kyle jun ren ass2

8/3/2019 Kyle Jun Ren Ass2

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Manufacturing OperationsManagement

Manufacturing Planning andSimulationENRFF3006

Lecturer- Dr Jun Ren

Kyle McGreevey - Enrkmcgr



Question 1

ABC, a company in the southwest of the UK are recently suffering a downturn in demand for

their products. As a result they have started to focus on reducing staff numbers as inintermediate solution to easing their financial problems.

It has been recognised by the management team that improvement is needed

concerning their methods of planning ahead. For a company of ABC’s size and output, it has

been decided that a forecasting method that can reasonably predict demand for six to eight

weeks ahead is best suited. It has also been decided that certain processes need to be set down

as how best to deal with, and adjust to, sudden increases and decreases in demand.

There are 4 main Forecasting methods that would suit this type of Operational planning,

and they fall into the category of ‘Time Series Models’. These models are;

1. The moving average

2. Weighted moving average

3. Exponential smoothing and,

4. Linear trend line.

Each model will be briefly described and then a conclusion will be drawn as to which model

actually best suits the needs of ABC as a business.

1. The Moving Average

A. Naive Forecast - this is the simplest method of forecasting as it only forecasts by

taking the current demand and using it as the next months forecast, as seen below,

(Table 1)- Moving Average Naive Approach

B. Simple Moving Average – Again a very simple method of forecasting. It is similar to

the Naive Forecast but takes into account more periods of time for its forecast, i.e 3

months instead of just 1.



(Table 2) – Simple Moving Average Approach

It follows this formula,

(Fig 1) – Calculation of a Simple Moving Average

2. Weighted Moving Average

The Weighted Moving Average (WMA) method of forecasting builds upon the Simple

Moving Average. This is as the WMA uses an extended period of time like the simple

moving average, but it weights each period of time differently with the more recent beingmore heavily weighted than older time periods. As demonstrated below;

(Table 3) Weighted Moving Average info



(Fig 2) Calculation of Weighted Moving Average Method

3. Exponential Smoothing

Exponential Smoothing again builds upon the previous model that was discussed. Where we

have already assigned the time periods with an exponentially decreasing weight we now add

in a smoothing constant that is usually a pre-defined figure that is between 0 and 1.

(Fig 3) – Calculation of Exponentially Smoothed data.

4. Linear Trend Line

With the goal of forecasting we can use linear regression to fit a predictive model to

an observed data set of X and Y values. When using this model, if we are given

further values of X(time period) then we can determine the missing values of Y

(demand forecast).

y = a + bx

where

a = intercept (at period 0)

b = slope of the line



x = time period

y = forecast for demand for period x

Method Selection

It was decided that in the case of Forecasting for ABC that Exponential Smoothing would be

used as it fits most closely to how ABC needs to be run. This is as it gives



Question 2 - Witness Modelling. (Assuming machine processing cycle times are fixed and a production run of 5000 mins)

By using Witness modelling software, the production line shown below in (Fig.1), was analysed toascertain what fundamental problems were contained within its processes over a run of 5000 minutes,

producing only 217 parts.

(Fig.1) Original process results

As shown in (Fig1), after 5000 minutes the line has become blocked around the Produce1, Inspect

and Rework sections. As the rework area is full to its capacity of 20 units a blockage is caused

because no more parts can get in, this in turn blocks up the Inspect area as it can no longer deliver

parts to the Rework section. This has a further knock on effect of filling the C3 buffer/Conveyor and

now the Produce1 station, which takes parts form C2 buffer/Conveyor and the Rework can no

longer keep turning out parts as there is nowhere for them to go. There is also a blockage situation

earlier in the line. This is caused by the Weigh process having a cycle time of 3 minute and the wash

process having a cycle time of 5 minutes. This will also cause a blockage as the C1 buffer/Conveyor

will be filled faster than the Wash process can deal with the incoming stock.

Limited Budget solution (Assuming machine processing cycle times are fixed)

There are several ways in which this process could be improved but the simplest improvement that

could be made for the biggest impact to output, was to increase the index time for C1

buffer/Conveyor from 0.5 minutes to 8 minutes. This would increase the efficiency of the cell due to

the fact that it would effectively allow the Produce1 process more time to draw parts from C2

buffer/Conveyor, which is now being filled at a slower rate, and also the added time to complete the

rework process, so now the Rework station is no longer filling to capacity the widgets requiring

rework can be taken and reworked whilst the produce process waited for the next widget being

washed.

(Fig.2) Limited Budget solution – output increased by 292% to 635 widgets.



Increased Budget Solution

If there was an unlimited budget available to improve the output of this cell then there would be anear limitless amount of ways that the output could be increased.

In realistic circumstances and budgets, then the simple addition of further machinery is the answer. If

a second produce machine was added, Produce2, with the same parameters as Produce1 then there

would be a marked increase in output to 1039, an increase of 822 Widgets.

(Fig.3) Increased Budget solution –

output increased by 478% to 1039 widgets.

Going a step further if a second Washer was added to the cell a further slight increase in output would

be possible to 1070 widgets, a further 2.9% increase on output.

(Fig.4) Increased Budget solution 2 – output increased by a further 2.9% to 1070 widgets.

Of course, if budget really was no object then more and more machinery and personnel could beadded to increase production output to an almost infinite level. But one of the major factors that

need’s to be considered is the demand for the Widget, this would dictate the point at which it is no

longer viable and cost effective to produce more parts faster.

kyle jun ren ass2

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