E L S E V I E R Journal of Materials Processing Technology 61 (1996) 113-119

Journal ol

Materials Processing Technology

0PPP): A Hybrid System for Integrating Process and Production Planning in Batch Part Manufacturing

Aziz E. EISayed, M.H. Elwany

Production Engineering Department, Faculty o f Engineering Alexandria University, Alexandria 21544, Egypt

A b s ~ a ~

Integration of planning functions is a key issue of the current manufacturing systems advances. Process and production planning are two multi-task activities of particular importance in batch manufacturing environment. This paper presents the structure and functional relationships within (PPP), which is a prototype system for integrating process planning to production planning tasks. Three basic system integrating tools have been implemented to build (PPP): the IDEF0 approach, a relational database platform, as well as linear and non-linear analytical models. The framework of each of the (PPP) building blocks is discussed. The (PPP) system model section is emphasized and verified using numerical data.

Keywords : Manufacturing decision support systems - Process Planning - Production Planning - CIMS

1. Introduction

Manufacturing planning decisions are made on different phases in the product development cycle, such as: process planning, scheduling, and aggregate production planning. Integrating these decisions, so as to perform planningtasks concurrently, is a challenging and stimulating research area [1]. Integration will result in shorter lead-times, more efficient manufacturing systems, and a high competitive advantage [2,3,4]. In part batch manufacturing, where production-to-order is the dominant climate, process planning is a main subject phase to meet design specifications and ensure quick response to customer orders. Because it is a multi-task module, process planning is also a very attractive topic to link various production decision making functions [5]. Many of the reported process planning systems have considered the issue of integration implicitly, and thus they offer adequate interfacing capability with the design stage through defining parts geometrical features[6,7,g]. Other computer-aided process planning (CAPP) systems provide effective linkage with manufacturing real-mode through generating ChIC programs [9]. In several cases, process planning prototypes aim at satisfying horizontal integration with design and manufacturing through clustering different process planning activities [10]. However; little work has been devoted, so far, to integrate process planning tasks with the production planning domain, which is denoted by what we call vertYcal integration. Some research work have attempted to include machine selection alternatives into the process planning stage to satisfy certain scheduling criteria [11]. Consislamt with this concept, a novel process planning multi-level architecture has been proposed to link design and production control functions [12]. A universal manufacturing interface is suggested in [13], which enables data exchange between several production tasks via computer-

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aided process information systems. An integrated model for process planning and job-shop scheduling functions has been described by means of distributed approaches in [14]. Similar models have been mentioned to integrate: process planning to shop floor scheduling [15], production planningto scheduling [16], and job-shop planningto scheduling [17], using different resource decomposition procedures under various criteria.

In this work, we concentrate on building a system, which has been tentatively named (PPP), to integrate process planning tasks to production planning functions in batch manufacturing. In terms of objectives, (PPP) considers the output of a process planning system to be the assignment of manufacturing resources on the shop-floor level to produce a given part under an economic objective criteria (e.g. minimum production cost, minimum lead time,...etc)[18]. On the other side, the objective of the production planning functions is to determine the levels of production resources (e.g. machine capacities, labor, production quantities,...) which mininlize production cost, set- up cost, inventory cost, or other cost parameter(s), over some planning horizon within some demand pattern [19]. Accordingly, the decision variables, planning objectives, and manufacturing resources are shared between the two phases namely; process and production planning. Such an assumption yields a good fotmdation for integrating the previous activities in a single multi-objective planning system.

An interfacing strategy could be easily sketched through building production relational data base platform [20], to handle process planning information flow simultaneously with production planning activities. Another integrating paradigm is obtained by assuming planning activities within the manufacturing system to be performed using a hierarchical structure with some coupling (integrating) constraints to

1 1 4 A.E. Elsayecl, M.H. Elwany/dournal of Materials Processing Technology 61 (1996)113 119

link the different planning levels. Together with the hierarchical structure, analytical models are constructed to represent the input-output relationships between the production and process planning for each part before it is fed to the shop floor.

The next sections of this paper will cover: a description of the (PPP) system architecture, functional analysis of the (PPP) system modules, building system analytical models, and system models verification with numerical data.

User interventions are necessary during different levels of the system execution to relate the part process to the batch production data. As in similar applications, (PPP) is built with the capability to have a compatible data structure with various file format to ensure data transfer between the planning levels, and to accommodate various forms of inputs. The system presently includes the generation of part batch aggregate plans, and the corresponding preliminary part process plans.

2. (PPP) System Architecture: 3. (PPP) System Functional Analysis

As illustrated in figure (1), (PPP) is a hierarchical planning system, supported with relational data bases, to integrate the two functions: process planning and production planning. The proposed system incorporates three levels of production plans: aggregate plans for part batch's, master production schedules for end items, and bill of materials for part components. Two levels of process plans are elaborated: component detailed process plans, and family process plans. The system assumes that in a part batch manufacturing environment, production planning decisions for end items are located in a higher level of the hierarchy, and they are presented by their master production schedules. The bill of materials, which describes the relationship between the end item and its components sub- assemblies is fotmd in the second lower level. Such a decomposition scheme is combined with obtaining detailed process plans for the part components, where different process planning modules are elaborated at that level to get the optimum machine/tool combmation to produce each manufacturing feature of the components.

Since it is difficult to consider every detail associated with production over a long planning horizon, the end items are grouped into families of similar items and aggregate production plans (medium term plans) are generated at the highest level of the system. The aggregation procedure groups the resources related to the manufacturing system such as, machines into machining centers (cells), labor force into labor pools,...etc. The corresponding process plans for the families of items are deduced, including less planning decisions, as compared to the component process plans.

m

E Fig.1. The (PPP) System Architecture

An IDEF0 (integrated definition) system identification technique is used to specify the functional relationships between the different system components, phases and modules [21].The reason behind adopting such a methodology is its capability to apply a top-down approach to identify the modules where integration between process and production planning tasks would take place.

Figure (2) shows the IDEF0 basic block for the (PPP) system at the highest level of the hierarchy (node A0), where the system inputs are: the part designs, the product order and part demand forecasts. The system controls are: the production resources (labor, machines,...) and the process resources (tools, process type . . . . ). The mechanisms are: the system models and the integrators (mainly model constraints). (PPP) outputs are: part batch production plans (Aggregate plans) and process plans.

Production Process

Part Orders PPP Plans

Process Demand AO Plans

system Integrators Models (Constraints)

Fig. 2. The (PPP) System, the IDEF0 basic diagram : node A0

The basic node A0 of the (PPP) system is decomposed into four functional nodes (A1,A2,A3,and A4 ), where the description of each nodal function together with its inputs, outputs, controls and mechanisms are detailed in figure (3).

~ 0 ~ = I , I Restx.rcas I Master I ' Agffeg~e i Produ~on I F~oce~

"l il ill F o r e ~ Fan'fly

I 2y~ ~rdirrin~y

~ ° ~,~ / I I 1 ~t ors De~led

Fig. 3. (PPP) system, the IDEF0 decomposition of node A0

A.E. Elsayed, ~ H. Elwany/Journal of Materials Processing Technology 61 (1996)113 119 115

Since the subject matter of this study is to integrate process to production planning tasks, further breakdown of the system nodes related to those specific areas will be performed. The analysis of node A2 (performing aggregate plan) into its sub- functiunal modules is given in figure (4), while the decomposition of node A3 (perform a preliminary process plan) is illustrated in figure (5).

C21 Total 1 c ~ Part ~ ~oQk~don ~ Part

Prod.c~n . I quN i q

t l - - r ~ l I I i . ~ I M3

Constraint I Pr~ m -

I n ' ~ r y Pamn 'z te~

~ ., l.nbor

[ Produc'don

Fig. 4. The (PPP) system, the IDEF0 decomposition of node A2

I~oo=~ / I. s~,oaie - I - ~ ~ co~m',d D m . . ~ t t l omB~.- ~ 11..

Llal -Ill MadllnOs =r Totid and Tools | Co~ and 8olec'llon M33 lane Cl4~da Proce~ A3.4

Vadal01e~ Model M34

Cost

I

j .

/

' -L-

% % &'nm~ t Mo¢~ Hans

List of

Proc¢~

"~me

Fig. 5. The (PPP) system, the IDEF0 decomposition of node A3

The analysis shows that the previous structural procedure is divided into a number of modules which are integrated by analytical models. A traditional integer/linear programming model is used for the aggregate production planning module. The model treats the problem as one of minimizing total cost of production subject to constraints for: (customer demand, labor availability, inventory levels . . . . ). The main advantage of the pervious model is its relative simplicity, and its capability to constitute a manufacturing cost, set-up cost, and other process- oriented cost element which tend to disaggregate the problem to lower levels, thus interfacing with process planning is made possible. In this connection, the model with that level of detail, will need information from shop-floor data.

Such data is transferred backward from process planning to the aggregate planning levels.

4. System Analytical Models

Typical production planning tasks, phases and functions have been analyzed using the IDEF0 technique. With such decomposition approach, R has been shown that production and process planning decisions are presented by the functional nodes A2, and A3 respectively, at different levels of the system hierarchy. In general, the objective of the production planning phase is to determine the level of resources required to manufacture a specific product configuration over a given planning horizon. Thus, the planning procedure is concerned with matching the quantity and timing of a product outputs with the production system capacity, inventory levels, labor force, and other input resources.

On the other hand, the determination of the process variables (operatmg conditions ,e.g. cutting speed, feed, depth of cut,...etc) to manufacture a specific part and the estimation of production costs and production time, constitute basic modules at the process planning levels. Consequently, the problem is formulated using the part production cost as an objective function in terms of the operating conditions, and a set of constraints dictated from the manufacturing systems. Integrating the two planning levels will be performed using an analytical modeling strategy which comprises two distinct models.

4.1. Model building strategy

A dual stage sequential strategy is used to deal with the problem at both the production planning and the process planning level. Assuming an aggregate planning environment, the total production cost is formulated as a first model [22,23]. The production cost element in the previous model is disaggregated into more detailed cost elements so as to form a second model at the process planning decision level. A bottom- up solution procedure is proposed at this stage to solve the two models, i.e. model (2) will be solved first.

4.2. Assumptions and initial conditions

• For a given part batch, the demand is deterministic and it is known all over the planning horizon

• Parts are produced on the shop-floor level through performmg a consecutive number ofjobs, where a job is characterized by an operation, a machine, and a cutting tool.

• Process planning variables are: the cutting speed and feed for every operation.

• Tools, machines selection, and sequence determination criteria are not included in this study.

Model l The problem is tackled by assuming an aggregate planning

model (AP) for the production planning phase. The inputs to the aggregate planning paradigm includes: ) Product demand forecasts • Production facility resources upper and lower bounds ) Production capacities The outputs from the model are:

116 A.E. Elsayed, M.H. Elwany/Journal of Materials Processing Technology 61 (1996)113-119

s Quantity and time of different part families to be produced.

• Quantity and time of different part families to be held as inventory.

• Regular labor force requirement. • Overtime labor requirement. The following notations are used: Subscripts: Part batch (i) = 1, ..., N ( Total number of parts) and, Time (t)

= 1,..., T (Planning horizon) Decision variables: For a part product and time combination (i,t) : Pit = Quantity of part product, (parts); Qit = Quantity of product to be held in inventory, (parts); Rt = Regular labor required, (man-hours);and Qt = Required

over-time labor, (man-hours). Model Parameters: Air=Unit part production cost, (S/part); Bit=Unit part inventory carrying cost, (S/part); Ct--Labor rate, (S/man- hours) Dt=Over-time labor rate, (S/man-hours); Hi=labor, (man- hours/part); Rm~,=Maximum available number of regular labor, (man-hours); Omax ffi Maximum available number of

overtime labor ,(man-hours); dit = Part demand forecast, (parts). Having the above conditions, an (AP) model can be easily sketched to take the following form :

M~nimiTe N T T

Z=Y~ ~ (AitPit + Bit Qit) + Y~ (Ct Rt + Dt Ot) (1) i=lt=l i=l

Subject to:

Pit + Qi t -1- Qit = dit vi , t (2) N ~HiPit - R t - O t = 0 v i ( 3 ) i=l

0_< Rt - ( R m J t v t (4)

0-- Ot ~(Omax) t Vt (5)

Pi t ,Qi t -> 0 ViA (6)

As stated in equation (1), the objective of the model is to minimi7e the total production cost (Z) comprising : the production cost, inventory cost, and the labor cost. Constraint (2) states that the sum of the quantity produced at time (t) and the quantity held in inventory should satisfy the demand of the part (i). Constraint (3) represents the labor balance equation. Constraints (4), and (5) impose upper and lower bounds on the labor resources.

Model 2 A major characteristic in part batch manufacturing

environment is the high cost of tooling and set-ups. This is due to the frequent set-up and the continuos change in demand pattern. As a result, a more realistic formulation of the planning system would consider the inclusion of detailed cost elements to present actual-manufacturing situations. Hence, the total part production cost element in equation (1), (Air) can be disaggregated to incorporate mdividual cost parameters (e.g. tool cost, set-up cost, and the manufacturing cost).

An economic process variables selection model is assumed here to handle the problem at the process planning level. The inputs to the model includes: • The unit production cost elements including: tool cost,

set-up cost, and manufacturing cost • The upper and lower bounds on the process variables

which are imposed from the inherent characteristics of the machines, tools, and part systems.

The output from the model are: • The process variables (Cutting speed, feed rate) for a given

machine, and operation combination (kd) The following notations are used : Subscripts:

Machine (k) = 1, ..., K ( number of machines) and, Operation (j) = 1,..., M (number of operations) Decision variables: For a machine (k), and operation (j) combination we define:

Xkj-- Cutting speed, (mtYmin); and Ykj = Feed rate, (mm/rev.)

Parameters: ekj=cost of one cutting tool required for operation (j) on

machine (k), (S/tool); fkj =Set-up cost per unit time, (s/rain);

gkj =manufacturing cost per unit time, ($/min); hkj = Constant

which depends on operation (j) and machine (k) combination; Skj=Depth of cut, (mm); nkj=Constant of the tool life

equation; and (m, q, r ) = exponents of cutting speed, feed, and depth of cut in the tool life equation; X L k , X U k = Lower and upper cutting speed bounds (mt/min); YLk ,YUk = Lower and upper feed rate bounds (mm/rev); Wkj=Power consumption

factor; mlkj,m2k7 Feed and depth of cut exponents in power

constraint; PWk= machine power rating, (Kw); TLkj =

minimum tool life requirement, (min.), and TPTkj = Total

production time for operation (j) on machine (k), (min.). Thus, the problem of process variables selection under the criterion of total production cost per part can be written as: Minimize

K M K M Ait = ~ ~ Akj = ~ ~ (Ekj + Fkj + Gkj) (7)

k=lj=l k=lj=l where; For operation (j) on machine (k) combination; Akj =

Production cost, Ekj =Tool cost ; Fkj = Set-up cost; and Gkj =

manufacturing cost. Such that:

EkJ : ekJ (trnkjTl~l) (7.1)

=eki(hkjn~tX~-'Y~-lS[~)

(7.2) =fkj[ (tWkj + ttkj (hkjnk) X ~ j -1 Ykqfl S[,r) ]

Gkj = gkjtmkj (7.3) -1 -1

=gkj hkjXkj Ykj

Subject to:

• . m1~ ~ 2 ~ < • Vk WkjXkjYkj Ski - PWkj (8)

A,E. Elsayed, M.H. Elwany/Journal of Materials Processing Technology 61 (1996)113-119 117

XLk_< Xkj ~XUk Vk (9)

YLk <Ykj-<YUk Vk (10)

TLkjnklXk y jS[j _< 1 Vk,j (11)

(PitTPTkj) t < t Yk,j,t (12)

The objective of model 2 in equation (7) is to minimize the unit part production cost (Ait) including : set-up cost, part/ tool cost, and onit manufacturing cost. Constraint (8) assures that the cutting power for operation (j) on machine (k) should be less than the machine power rating. Constraints (9), and (10) presents the upper and lower limits of the process variables, while constraint (11) shows that the life ofthe cutting edge should exceed the minimum tool life requirement. Constraint (12) is a coupling (integrating) constraint between model 1 and 2. It states that for every part batch the total production time should be less than the planning time bucket (t).

4.3 Numerical Example

A numerical example is suggested to investigate the validity of the solution procedure and the system analytical models. Table (1) shows the input data for model 1, where a production planning horizon of 90 hours is proposed, a time interval of 10 hours together with 5 part batch types . The required batch types are expressed at different time buckets as (Lit), for example L I I 0 indicates part batch I required at time 10, and L420 means part batch 4 required at time 20. Thus, the example includes 20 planned orders each for every part batch and time combination data as shown in table(l). Table (2) exhibits input data for model 2, with (Pikj)representinga

specific job. For example P124 means operation 4 on machine 2 for part batch 1, and P224 is operation 4 on machine 2 for

part batch 2. A manufacturing shop of 4 different machine types is assumed and 24 jobs with similar set-up and machining costs per unit time as; ($0.2/min) and ($0. l/rain) respectively, we also assume the power consumption factor for all machines to be (0.8).

4.4. Solution Procedure

In this paper, the General Interactive Optimizer (GINO) PC version of LINDO has been used to solve both model 1, and 2. Originally developed for mainframes, GINO/LINDO is a multi-purpose system for solving linear and non-linear programming problem types, similar to model 1, and 2.

Table (3) shows the system output for model 2, while table (4) exhibits the results of model 1. Both outputs have the DAT file format which is easily convertible to the other (PPP) readable formats. For the example problem data, an average CPU time of approximately (8) seconds for each job of model 2, and (2.5) minutes for model 1 were taken using a standard 486-DX2 machine.

5. Conclusions

(PPP) is a prototype multi-level integrated manufacturing planning system which has been developed using the IDEFO approach, a relational data base platform, together with analytical modeling techniques. (PPP) integrates process planning to production planning (presently aggregate plans to family process plans) tasks. (PPP) holds an open architecture, so it has the capability to incorporate other various manufacturing-related decision making functions. This paper emphasized the model building section of the system. It shows that there is a fair potential for integrating the production and process planning activities and to minimize both: the batch and part cost of production concm-rently. Such an integration procedure result in enhancement of the production system overall performance and its competitive advantage.

Table (1) Input Data Matrix for Model 1

Batch B C D d H Rmax Omax Batch B C D d H Rmax Omax

(Lit) (Lit) LllO 3.0 3.0 4.0 20 0.85 30 10 LI20 3.0 3.0 4.0 55 0.85 22 13 L150 3.0 3.0 4.0 20 0.85 12 6 L220 5.5 6.2 2.3 20 1.98 12 23 L240 5.5 6.2 2.3 25 1.98 55 18 L270 5.5 6.2 2.3 31 1.98 72 19 L290 5.5 6.2 2.3 12 1.98 10 14 L310 6.2 7.4 1.2 20 1.52 8 8 L320 6.2 7.4 1.2 35 1.52 12 9 L340 6.2 7.4 1.2 53 1.52 75 24

L360 6.2 7.4 1.2 34 1.52 9 12 L370 6.2 7.4 1.2 52 1.52 100 35 L380 6.2 7.4 1.2 78 1.52 120 24 L420 4.5 3.4 3.7 60 0.75 24 22 L440 4.5 3.4 3.7 65 0.75 21 14 L450 4.5 3.4 3.7 12 0.75 24 12 L510 5.6 7.8 8.1 78 1.54 40 15 L520 5.6 7.8 8.1 56 1.54 45 25 L540 5.6 7.8 8.1 90 1.54 100 35 L590 5.6 7.8 8.1 100 1.54 200 50

118

Table (2) Input Data Matrix for Model 2

A.E. Elsayed, h/L t-L Elwany/,lournal of Materials Processing Technology 61 (1996) 113-119

e tw tt h n m q r S ml m2 Pw XL XU YL YU TL p~j $ mm mm mm kw m/rain m/ram mm/r mm/r min

P l l l 0.2 12 2 7.8 530 0.92 0.65 0.4 0.5 2.7 1.2 2.3 30

Pl12 0.8 5 3 12.5 430 0.34 0.24 0.18 0.3 2.7 1.2 2.3 30

Pl13 0.3 20 5 66 390 0.25 0.22 0.18 0.6 2.7 1.2 2.3 30

P124 0.1 2 11 27 510 0.73 0.35 0.22 0.7 4.9 3.8 6.4 30

P211 0.2 1.5 2.4 56 680 0.65 0.34 0.24 0.2 2.7 1.2 2.3 30

P212 0.4 0.9 0.4 34 344 0.83 0.45 0.33 0.5 2.7 1.2 2.3 30

P213 0.2 12 3 15 720 0.63 0.44 0.12 1.2 2.7 1.2 2.3 30

P234 0.5 2.3 4 44 467 0.54 0.32 0.23 0.9 3.5 2.3 3.4 20

P235 0.8 19 8 25 670 0.34 0.24 0.19 0.6 3.5 2.3 3.4 20

P236 0.7 9 2.3 134 478 0.72 0.54 0.23 1.2 3.5 2.3 3.4 20

P237 0.1 23 3.4 34 520 0.56 0.37 0.18 0.7 3.5 2.3 3.4 20

P228 0.1 11 5 32 710 0.67 0.34 0.23 1.0 4.9 3.8 6.4 30

P311 0.8 4 4.7 77

P322 0.1 13 10 87

P323 0.8 8 3 65

P334 1.2 14 2 87

P345 1.3 2 5.4 97

643 0.92 0.75 0.3 0.8 2.7 1.2 2.3 30

532 0.8 0.43 0.12 0.9 4.9 3.8 6.4 30

349 0.72 0.45 0.21 1 4.9 3.8 6.4 30

566 0.65 0.43 0.22 0.6 3.5 2.3 3.4 20

434 0.55 0.34 0.23 0.5 2.6 1.4 5.3 30

P411 1.5 3.4 3 123 560 0.67 0.32 0.19 0.9 2.7 1.2 2.3 30

P422 0.8 1 3.5 56 640 0.45 0.38 0.23 1.2 4.9 3.8 6.4 30

P423 0.7 9.2 4.6 98 543 0.67 0.56 0.18 1.2 4.9 3.8 6.4 30

P444 0.4 4.7 3.2 34 765 0.74 0.43 0.21 0.8 2.6 1.4 5.3 30

P531 1.4 21 12 432 543 0.62 0.51 0,31 1.1 3.5 2.3 3.4 20

P532 2.1 11 7 54 765 0.45 0.23 0.18 0.8 3.5 2.3 3.4 20

P533 1.1 23 3 98 567 0.62 0.53 0.23 0.8 3.5 2.3 3.4 20

188 0.02 I 22

188 0.02 1 63

188 0.02 1 52

230 0.01 2 18

188 0.02 1 43

188 0.02 1 25

188 0.02 1 44

220 0.01 1.5 22

220 0.01 1.5 100

220 0.01 1.5 33

220 0.01 1.5 54

230 0.01 1.5 42

188 0.02 1 80

230 0.01 1 43

230 0.01 1 35

220 0.01 1.5 67

190 0.01 2 78

188 0.02 1 30

230 0.01 1.5 64

230 0.01 1.5 76

190 0.01 2 87

220 0.01 1.5 43

220 0.01 1,5 87

220 0.01 1.5 92

Table (3) Output Solution Matrix for Model 2

job (Pikj) X Y tm TPT A m/min mm/r rain min S/job

P l l l 72 0.2

P l12 63 0.4

Pl13 95 0.3

P124 82 0.8

P211 56 0.1

P212 68 0.2

P213 120 0.2

P234 87 0.3

P235 134 0.3

P236 53 0.4

P237 110 0.4

P228 98 0.4

0.54 14.60 2.517

0.50 7.51 1.103

2.32 24.4 4.480

0.41 4.60 0.522

10.01 13.70 2.343

2.50 5.44 0.725

0.63 14.70 2.532

1.69 6.09 0.832

0.62 21.60 3.932

6.32 17.70 3.234

0.77 25.80 4.765

0.82 13.90 2.383

job X Y tm (Pi~) m/min mm/r rain

P311 38 0.2

P322 56 0.2

P323 64 0.2

P334 90 0.2

P345 110 0.1

P411 82 0.3

P422 130 0.4

P423 65 0.2

P444 76 0.1

P531 56 0.4

P532 88 0.3

P533 76 0.2

TPT A mm S/job

10.10 16.70 3.044

7.77 24.6 4.533

5.08 15.50 2.813

4.83 21.00 3.881

8.82 13.40 2.414

5.01 10.70 1.919

1.08 4.12 0.432

7.54 19.20 3.502

4.47 11.30 1.888

19.30 45.60 9.114

2.05 15.10 2.660

6.45 31.60 6.004

Total Jobs Production Cost: $ 71.57

A.E. Elsayed, M.H. Elwany/Journal of Materials Processing Technology 61 (1996)11~ I I 9

Table (4) Output Solution Matrix for Model 1 (AP)

119

Batch (Lit) Pit Qit R t 0 t Batch (Lit) Pit Qit RI Ot

Ll l0 40 20 25 9.1 L120 35 40 17.5 12.3 L150 18 42 9.73 5.6 L220 17 45 11.3 22.4 L240 36 34 53.6 17.8 L270 43 22 66.4 18.9 L290 11 23 9.22 12.6 L310 9 34 7.9g 5.7 L320 13 56 11.6 8.2 L340 63 46 72.3 23.5

L360 13 67 8.36 11.4 L370 85 34 96.9 32.3 L380 89 23 112 23.6 L420 59 24 23.1 21.5 L440 44 45 20.7 12.6 L450 46 11 23 11.8 L510 33 56 38.1 12.7 L520 43 69 42.7 23.5 L540 83 76 94.1 33.7 L590 150 23 190 45.6

Total Batch Production Cost : $ 26365

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