1. FACILITIES PLANNING AND
PRODUCTION MANAGEMENT
Final Report: TOWER OF HANOI
Linnaeus University
School of Engineering
1SE007 – Facilities Planning and Production Management
Authors: Examiners:
Boris Batljan Anders Ingwald
Fatih Topaloglu Anna Glarner
Nikolaos Georgadakis Farvid Mojtaba
Serkan Alan

2. Acknowledgement
We would like to thank our teacher/tutors Anders Ingwald, Anna Glarner and Farvid Mojtaba
who have helped and supported us when make this report. Also we want to thank for the
lectures and necessary information.

3. Table of Contents
1. Introduction.................................................................................................................. 4
1.1 Task.................................................................................................................................... 4
1.2 Account for Assumptions..................................................................................................... 4
2. Theory .......................................................................................................................... 5
3. Empirical Findings ......................................................................................................... 8
3.1 Task 1: Business Strategy........................................................................................................... 8
3.2 Task 2: Routing/List of Operations ............................................................................................ 9
3.3 Task 3: Facility Layout.............................................................................................................. 13
3.4 Task 4: Material Handling........................................................................................................ 15
3.5 Task 5: Safety Stock and Economic Order Quantity................................................................ 16
3.6 Task 6: Demand and Forecast ................................................................................................. 17
4. Results and conclusions............................................................................................... 19
5. References.................................................................................................................. 19
Appendix........................................................................................................................ 21

4. 1. Introduction
Here are we going to present our task and assumptions.
1.1 Task
We have six different tasks for this report. Tasks are showed at below with short descriptions.
1. Business Strategy: We will define business goals, who is our customer and where
should the facility be layout.
2. Routing/List of Operations : Which way we choose for production, our production
numbers and calculations.
3. Facility Layout : How our facility layout, how we choose that alternative and desicion
process will be presented.
4. Material Handling : How our material transfer between stations and reasons will be
presented.
5. Safety Stock and Economic Order Quantity : With holding and setup we calculate
optimal production.
6. Demand and Forecast : Acording to previous years data, we will calculate demand and
forecast for 2011.
1.2 Account for Assumptions
 Assume Operation Times to Produce a Game
Figure 1: Time Calculation for Parts
We assume production times. For brass, we have four different diameters. The table above
shows average brass produces time.
 Working Time
We have 7.5 hours working time and half an hour lunch break per a day. We assume that we
will work 20 days a month. When we calculate work days per month we consider weekends,
religion holidays, national holidays. It means we have 27,000 seconds per a day; 540,000
seconds per a month working time.
 In task 5 annual demand is used as 72000 but normally it is 71811. It helps to
calculation.
Name of Part Time for Produce
One
We Need for One
Game
Time to Produce One Game
Brass 10 seconds 4 40
Peg 8 seconds 3 24
Base 70 seconds 1 70

5. 2. Theory
Here are we going to present the theory we used.
2.1 Relationship Chart
According to Chien (2004) is Muther‟s view and method regarding systematic layout
planning (SLP), not only a proven tool in providing layout design guidelines, the method is
used over the whole world among enterprises and in the academic world.
Figure 2. Example of an Activity Relationship Chart
When starting with the SLP procedure and improving the Activity Relationship Chart does the
process start with making the relationship between each activity on the chart more
comparable. The activity relationship is used to decide the relationship score and diagram
between each activity and is an important indicator in decision-making. Figure 2 shows an
example of an activity relationship chart, the chart usually looks like the one in figure 2.
2.2 Material Requirements Planning (MRPI)
According to Hill (2005) is MRP a system that determines the final services and products
(depends on what kind of products and the amount) that a company will produce in the future;
the system does also specify the necessary inputs to meet that demand. MRP is also used to
manage the capacity needs in a company and also the material needs. For example is the
demand for engines, tires and brakes linked to the demand of vehicles. To determine the
number of engines, tires and brakes we need to determine the number for vehicles. When we
have the number of vehicles can we calculate the requirements for all dependent items.

6. 2.3 Master Production Schedule (MPS)
Master production schedule focuses on to produce certain quantities of services or products in
a particular time periods. To do this, does it take statements of demand (forecast sales and
known orders) and test those against statements of capacity and resources for the same
period(s). The result would be an anticipated schedule of finished services and products. This
schedule has a key role in the control system that leads to an agreement between marketing
and operations on what a company shall produce. The requirements are inventory records, the
quantity and timing of current operations schedules, outstanding purchase orders, up-to-date
bills of materials that reflect changes and clear information about the customers requirements
(the existing customers), current orders and sales forecasts. The master production schedule is
based mostly on forecast in the later periods of the planning horizon. (Hill, 2005)
2.4 Material Handling System Equation
According to Tompkins (2010) are they‟re some material handling system designs, and the
material handling system equation is one of them. The handling system is used to identify
opportunities for improvement. It gives framework to identify solutions to material handling
problems. What defines what type of material has been moved, where and when identifies
the time and place requirements, who and how tells the material handling methods. Whit
these questions shall the system lead to a recommended system. The material handling system
equation is given by:
Materials + Moves + Methods = Recommended System
Figure 3. Material Handling System Equation

7. 2.5 Simple/Weighted Moving Average and Exponential Smoothing
Inman (2006) explains that a simple moving average takes a predetermined number of
periods, sums their actual demand, then the sums is divided by the number of periods to reach
a forecast. For each period, the latest period gets added and the oldest period of data drops off.
A good example could be if we use actual demand with example numbers; 45 in January, 60
in February and 72 in March would give the result of:
45 + 60 + 72 = 177/3 = 59
If there would be interesting to get the forecast for May, we would drop January‟s demand
from the equation and add the demand from April.
A weighted moving average takes the predetermined weight to each month of past data, then
shall the past data from each period be summed, and at least be divided by the total of the
weights. The results are later on summed to achieve a weighted forecast. Generally can it be
said that when the older the data is, the smaller is the weight, and the more recent the data is,
the higher will the weight be. If we say that we use demand examples with a weighted
average using weights of .4, .3, .2 and .1, would the forecast fore June be:
60(.1) + 72(.2) + 58(.3) + 40(.4) = 53.8
Exponential smoothing is about taking the precious period‟s forecast and adjusts it by a
predetermined smoothing constant, ά (called alpha) multiplied by the difference in the
previous forecast and the demand that actually occurred during the previously forecasted
period (called forecast error). The formula of exponential smoothing is:
New forecast = previous forecast + alpha (actual demand – previous forecast)
F = F + ά(A-F)
Exponential smoothing requires an amount of past data and a beginning or initial forecast. It
does also require the forecaster to begin the forecast in a past period and to continuously work
forward to the period for which a current forecast is needed. Initial forecast can be an actual
forecast from a previous period, actual demand from a previous period, or it can also be
estimated by averaging all or part of the past data. The accuracy of the initial forecast will not
be critical if someone is using large amounts of data, just because exponential smoothing is
self-correcting. If there are enough periods of old data, will the exponential smoothing
eventually make enough correction to compensate for a reasonably inaccurate initial forecast.
Using for example an initial forecast of 50, and an alpha of .7 for February will the forecast
for February be:
New forecast (February) = 50 + .7(45-50) = 41.5
New forecast (March) = 41.5 + .7(60-41.5) = 54.45
This process will continue until the forecaster reaches the desired period. (Inman, 2006)

8. 2.6 Economic Order Quantity (EOQ)
According to Hill (2005) is there one fundamental decision in the inventory management,
which can be very hard to answer. The question is how much a company should order, in the
context of what quantity will result in the lowest total cost. The economic order quantity
(EOQ) and the economic batch quantity (EBQ)/economic lot size (ELS) models are linked to
the question: “how much shall for example a company order to minimize the total cost of
holding inventory?” The formulas for each are given below:
- EOQ = 2zCs/cC
- EBQ/ELS = 2zCs/cC * p/p-d
Variables:
 z = total annual usage
 Cs = cost of placing an order
 c = unit cost of the item
 C = carrying cost rate per year
 p = production provisioning rate (units) per day
 d = demand rate (units) per day
It is important to note that these models make the following assumptions:
- The rate of demand is constant
- Costs remain fixed
- Operations capacity and inventory holdings are unlimited
3. Empirical Findings
3.1 Task 1: Business Strategy
Business Goals
Our company is customer oriented. We try to be always in time for our deliveries and never
let our customers down .The quality of our products is high but without exaggerations to the
price or the appearance. Also, as a profit organization, we are looking forward to increase our
earnings year by year but not in the expense of our clients. On the contrary it is our constant
effort to lower the production cost by improving the facility‟s quality rate. That is the reason
that the production line is equipped with new and up to date machinery.
Customers
Since we are a factory, it is preferred for us to sell our products at wholesale. We ship the
products to retailers all over the world, toy stores, hobby centers and also supermarkets on
selected countries trying to make our product available for a big number of people. The target
group varies from country to country but in general are people interested in puzzles and
mathematical games.

9. Location of the customer
Although our industry is capable of delivering all over the world, we focus on countries that
have normal or high living standards. Also, one of the regions that the company pays more
attention is west Europe and France in particular. That is happening because of the popularity
the game has there since its inventor of was of French nationality
Facility Location
After consideration, it was decided that best location for a company of this kind is best to be
in central Europe. France and Germany represent the prerequisites we have set. Stability in
the economic life in addition to the low tax rates for new or young factories were the main
concerns. The proximity to the target countries also affected our decision. Moreover, rent
costs, experienced labor in factories and easier delivering throughout Europe was also
considered.
Specifics about the factory‟s location
- A non-urban area  low rents, plenty of space
- Close to highway  raw material/finished products deliveries
- Preferably close to a port  shipping with containers
3.2 Task 2: Routing/List of Operations
Machine Choosing:
Name Cost per Hour Cost per Month
Alternative 1 103 Vertical
Machining Centre
150 56,320
104 Assembly Table 20
106 CNC Auto Lathe
with Bar Feed
140
2x Operators 21x2
Alternative 2 101 Band Saw
Machine
20 56,480
102 CNC Auto Lathe 100
103 Vertical
Machining Centre
150
104 Assembly Table 20
3x Operators 21x3
Alternative 3 103 Vertical
Machining Centre
150 82,080
104 Assembly Table 20
2x 106 CNC Auto
Lathe with Bar Feed
140x2
3x Operators 21x3
Figure 4: Machine Alternatives
First we compare alternative 1 and alternative 2. Both alternatives satisfy our demand and
operation costs are almost same. We choose alternative 1 because alternative 1 is more
automatic and less human dependent. Human work can be wrong or more possibility to make

10. fault. On the other hand automatic systems are more trustable and less possibility to make
some faults.
Then we think we can use 3 machines for 3 parts. On that way our production will be so fast
because we produce all parts all the time and do not lose time for setup machines. But when
we compare our production, numbers between produced parts are too far from each other and
we do not need that production. Also our production is too much that our demand. So we do
not have to that much operation cost because we do not need it.
Calculate Working Time
Spend Time Description Time per Month (Seconds)
Brass Setup time 147,600
Mini Setup Time 5,400
Peg Setup Time 19,200
Base No Setup Time 0
Figure 5: Setup Time for Machines
Name of Part Days a Month Seconds a Month
Brass 12 304,200
Peg 8 196,800
Base 20 540,000
Figure 6: Clear Work Time
In production planning, we have 106 CNC Auto Lathe with Bar Feeder which we are
planning to produce brass and peg. The machine produces brass 2 days, after one day peg,
then 2 days Brass and one day peg. And we have 103 Vertical Machining Centre which we
produce just base part.
Calculate Production
Name of
Part
Average
Deviation
for Planned
Production
Time
Total Planned
Production
Time per
Month
(Seconds)
Clear
Work
Time per
Month
(Seconds)
Needed
Time to
Produce for
one Game
(Seconds)
Real
Capacit
y per
Month
(Items)
Avera
ge
Scrap
Rate
Approved
Product
Capacity
per Month
(Items)
Base 1.18 540,000 457,627 70 6,536 5.58% 6,157
Peg 0.94 196,800 209,361 24 8,723 1.34% 7,715
Brass ø40 1.05 90,000 85,714 12 7,142 0.74% 7,089
Brass ø30 1.02 80,200 78,627 11 7,148 1.02% 7,075
Brass ø25 1.02 67,000 65,686 9 7,298 0.78% 7,241
Brass ø20 1.14 67,000 58,772 8 7,347 0.66% 7,299
Figure 7: Number of Parts We Produce
When we calculate “Average Deviation for Planned Production Time” we tool average of
previous five years, then we divided “Average Deviation for Planned Production Time” with
“Total Planned Production Time per Month” and got “Clear Work time per Month”. For
calculate “Real Capacity per Month” we divided “Clear Work Time per Month” with
“Needed Time to Produce for one Game”. We took average of previous five years scrap rate
to calculate “Average Scrap Rate”. At the end, to find “Approved Product Capacity per
Month” we subtract “Average Scrap Rate” from “Real Capacity per Month”.

11. Calculate Cost
Name Cost per Hour Working Hours per Month
103 Vertical Machining Centre 150 160
106 CNC Auto Lathe with Bar Feeder 140 160
104 Assembly Table 20 160
2 Operators 21x2 160
Total Operating Cost: 56,320
Figure 8: Operating Cost
Name of Part Real Capacity
per Month
Length
(Meters)
Price per Meter
(Pound)
Cost
Base 6,536 0.102 6 4,000
Peg 26,169 0.05 1.8 2,355
Brass ø40 7,142 0.005 170 6,071
Brass ø30 7,148 0.005 140 5,004
Brass ø25 7,298 0.005 78 2,846
Brass ø20 7,347 0.005 50 1,837
Total Cost for Row Material 22,113
Figure 9: Raw Material Cost
Calculate How Many Games We Can Sell
According to previous years‟ quality rate, we calculate weighted moving average for quality.
Our weighted moving average for quality is %98.77. Our lowest production is base. So when
we calculate how many games we will produce per a month, we took base production number
as game production number which is 6,157. Our game production per year is
12x6,157=73,884. For find how many games we can produce to sell per year, we multiply
how many games produced with weighted moving average for quality which is
73,884x0.9877=72,975
Our capacity for produce games to sell is 72,975 games per year
List of Operation for Peg
Part name: Peg Prep. by:
Group 62
Part no: AI1003 Date:
05-03-2013
Amount: 3,271 / Day
Op # Description Work site Setup time
(min)
Op. time
(sec)
Cost (£)
0 Feeding 106 - 3 0.12
1 Screw Cutting 106 30 min / 3 35.12
2 Cutting 106 1 day 2 35.08

12. List of Operation for Base
Part name: Base Prep. by:
Group 62
Part no: AI1002 Date:
05-03-2013
Amount: 6,536 / Month
Op # Description Work site Setup time
(min)
Op. Time
(sec)
Cost (£)
0 Put the Parts 103 - 7 0.29
1 Site Cutting 103 - 10 0.42
2 Face Cutting 103 - 20 0.84
3 Frame Cutting 103 - 10 0.42
4 Drill 103 - 8 0.33
5 Screw Cutting 103 - 8 0.33
6 Take the parts 103 - 7 0.29
List of Operation for Brass
Part name: Brass Pieces Prep. by:
Group 62
Part no: AI1004-1007 Date:
05-03-2013
Amount: 4,822 / 2 Days
Op # Description Work site Setup time
(min)
Op. Time
(sec)
Cost (£)
0 Feeding 106 - 2 0.08
1 Drilling 106 30 min / 2 21.08
2 Cutting 20 106 2 days 4 21.16
3 Cutting 25 106 5 5 21.19
4 Cutting 30 106 5 7 21.27
5 Cutting 40 106 5 8 21.31

13. 3.3 Task 3: Facility Layout
While designing the facility layout we tried to be as simple as possible. We considered about
different difficulties that may present on the production and tried to prevent them. Since the
machines 103 and 106 are the only ones to use raw materials (the first levels of our bill of
material) we placed them next to the storage of raw materials. Also, knowing that the brass
bars are difficult to handle (even 6 meters long) we placed the 106 in such way that the
feeding is easy and simple.
The storage of finished products was designed as a simple square room next to the last
production station. Shelves are being used and the capacity is big enough to store a lot more
products than our safety stock.
The handling of the products is easy and carts are used to move objects around the facility.
But for safety reasons, the machinery on our blueprints is designed bigger than the reality
(example: 103- in reality 5mX4m, on blueprints 6mx5m) . This idea was approved among
other reasons because movement around the factory must be easy for the workers and contact
with dangerous machine should be avoided. The area covered was increased by nearly 25%
(160m2
198m2
) but the extra cost for renting a bigger place was considered an investment
on safety.
Production
The production is close to straight-line production prototype but there are some differences.
Also there is no storage between the stations and the handing is easy because of the resistance
of the product (no special handling needed ex. Temperature or fragility )
Relationship Chart
Figure 10. Relationship Chart
As mentioned before, it is of big importance that 103 and 106 are close to the raw material
storage and also near to the assembly station that is the next step of the production. In
addition, the finished product storage must be close to the assembly area to minimize the
transporting time.
1.Raw Material Storage
2.Lathe 106
3.Vertical 103
4.Assembly 104
5.Finished Product Storage
Proximity needed
Proximity is
unimportant

14. Relationship diagram
Figure 11. Relationship Diagram
Space Relationship Diagram
Figure 12. Space Relationship Diagram
Layouts
Figure 12. Layouts
The second layout is both more space efficient and production assisting.
Raw Material
R.M Storage
106
103
Assembly
1 2
3
4
Storage
5
4mX8m 1.
5mX9m 2.
3X3
4.
5mX6m 3. 5mX5m 5.

15. 3.4 Task 4: Material Handling
When my group and me thinking about material handling, we took some decisions. Everyone
can use it easily because we do not have someone who responsible for material handling,
operators do that. It is not necessary to be sensitive because we will not carry fragile parts.
And of course we have to choose cheapest alternative.
According these requirements, we decide to use push/pull hand control wheeled steel cars. We
planned that in every station has cars. We locate two cars for every station. With that, operator
take row material from car and when it is finished put part to another car. And also we have
one extra car. When operator moves the parts to next station, he/she replace car‟s location
with free car. With that production will not be stop.
Also we want to show our material handling system with “Material Handling Systems
Equation”
Why? We have to transfer parts between stations.
What? Row material, machined part and finished product
Where? Between stations and storages
When? When part‟s operation finish in that station
How? With push/pull hand control wheeled steel cars
Who? Operators
Which? Hand control wheeled steel cars by operators
Figure 13. Material Handling

16. 3.5 Task 5: Safety Stock and Economic Order Quantity
EOQ is mainly describe the relationship between ordering cost, holding cost and the order
quantity. Thanks to EOQ, total cost can be minimized by the optimum batch size.
Formulations which are used in task 5 are presented below.
Formulations
The general Q*
formulation is include A= ordering cost, h= holding cost, D= annual demand
𝐻𝑜𝑙𝑑𝑖𝑛𝑔 𝐶𝑜𝑠𝑡 = (𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛 𝐶𝑜𝑠𝑡𝑠 + 𝑅𝑎𝑤 𝑚𝑎𝑡𝑒𝑟𝑖𝑎𝑙 𝐶𝑜𝑠𝑡𝑠)/2
𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛 𝐶𝑜𝑠𝑡 = 𝑀𝑎𝑐𝑕𝑖𝑛𝑒 𝐶𝑜𝑠𝑡 + 𝑂𝑝𝑒𝑟𝑎𝑡𝑜𝑟 𝑐𝑜𝑠𝑡
𝑂𝑟𝑑𝑒𝑟𝑖𝑛𝑔 𝐶𝑜𝑠𝑡 = 𝑆𝑒𝑡𝑢𝑝 𝑡𝑖𝑚𝑒 ∗ 𝑐𝑜𝑠𝑡 𝑜𝑓 𝑚𝑎𝑐𝑕𝑖𝑛𝑒(𝑕𝑜𝑢𝑟)
base holding cost operation
cost
raw material
cost
setup
cost
EOQ
1,89 3,16 0,612 0 0
brass 20 holding cost operation
cost
raw material
cost
setup
cost
EOQ
1,44 0,38 2,5 11,67 1080
brass 25 holding cost operation
cost
raw material
cost
setup
cost
EOQ
2,16 0,42 3,9 11,67 882
brass 30 holding cost operation
cost
raw material
cost
setup
cost
EOQ
3,75 0,50 7 11,67 669
brass 40 holding cost operation
cost
raw material
cost
setup
cost
EOQ
4,52 0,54 8,5 11,67 610
pegs holding cost operation
cost
raw material
cost
setup
cost
EOQ
0,51 0,38 0,63 11,67 1823
Figure 14: EOQ Calculation
Calculation example of Brass 20
Operation Cost= (8/60)*(140/60)+(21/60)*0,2=0,38
8 second is operation time over 60 to find minute. 140 is cost of machine per hour. 21 is
operator cost per hour and multiple with 0,2 which is operator requirement.
Raw material Cost= (5/100)*50=2,5
5mm shows thickness of brass and 50 is raw material cost for per meter.
Holding cost= (operation cost + raw material cost)/2 =1,44
Ordering cost= 5*(140/60)=11,67
5 shows setup time and 140 is operation time of machine.

17. 𝐸𝑂𝑄 =
2∗𝐴∗𝐷
𝑕
, 𝐸𝑂𝑄 =
2∗11,67∗72000
1,44
= 1080
Safety Stock
In this project safety stock added to amount of production. Safety stock is prevent to company
shortage stock by maintain the product. Stock out problem can affect the company more than
keep stock. So safety stock which another name is buffer stock should be used to meet
customer needs. Also sometimes forecasting doesn‟t find certain number of sales. In this
situation safety stock help to meet customer needs too. In this project safety stock is choose
as a 5% of 6000. Company have 300 units of finished product each month. This number can
meet the fluctuation of forecast or demand.
3.6 Task 6: Demand and Forecast
Company‟s last two years sales data presented in table 3. Thanks to these data next year
forecast can be done. Simple moving average, weighted moving average, regression and
exponential smoothing methods used for the 2011„s forecast. The 2009 and 2010 sales data
represented below.
1 2 3 4 5 6 7 8 9 10 11 12
2009 5805 6061 5888 5944 5845 5822 5992 6079 5892 6141 5873 5892
2010 5810 5882 5771 6184 6167 6075 6046 6136 5967 6075 6024 5954
SMA is mainly unweighted mean of previous “n” data. In this part of report during the
forecasting n is equal to 5. In table 9 data looks varying slowly so n used like 5. If between
two months these sales amount were changing much, varying would be more and in this case
n had to be smaller number. There is a calculation example of SMA for January of 2011.
𝑺𝑴𝑨 =
𝟔𝟏𝟑𝟔+𝟓𝟗𝟔𝟕+𝟔𝟎𝟕𝟓+𝟔𝟎𝟐𝟒+𝟓𝟗𝟓𝟒
𝟓
= 𝟔𝟎𝟑𝟏
Weighted moving average method is another method which is used for forecasting. This
method is also mean of previous “n” data but the difference is that previous first data
weighted more than others. In this chapter this weight like 0,4 for first previous month sales
data, 0,3 for second , 0,2 for third and 0,1 for fourth data. It means that first previous month
sales data affect the forecast more than others because of the weight coefficient. There is a
calculation example for January of 2011.
𝑾𝑴𝑨 = 𝟎, 𝟒 ∗ 𝟓𝟗𝟓𝟒 + 𝟎, 𝟑 ∗ 𝟔𝟎𝟐𝟒 + 𝟎, 𝟐 ∗ 𝟔𝟎𝟕𝟓 + 𝟎, 𝟏 ∗ 𝟓𝟗𝟔𝟕 = 𝟔𝟎𝟎𝟏
Regression is method to find a relationship among the data to forecast. It is nonlinear
regression and based on dependent parameter and independent variables. For the regression
method Minitab 14 which is statistical calculation software is used. The model show that
basic parameter is 5910 and each month increase the forecast by the 9,49* ( month)
𝑭 = 𝟓𝟗𝟏𝟎 + 𝟗, 𝟒𝟗 ∗ 𝒎𝒐𝒏𝒕𝒉 So forecast for January of 2011 is like:
𝑭 = 𝟓𝟗𝟏𝟎 + 𝟗, 𝟒𝟗 ∗ 𝟏 = 5919

18. Exponential smoothing method is last method for forecasting. This method mainly weighted
by a number (α) between 0 and 1 to difference of past real and forecasting data. In this part α
is equal to 0,3. Deciding α is mainly most important part of this method. When α is so big it
means that previous data and its forecast so important for next month forecast. 0,3 is chosen
and it is not big number to affect next forecast so much. There is a calculation for January
2011.6041 is showed that 2010/12 sales forecast and 5954 is same month real data.
F2011/01= 6041+0,3*(5954-6041)= 6015
During forecasting all four method was used and then average of these methods results is the
forecast for 2011. Average of these four method is more useful than per method because when
average of them used it is decrease the error of forecast. There is average of January 2011
forecast table.
Figure 15: Forecast for 2011
MPS
Week 1 2 3 4 5 6 7 8 9 10 11 12
Forecast 5992 5984 5982 5978 5977 5979 5980 5982 5984 5987 5991 5996
Available 89 186 285 300 300 300 300 300 300 300 300 300
MPS 6081 6081 6081 5993 5977 5979 5980 5982 5984 6987 5991 5996
On Hand 0
Figure 16: MPS
First three months machines work full capacity to meet customer needs and make a safety
stock. Safety stock is chosen as 5% of 6000 which is general number for forecast. When reach
the safety stock which is fourth month in this case, manufacturing is going on just as amount
of forecast.
2011
SMA WMA Regression Exponential Average
January 6031 6001 5919 6015 5992
February 6002 5995 5929 6008 5984
March 6006 5984 5938 6001 5982
April 5987 5982 5948 5995 5978
May 5978 5982 5957 5990 5977
June 5982 5979 5967 5986 5979
July 5980 5978 5976 5984 5980
August 5979 5979 5986 5983 5982
September 5979 5980 5995 5982 5984
October 5980 5982 6005 5983 5987
November 5982 5984 6014 5984 5991
December 5985 5988 6024 5986 5996

19. MRP
As you can see there is kind of bill of material of Tower Hanoi. In this figure req show that
abbreviation of requirement. January and Fabruary of 2011 has same production amount. So
these two months has same MRP.
4. Results and conclusions
In this paper we consider about almost all manufacturing phase. From facility layout to
scheduling all parts activities calculated or discussed. Good planning enables to companies to
use minimum resources while getting the maximum benefit.
The aim of this paper is to show how planned manufacturing , facility layout, decided
business strategy, selected material handling also how it can be planned better and how it can
be supported by the relevant other theories. This is done by solving several tasks related to
facilities planning and production management. In the first task, the business strategy is
explained in detail. The selection of the machines is argued in the second task. There are three
alternative to produce tower of Hanoi. Alternative one which is consist of vertical machining
centre, assembly table, CNC Auto lathe with bar feed is chosen because of the costs. Also in
second task all the manufacturing operation time presented. In the third task, two facility
layout are suggested for the machines which are selected in the second task. Alternative 2 is
selected because it has less space and better material handling function for CNC Auto lathe
machine. In the fourth task, material handling system equation are identified and answered
seven question and decided to material handling system. Lastly, in the fifth and sixth task
calculated EOQ and decided safety stock with one year forecast.
Tower of hanoi
req=6081
Base Plate
req=6081
set of pieces
req=6081
Brass 20
req=6081
Brass 25
req=6081
Brass 30
req=6081
Brass 40
req=6081
Pegs
req=18243
Figure 17: MRP

20. 5. References
Inman, R. Anthony. "Forecasting." Encyclopedia of Management. Ed. Marilyn M. Helms. 5th
ed. Detroit: Gale, 2006. 307-311. Gale Virtual Reference Library. Web. 5 Mar. 2013.
Hill, Terry. 2005. Operations management. 2nd ed. Basingstoke: Palgrave Macmillan
Tompkins, J.A., White, J.A. and Bozer, Y.A., 2010. Facilities Planning. 4th
ed. Hoboken:
John Wiley & Sons, Inc.
Chien, T.K., 2004. An empirical study of facility layout using a modified SLP procedure.
Journal of Manufacturing Technology Management, [e-journal] 15(6). Available through: The
Emerald Research Register <www.emeraldinsight.com/1741-038X.htm> [Accessed 5 March
2013]

21. Appendix
The game, Tower of Hanoi, consists of 1 ground plate made of aluminum, 3 play pegs made of
aluminum and 4 play bricks made of brass.
Figure 2 Drawings of Tower of Hanoi

22. Figure 3 Bill of Material
Figure 4 Product structure
Level 2
Level 1Tower of Hanoi
PegsSet of PiecesBase Plate
Piece 1 Piece 4Piece 3Piece 2
3
1
1
1
1
11
Level 3
Brass,
SS-ISO
5170, Ø
40 x 6
Brass,
SS-ISO
5170, Ø
25 x 6
Brass,
SS-ISO
5170, Ø
20 x 6
Brass,
SS-ISO
5170, Ø
30 x 6
Aluminium,
EN6082-T6,
50 x 102 x 8
Aluminium,
EN6082-T6,
Ø 8 x 35

23. Table 1 Cost of material
Material Standard length Cost
Aluminum, EN6082-T6, 50x8 3 m 6 £/m
Aluminum, EN6082-T6, Ø 8 2 m 1,8 £/m
Brass SS-ISO 5170, Ø 40 4 m 170 £/m
Brass SS-ISO 5170, Ø 30 4 m 140 £/m
Brass SS-ISO 5170, Ø 25 6 m 78 £/m
Brass SS-ISO 5170, Ø 20 6 m 50 £/m
If bought cut in special length the lead-time is 2 weeks.
Table 2 Production and quality rate of AI1001
Year Production Quality rate
2006 80 000 98,9%
2007 79 000 98,1%
2008 77 000 99,1%
2009 73 000 99%
2010 65 000 98,8%
Table 3. During 2009 and 2010 WÄDUR had the following orders:
Month
Orders
2009 2010
Jan 5805 5810
Feb 6061 5882
March 5888 5771
April 5944 6184
May 5845 6167
June 5822 6075
July 5992 6046
Aug 6079 6136
Sept 5892 5967
Oct 6141 6075
Nov 5873 6024
Dec 5892 5954

24. Table 4. Average deviation from planned production time.
Year AI1002 AI1003 AI1004 AI1005 AI1006 AI1007 AI 1001
2006 1,0 0,75 0,86 1,0 1,0 1,1 0,9
2007 1,3 0,75 1,0 1,1 1,0 1,1 0,9
2008 1,3 1,0 1,28 1,1 1,1 1,1 0,95
2009 1,0 1,2 0,71 1,0 1,1 1,2 0,95
2010 1,3 1,0 1,42 0,9 0,9 1,2 0,9
1 means according to plan, >1 it takes more time, <1 it requires less time
Table 5 Average scrap rate
Year AI1002 AI1003 AI1004 AI1005 AI1006 AI1007 AI 1001
2006 7,1 % 2,1 % 0,6 % 1,0 % 0,9 % 0,5 % 1,1 %
2007 6,0 % 1,2 % 0,7 % 1,1 % 0,9 % 0,5 % 1,9 %
2008 6,0 % 1,1 % 0,9 % 1,1 % 0,7 % 0,7 % 0,9 %
2009 3,7 % 1,4 % 0,5 % 1,0 % 0,8 % 0,7 % 1,0 %
2010 5,1 % 0,9 % 1,0 % 0,9 % 0,6 % 0,9 % 1,2 %