeed with activity-based costing. Cost technology, Hillsboro, 1991.
[12] Tsai W-H. A technical note on using work sampling to estimate the
effort on activities under activity-based costing, International Journal of
Production Economics, vol. 43, pp. 6–11, 1996.
[13] C.S. Park and G.T. Kim, An economic evaluation model for advanced
manufacturing systems using activity-based costing, Journal of
Manufacturing Systems, vol. 14, pp. 451-439, 1995.
[14] Jeffrey, W. Design for manufacturing: a tool for reducing manufacturing
cycle time, Proceedings of the 2000 DETC ASME Design Engineering
Technical Conference, 10-13 September, Baltimore, Maryland, USA.
2000.
[15] Minis, I., J. W. Herrmann, G. Lam and E. Lin. A Generative Approach
for Concurrent Manufacturability Evaluation and Subcontractor Selection,
Journal of Manufacturing Systems vol. 18, pp. 383-395, 1999
[16] Karmarkar S., Kekre S., Lot sizing in Multi-Item Multi-Machine Job
Shops, IIE Transactions, vol. 17, pp. 290-298, 1985.
[17] Kleinrock, L., Queueing Systems, Vol. I: Theory. Wiley, NewYork, 1975.
[18] Narayan, U. An Introduction to Queuing Theory Modelling and Analysis
in Applications, Birkhauser, City. 2008.
[19] Tornberg K, Jämsen M, Paranko J. Activity-based costing and process
modeling for cost-conscious product design: A case study in a
manufacturing company, International Journal of Production Economics,
vol. 79, pp. 75–82, 2002.
[20] Saaty TL. In: The Analytic Hierarchy Process. New York: McGraw-Hill;
1980.
[21] Yannou B, Hajsalem S, Limayem F. Comparison between the SPEC
method and Value Analysis for an aid in preliminary design of products,
Mecanique & Industries, vol. 3, pp. 189–99, 2002.
[22] Blanc S, Ducq Y, Vallespir B. Evolution management towards
interoperable supply chains using performance measurement. Computers
in Industry, vol. 58, pp. 720–32, 2007.
[23] Almannai B, Greenough R, Kay J. A decision support tool based on QFD
and FMEA for the selection of manufacturing automation technologies.
Robotics and Computer-Integrated Manufacturing vol. 24, pp. 501–507,
2008.
ISSN: 2231-5381
http://www.internationaljournalssrg.org
Page 101