XYZ Company is an Egyptian based

NAME NANCY MOHAMED SAAD ELSHERBINI
STUDENT ID 17126074
UNIT CODE MBA7061
UNIT TITLE OPERATIONS MANAGEMENT
WORD COUNT 6000
DUE DATE 21/4/2018
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Table of content
Executive summary……………………………………………………………………………………………………………………..2
Company overview……………………………………………………………………………………………………………………..2
Forecasting………………………………………………………………………………………………………………………………….3
Quality control…………………………………………………………………………………………………………………………….4
Capacity planning………………………………………………………………………………………………………………………..11
Suppliers……………………………………………………………………………………………………………………………………..14
Inventory management……………………………………………………………………………………………………………….16
Order fulfillment………………………………………………………………………………………………………………………….18
Aggregate planning……………………………………………………………………………………………………………………….20
Conclusion…………………………………………………………………………………………………………………………………….22
Appendix……………………………………………………………………………………………………………………………………….23
Bibliography…………………………………………………………………………………………………………………………………..32
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Executive summary:
XYZ Company is an Egyptian based company specialized in the production of alkyd resins. The company
engaged me as an operations management consultant to assess its operations & decide on areas with
obstacles that needs to be improved.
We assessed different areas of the organization starting from evaluating the company forecasting
technique. Forecasting is not considered an activity with great importance at XYZ because of several
factors from their point of view Alkyd is considered as a product with high standardization, creating
great stability in the usage of requirements. After assessing XYZ forecasting we concluded that its
forecast deviates a lot from actual results so by the aid of a software like POMQM we recommended
that the company may use multiplicative decomposition technique on future forecasts as we found
alkyd demand to be seasonal and also have trend, also the company might adapt different forecasting
techniques under seasonality & trend. We also require the company to assess it forecasting technique
every year to check its accuracy.
The operations director main concern is continuous trial minimize operating costs parallel to the
organization low cost strategy. Though, operations director clarified that they have steady complications
coming from suppliers; delays, quality problems, etc. on this issue we recommends the company to
calculate its break-even point comparing the two total costs of insourcing and outsourcing. The breakeven calculations recommend that the company start insourcing and replacing one of its international
suppliers with local supplier. And since the company doubts the quality of local products it should state
clear conditions on their contracts with local suppliers regarding quality and inspect or test samples of
raw materials purchased before accepting it with penalty clause on quality variation & any delays. By
this the company will be compensated on any obstacles that may arise.
Another issue is quality problems XYZ Company has although those problems are fixed before reaching
customers; they still contribute in operations cost rising. On solving this issue the company should
decide on how much & when to inspect product whether before, during or after production. We took 20
samples with 5 observations from a used chemical to inspect the time needed; workers are responsible
on performing in-process quality, and escalate any defects discovered to supervisor. Time needed on
performing each step is critical on delivering products to the customers in a timely manner.
In the same manner we found that the company’s capacity planning needs to be revised in order to to
reach high levels of utilization and efficiency. We evaluated the production process as the company uses
batch processing on producing resins. Batch processing provides the company with appropriate volume
and flexibility needed for output customization to cover individual orders. The manufacturing process
for XYZ is considered the same as results of high standardization amongst the ingredients. By reassessing
the production process we found that one of production greatest problems is bottle necks, we solved
this problem and decreased cycle time from 218sec to 180sec resulting in increase in efficiency from
70.31% to 83.33% and increasing output from 135tons/day to 160tons/day.
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Raw materials inventory XYZ Company tries to minimize inventory amount. The company is using pointof-use replenishment for some parts of operations, with deliveries coming directly to production floor.
Though, this strategy needs to be reassessed for international suppliers because the current model of
raw material ordering & inventory overlooks economic quantity order measures also ignoring quantity
discounts to be offered on large quantities. We adapted one of the economic order quantity namely
quantity discounts model and by calculating EOQ we reduced the total cost by $59977.
Solutions proposed here are comprehensive in nature as all the solution complements each other. The
major problem company faces is incoherence of strategies and decisions that company make.
Company overview:
Paints conquer a noticeable place in cultural history of manhood. People were always fascinated with
colors & used paints for 2 main reasons; decoration & protection to object or surface. A paint or coating
is considered a liquid or mastic composition created to act as thin coat to be converted to adherent film
once being applied. Paints & coatings contain specific elements that altogether play a role in its
performance properties including durability & final appearance.
In general, modern paint is complex mixture of ingredients that comprises binder (resin), additives,
pigments and solvents.
Resins are considered a vital component that present in every type of paints. It binds & glues ingredients
of a paint together & provides durability & resistance properties to paint. Resins have different forms
such as Latex, Alkyd, Epoxy and Polyurethane according to the industrial usage
XYZ Co is an Egypt-based company engaged in the production of Alkyd resin for the painting industry
and owned by mainly 3 shareholders. The owners started their business in 1998 in Borg El Arab
industrial zone. The total sales for XYZ were around 15 Million Egyptian pounds during year 2016. The
company is operating under a strategy that aims to provide its customers quality products at a
competitive selling price.
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Key issues identified
Section 1
Forecasting:
All manufacturers in each type of business have the same difficulty of making the right choice in
production planning balancing supply & demand. With the resources & production capacity limitation,
management teams must have a clear vision of future to make business planning strategies on
production, finances, marketing & personnel to achieve main business objective, which is profit
maximize. In order to reach this, there are some significant factors should be taken into consideration
like customer service level in terms of product quality & delivery on time. All marketers say “to delivery
the right quality & quantity of the product at the right time & at right place” is to maximize customer
satisfaction.
The case study is about one manufacturer of alkyd resins in Egypt that has the challenge in making
decisions on production plan to meet with marketing’s sales volume projection for every month. The
marketing department has to inform sales projection or sales forecast in quantity to the production
department to be their references to set the production plan on a monthly basis. There is no exact
forecasting technique that has been applied for the sales projection in the past. Marketers use their own
expert opinion demand intuition to forecast customer demand. The company has a max. Capacity of
42000 tons of alkyd resins each year; effective capacity 38000 tons/year demand they reach an actual
O/P of 30000 tons/year when operating for only one shift (8 working hours).
Graph 1.1
The above graph shows actual total demand on monthly basis of years 2015-2017.From the graph we
can see that the highest demand is 4900 metric tons on May 2017 demand the lowest demand volume
is 1500 metric tons on January 2015. Additionally, from the graph we can obviously see that data
pattern repeated year after year.
0
1000
2000
3000
4000
5000
6000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
5

2015	2016	%change	2016	2017	%change
1	1500	1750	16.66667	1750	2000	14.28571
2	1550	1850	19.35484	1850	1950	5.405405
3	2250	2800	24.44444	2800	3100	10.71429
4	3350	3800	13.43284	3800	4350	14.47368
5	4150	4500	8.433735	4500	4900	8.888889
6	1650	1950	18.18182	1950	2000	2.564103
7	1800	2000	11.11111	2000	1950	-2.5
8	2900	3400	17.24138	3400	3300	-2.94118
9	3850	4550	18.18182	4550	4350	-4.3956
10	1950	2300	17.94872	2300	2000	-13.0435
11	1750	1900	8.571429	1900	1700	-10.5263
12	1800	2100	16.66667	2100	2200	4.761905
sum	28500	32900	15.4386	32900	33800	2.735562

AssignmentTutorOnline

Table 1.1
The above table shows percentage change in demand each month y-o-y. From the graph demand table
above we can conclude that XYZ sales are increasing from 28.5 MT in 2015 to 33.8 MT in 2017. In
addition, the demand has seasonal peaks in May demand September.
Starting January the demand increases with an increasing rate till it reaches peak in May, then go back
to normal level with increasing rate till the second peak in September then back to normal levels with
minor decrease in November along 2015 demand 2016.
In 2017, the company was operating normally with signs of deterioration started in July, demand started
to drop when compared to 2016 sales.
One approach of evaluating business is by measuring how the company uses its resources effectively
demand getting the best results out of it. Utilization & demand Efficiency are two measures essential to
evaluate organization.
Generally demand forecast is predicted based on historical demand data demand performance. Forecast
value lies in making decision makers look to future objectively. Companies that track their past
performance will be aware of current demand future opportunities demand threats easily by precisely
analyzing historical data to predict future. By conducting demand forecasts we will improve business
with evaluation of past demand estimation of existing demand annual growth, demand let decision
makers compare themselves to industry averages. Moreover it will help on establishing policies allowing
them monitor prices easily demand operating costs to assure profit maximization, demand be aware of
trivial problems before becoming main problems.
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Table 1.2
Graph 1.2
Table 1.2, it specifies the method for computing tracking signals demand MAD where the actual &
forecast demand occurs. Graph 1.2 shows the deviation of the tracking signal that is plotted. Note that it
deviated from -25 MADs to +2.58 MADs. In a flawless forecasting model, the summation of actual
forecast errors would be 0; errors that result in overestimates should be offset by errors that are
underestimates. The tracking signal would then be 0, indicating an unbiased model, neither leading nor
lagging the actual demand. The company determines product production plans demand forecasts using
Actual Forecast Error Cum error Cum abs error Cum Abs MAD Track Signal
January 1500 1600 -100 -100 100 100 100 -1
February 1550 1600 -50 -150 50 150 75 -2
March 2250 2400 -150 -300 150 300 100 -3
April 3350 3200 150 -150 150 450 112.5 -1.333
May 4150 4000 150 0 150 600 120 0
June 1650 1600 50 50 50 650 108.333 0.462
July 1800 1650 150 200 150 800 114.286 1.75
August 2900 3100 -200 0 200 1000 125 0
September 3850 3900 -50 -50 50 1050 116.667 -0.429
October 1950 2000 -50 -100 50 1100 110 -0.909
November 1750 1650 100 0 100 1200 109.091 0
December 1800 1750 50 50 50 1250 104.167 0.48
January 1750 1800 -50 0 50 1300 100 0
February 1850 1800 50 50 50 1350 96.429 0.519
March 2800 2700 100 150 100 1450 96.667 1.552
April 3800 3700 100 250 100 1550 96.875 2.581
May 4500 4600 -100 150 100 1650 97.059 1.545
June 1950 1900 50 200 50 1700 94.444 2.118
July 2000 2100 -100 100 100 1800 94.737 1.056
August 3400 3500 -100 0 100 1900 95 0
September 4550 4500 50 50 50 1950 92.857 0.538
October 2300 2400 -100 -50 100 2050 93.182 -0.537
November 1900 1950 -50 -100 50 2100 91.304 -1.095
December 2100 2000 100 0 100 2200 91.667 0
January 2000 2100 -100 -100 100 2300 92 -1.087
February 1950 2100 -150 -250 150 2450 94.231 -2.653
March 3100 3300 -200 -450 200 2650 98.148 -4.585
April 4350 4400 -50 -500 50 2700 96.429 -5.185
May 4900 5400 -500 -1000 500 3200 110.345 -9.063
June 2000 2300 -300 -1300 300 3500 116.667 -11.143
July 1950 2500 -550 -1850 550 4050 130.645 -14.16
August 3300 4000 -700 -2550 700 4750 148.438 -17.179
September 4350 5200 -850 -3400 850 5600 169.697 -20.036
October 2000 2800 -800 -4200 800 6400 188.235 -22.313
November 1700 2300 -600 -4800 600 7000 200 -24
December 2200 2400 -200 -5000 200 7200 200 -25
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intuitive & subjective judgments. Maybe this is one reason leaded to production inefficiency. When
forecast is inaccurate, the production plan might be unreliable & will result in inventory shortage or
surplus (MUANGKLEING, 2009).
Tracking signal is calculated as the ratio of Cumulative Error divided by the mean absolute deviation. The
cumulative error can be positive or negative, so the Tracking signal can be positive or negative as well.
Tracking Signal should pass a threshold test to be significant. If Tracking Signal > 4 then there is
persistent under forecasting. Alternatively, if tracking signal less than -4 then, there is persistent overforecasting.
The following part is intended to study how to develop a systematic forecasting technique to be efficient
demand effective for the supply chain demand performance of XYZ Company by improving forecasting
accuracy demand minimizing forecasting errors.
Approaches to forecasting:
Mainly there are two approaches of forecasting; qualitative demand quantitative. Qualitative
techniques include soft information (e.g., human aspects, personal opinions, ideas. On the other hand,
Quantitative techniques comprise either the historical data projection or the creation of an associative
model that try to use causal or explanatory variables in doing the forecast.
Types of forecasting techniques:
Judgmental forecasts: depends on analyzing subjective inputs collected from different sources like
surveys.
Time-series forecasts: merely it assume the future output using past data as an input, using historical
data assuming that the future will be like the past.
Associative models: use equations that comprise of one or more explanatory variables can be used to
predict demand. For example, demand for carbon black might be related to variables such as oil prices.
There are two main issues to consider when deciding on the forecasting technique to be used first is the
time frame demand second is the demand behavior.
Time frame of forecasting:
There are three times frames is considered on business context; short term (less than 2 months),
medium term (3 months to 2 years), “they are primarily used to determine production demand delivery
schedules demand to establish inventory levels” (Prenhall, 1996). long term (more than two years) “is
normally used for strategic planning–to establish long-term goals, plan new products for changing
markets, enter new markets, develop new facilities, develop technology, design the supply chain,
demand implement strategic programs such as TQM” (Masaryk university, 2009, p. 7).
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Demand behavior:
Sometimes demand acts in a random & irregular way. Other times it shows expected behavior, with
repetitive patterns or trends, where the forecast might reflect. The three types of demand behavior
are trends, cycles, demand seasonal patterns.
Trend refers to a long-term upward or downward movement in the data. Population shifts, changing
incomes, demand cultural changes often account for such movements. Seasonality refers to short-term,
fairly regular variations generally related to factors such as the calendar or time of day. Restaurants,
supermarkets, demand theaters experience weekly demand even daily “seasonal” variations. Cycles are
wavelike variations of more than one year’s duration. These are often related to a variety of economic,
political, demand even agricultural conditions (Chukwukelue, Daniel, Chuka, & Sunday, 2013)
Figure 1.1
Choosing a forecasting technique:
From graph 1.1 we can conclude that XYZ company demand has a trend with seasonality. Also as we
have historical data from the company we recommend for the company to use time series approach
choosing one or more techniques of the trend or seasonal in figure 1.1 depending on budget dedicated
for the forecast demand the degree of accuracy accepted for the company.
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Forecast accuracy:
“Forecast accuracy is a significant factor when deciding among forecasting alternatives, accuracy is
based on the historical error performance of a forecast” (Stevenson, 2012, p. 78).
The 3 common used measures for accuracy are mean absolute deviation (MAD), mean absolute percent
error (MAPE) demand mean squared error (MSE).
Mean Absolute Deviation The technique for assessing forecasting methods uses the aggregation of
simple mistakes. MAD measures the accuracy of the estimation by averaging the alleged error using
absolute value of each error. MAD can be calculated by below formula (khair, 2017)
Mean Absolute Percentage Error: a close measure that is related to MAD by expressing the magnitude
of the error relative to the magnitude of the demand. MAPE can be calculated by below formula
(Blocher, Mabert, Soni, & Venkataramanan, 2004)
MAPE is easy and simple to understand that’s why it is popular. But does it meet the criteria for a good
measure of error? Referencing to National Research Council (1980), any summary method of error must
meet 5 basic criteria —clarity of presentation, measurement validity, ease of interpretation, support of
statistical evaluation demand reliability as MAPE meets most of above criteria (Bryan, Tayman, &
Swanson, 2015)
Mean Squared Error: simply it is calculated by averaging the deviations of forecast compared to the
actual demand, the deviations are squared, giving higher weight to errors which are farthest from the
actual demand (Blocher, Mabert, Soni, & Venkataramanan, 2004)
Tracking Signal is automatically used to detect when a forecast technique is not producing good
forecasts (Blocher, Mabert, Soni, & Venkataramanan, 2004)
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In this part we would like to evaluate the forecast technique we choose for XYZ company comparing it
with the already used given forecast for 2017 data. We repeated the forecast for 2017 using
multiplicative decomposition technique with the aid of POMQM software below is the results we got:
It’s clear that there is a great difference in the results taking MAPE as an example by the multiplicative
decomposition the error % is 1.83 compared to 16.57 in XYZ company forecast method.
As a general, as long as the tracking signal lies between +/- 4 assume the model is working good. We
plotted the tracking signal for 2017 for each month demand all tracking signal using the multiplicative
decomposition technique lies within acceptable range. Positive tracking signals show that demand is
greater than the forecast. Negative tracking signals show that demand is less than forecast.
Graph 1.3 (company forecast) Graph 1.4 (decomposition forecast)
XYZ FORECAST multiplicative decomposition
MSE 247500 5446.212
MAD 416.667 48.046
MAPE 16.57% 1.83%
-4
-2
4 2 0

1 2 3 4 5 6 7 8 9 10 11 12

-15
-10
-5
0

1 2 3 4 5 6 7 8 9 10 11 12

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Section 2:
Quality control:
“The most common quality definition in manufacturing is conformance, which is the degree to which a
product characteristic meets preset standards. Other common definitions of quality in manufacturing
include performance, such as acceleration of a vehicle; reliability, meaning that the product will function
as expected without failure; features, the extras that are included beyond the basic characteristics;
durability, the expected operational life of the product; demand serviceability, how readily a product
can be repaired” (Reid & Sanders, 2011, p. 152). The importance of these definitions is based on the
likings of each single customer. As every customer perceives quality from different point of view so
every customer defines high product quality differently.
“Quality control is process that evaluates output relative to a standard and takes corrective action when
output doesn’t meet standards” (Stevenson, 2012, p. 419).
“Inspection is an appraisal activity that compares goods or services to a stdemandard” (Stevenson, 2012,
p. 420) demand it can occur at 3 points before, after or during production. Inspection made before
demand after production comprises acceptance sample while inspection during production comprises
process control.
Quality of conformance of a process is most thing quality control is concerned about where quality
conformance is the product or service conformity with preset specifications where “Statistical Process
Control is an analytical decision making tool which allows you to see when a process is working correctly
demand when it is not. Variation is present in any process, deciding when the variation is natural
demand when it needs correction is the key to quality control.” (Statit Software, Inc., 2007, p. 2).
Control charts are a vital tool of constant quality control. It monitors processes showing process is
performance demand how the process demand capabilities are affected by changes to the process, then
the output information is used in making quality improvements. Also control charts are used to govern
the capability of the process as they can help in detecting special or assignable causes for factors that
hinder peak performance (Statit Software, Inc., 2007).
At XYZ Quality is emphasized, employees are capable of applying quality concepts demand using quality
tools. We have collected 20 samples from the XYZ production process time each with 5 observations
demand by using POMQM software we calculated upper demand lower control limits for the process
demand plotted a control chart.
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Graph 3.1 X bar chart
Graph 3.2 range chart
X bar demand Range control charts are usually displayed together for analysis of quality control. The Xbar chart is graphically representing the variation among subgroup averages.
Range chart mainly looks at variability among subgroups, where Variation within subgroups is presented
by the range. The range of amounts per subgroup was plotted on the Y-axis of the Range chart. On the
charts centerline is the average or mean of the range.
Visually analyzing above graph 3.1 demand 3.2 we can predict that process is in control since data falls
between the upper & lower control limits. Points that considered being too extreme to be named
random are examined by control charts. Though, even if points are entirely within control limits, the
data may not reflect a random process. On taking a final decision whether the process in control or out
of control we need to make run test checking for patterns in sequence.
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Run test:
We used excel sheet to calculate runs test that is a test of randomness, we used each sample mean for
the 20 samples. We started by calculating median then compared it to each sample mean using if
function if the mean is above median then write A if below median write B. Then we counted the runs
that are equals to 14; where n is the sum of both runs equals to 20. Then we calculated the mean,
variance, SD, z & p-value using below equation
Mean= (2AB/n) +1
Variance= (2AB(2AB-n))/((n^2)(n-1))
SD= square root variance
Alpha= its 5 % according to social sciences
Z= Absolute value of number of runs – mean / SD
p-value= 2*(1-norm.s.dist(z,true cumulative distribution function)
p value tells us the percentage chance that we have could have made the observations we made
through random error alone.
Since we got p-value 10.29 % which is greater than alpha that is 5% so the process is random.
(www.youtube.com, 2017)
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Section 3:
Capacity planning:
Capacity refers to the maximum an operating unit can achieve, where capacity planning aims to match
between projected level of demand & supply capabilities. The main questions in planning capacities are
the following:
1. What is the kind of capacity needed?
2. How much needed to match demand and using forecasted demand?
3. When is it needed?
Capacity can be classified by function as we have 2 main types of capacity design capacity “The
maximum output rate or service capacity an operation, process, or facility is designed for”; Effective
capacity “Design capacity minus allowances such as personal time, demand maintenance”; Actual
output “rate of output actually achieved–cannot exceed effective capacity” (Stevenson, 2012, p. 186)
XYZ Company has design capacity of 42000 tons/year; effective capacity 38000 tons/year demand the
actual O/P of 30000 tons/year when operating for one shift (8 working hours). Efficiency is 78.94%
demand utilization is 71.42%. Capacity measures are beneficial in defining 2 measures of system
effectiveness: efficiency demand utilization. Efficiency “is the ratio of actual output to effective
capacity”; Capacity utilization “is the ratio of actual output to design capacity” (Stevenson, 2012, p. 187).
Comparing effective capacity of 38000 tons/ year to 30000 tons /year of actual output looks pretty
good. Though, comparing it to design capacity of 42000 tons/year is less impressive although probably
more meaningful. Because effective capacity acts as a cap to actual output, the key to capacity
utilization improvement is by increasing effective capacity by improving quality problems, maintaining
machines at a good operating condition, training employees, and utilizing bottle neck equipment.
Hence, increasing utilization is positively correlated on the ability to increase effective capacity, demand
this entails knowledge of what is limiting effective capacity.
Most manufacturers have several operations demand often its effective capacities are not typical
leading to bottlenecks “the resource that requires the longest time in operations of the supply chain for
certain demand” (wikipedia, 2018).
In order to increase XYZ company efficiency one of the aspect that needs some adjustments is utilizing
bottleneck equipment. We assisted XYZ production process using POMQM software below is the results
using 135tons/ day / 8 hours for current situation demand recommended scenario:
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Cycle time is “the maximum time allowed at each workstation to complete its set of tasks on a unit”
(J.stevenson, 2005, p. 238). We can see in table 4.1 that cycle time is reduced from 213sec to only
180sec in order to clarify its importance we need to calculate output capacity by dividing operating time
per day / cycle time
Output capacity increased from 135tons/day to 160tons/day by just solving bottle neck problem. Also
idle time which is the unproductive time workers forced to wait beyond their control as it expresses for
how long a worker won’t be able to do anything, as he must wait for another resource decreased from
127sec to 60sec only. As a conclusion, efficiency has increased from 70.31% to 83.33%.m
current situation recommended
Cycle time 213.33 180 Seconds
Min (theoretical) # of stations 2 2
Idle time (allocated-needed) 126.67 60 Seconds/cycle
Efficiency (needed/allocated) 70.31% 83.33%
Balance Delay (1-efficiency) 29.69% 16.67%
operating time/day seconds cycle time output capacity
current situation 28800 213 135
recommended 28800 180 160
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Section 4:
Suppliers:
XYZ Company has 30 local demand international suppliers for parts demand materials. XYZ choose
suppliers according to preset criteria (ex., quality, service levels, delivery, cost, etc). Both international
demand local suppliers must be ISO 9000 certified and updating their supplier list annually. The top
management is much more concerned on strategic posture of low cost as a result procurement team
main goal is to comply to the strategy with all the suppliers compromising to other competitive factors.
One third of the suppliers are international suppliers with 8 million spent on them of which 90% are
from only two suppliers. On the other hand, local supplier spent amount is only 3 million where 75%
coming from 3 suppliers.
The main issue facing XYZ is getting material from international suppliers consume a lot of time with
long lead times reaching 20 days. XYZ now is in the process of deciding on insourcing one material from
one or two international suppliers. Below is collected primary data about different costs of each
alternative as shown in below table:
It’s very important to take into consideration other aspects like (quality, delivery demand flexibility)
needs to when deciding with to insource or outsource.
In order to make such decision will make use cost volume analysis, as “it focuses on relationships
between cost, revenue, demand volume of output” (Stevenson, 2012, p. 200). The aim of cost–volume
analysis is to estimate organization income under diverse operating conditions.
The use of this technique requires identifying all costs incurred to given product production. These costs
then are differentiated as either fixed or variable costs. Fixed costs are constant regardless output
volume (example: rent, etc.). Variable costs are directly correlated with output volume. The main
elements of variable costs are materials demand labor costs (Stevenson, 2012).
Below are some equations are essential to understand on using cost volume analysis:
Total cost (TC) = Fixed cost (FC) + variable cost (VC)
VC= quantity (Q) * variable cost/unit (v)
Total revenue (TR) = revenue/unit (R)*Q
Profit (P) = TR – TC
outsource Insource
Fixed
cost
None $1,000,000
Variable
cost
$ 900/ton $ 100/ton
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The break-even point is a point where P equals to zero demand TR equals to TC so at this point
companies neither making profit nor making losses, so any amount above this point is considered as
profit demand any point below this point is considered as loss.
Using the POMQM software we calculated the break-even point amounting to 1250 tons at this point
the company is neither profiting nor losing.
Graph 3.1
On deciding whether to outsource or insource, when need to interpret above graph; where option 1 is
outsource demand option 2 is insource. We can put it as the following if quantity produced will be less
than 1250 tons then outsource since it has lower total cost. On the contrary, if quantity produced will be
greater than 1250 tons then it’s better to insource since its cost is lower.
The company average demand per year for the 2015-2017 is 31733tons/year as a result will would
recommend that the company change one or two international supplier with local ones taking in to
consideration the quality demand time for the this decision.
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Section5:
Inventory management:
Inventory is the store for goods or material as inventory can be divided in too 3 main categories raw
materials, work in process or semi-finished goods and finished goods. Inventories are important part of
business. Inventories are not just important for the smoothness of operations but also it related to
customer satisfaction. That’s why the inadequate control over inventory might results in lost sales,
dissatisfied customers, missed delivery, over stocking, bottle neck production, etc. As a result inventory
management is concerned with two man areas level of customer satisfaction & costs of ordering and
carrying inventory.as a result top management main concern is to balance between customer
satisfaction & cost of inventory (Stevenson, 2012).
In order to effectively manage inventory mangers must keep records of all in hand inventory and on
order inventories, have a reliable forecasted demand to be able to decide on raw materials purchases
and other expenses and cost, know lead times & its variability, have estimation of carrying, ordering
&shortage costs and finally a classification system for inventory (Stevenson, 2012).
Since inventories are used mainly to satisfy demand, then manager must have a reliable forecast of
demand amount and timing. In addition, mangers should know how lead time which is the time
between submitting & receiving orders & demand varies as the greater the variability the greater the
need for more stocking.
Inventory has different types of cost related to it as mentioned previously, purchasing cost “The amount
paid to buy the inventory”; Holding (carrying) cost “cost to carry an item in inventory for a length of
time, usually a year”; Ordering costs “Costs of ordering and receiving inventory”; Shortage costs “Costs
resulting when demand exceeds the supply of inventory; often unrealized profit per unit” (Stevenson,
2012, p. 562).
In inventory management ordering policies are very important issues deciding on how much & when to
place an order is a crucial issue. So it is important to understand these two definitions Cycle stock “the
amount of inventory needed to meet expected demand”; Safety stock “Extra inventory carried to
reduce the probability of a stock out due to demand and/or lead time variability” (Stevenson, 2012, p.
563)
The company strategy is to minimize inventory amount, adopted point-of-use replenishment for some
areas of operations, having deliveries come directly to the production floor. Though, it is needed to
reevaluate this strategy for international purchases. The current model of ordering demand inventory
ignores calculated economic order as well as quality discounts that might be offered.
Economic order quantity (EOQ) “The order size that minimizes total annual cost” (Stevenson, 2012, p.
566). There are several models on calculating EOQ the best model to be applied for the XYZ Company is
quantity discounts model -according to collected data- meaning that large orders get higher discounts.
19
When quantity discounts offered, buyers weigh the potential paybacks of decreased purchase price &
fewer orders resulting from purchasing in big quantities versus carrying costs increase caused by greater
average inventories. Buyers aim is to choose order quantity that will minimize total cost; as the total
cost includes carrying cost + ordering cost + purchasing cost.
Using POMQM software we have calculated EOQ that is equals to 500tons/time, this quantity is the
optimal quantity to decrease the overall total cost.
Comparing the total costs XYZ is used to purchase as 350ton/time to 500ton/time
350ton TC =$721697.1 & 500ton TC = $661720 then XYZ saved $59977.1.
Range Q* (Square root formula) Order Quantity Total Setup Cost Total Holding Cost Total Unit Cost Total Cost
1 to 349 424.26 NA NA NA NA NA
350 to 449 424.26 424.26 848.53 848.53 720000 721697.1
450 to 499 424.26 450 800 900 690000 691700
500 to 999999 424.26 500 720 1000 660000 661720
20
Section 6:
Order fulfillment:
XYZ aim is to improve performance of delivery & lowering the finished goods stock. XYZ got complaints
from customers about lost orders and the required time to such orders.
As the Marketing Director acknowledged based on market surveys that customer aren’t satisfied with
delivery terms & continuous delays on delivery. Manager decided to make an initial review to flow of
customer orders.
XYZ can efficiently manage & improve the business process by understanding & developing flowchart of
process relationships & activities making up the business process.
Recognise focal point of mapping effort. As the mapping effort must emphasis on the activities & flows
associated with company movement through the process and detect clear boundaries, starting & ending
points.
On assessing order fulfillment we found below:
customer order is sent to XYZ either by fax or e-mail; One out of 100 orders gets lost accidently to solve
this issue we will assign only one employees that will be responsible for receiving orders through emails
and by automated system the employee will forward order to department in charge for processing the
order and by a queueing system the processing employee will not being able to take another order
21
unless finishing the inventory check and handle it to inspector that records the receiving of processed
order and he will be the one responsible for queue flow after receiving the order. Then inspector
handles order to delivery. Also we must set a floor for orders accomplished by each employee and
correlate number of orders to monetary incentive.
22
Section 7:
Aggregate planning:
Is considered as intermediate-range planning for capacity that covers the time horizon of 2 to 12 months
.It is mainly useful to organizations experiencing seasonal or by effectively utilizing organization’s
resources to match expected demand. Planners usually make decisions on, employment levels, output
rates, backorders, inventory levels and subcontracting.
There are different aggregate planning strategies in order to balance supply & demand. Proactive
strategy attempting to adjust demand to match capacity; Reactive strategy attempting to adjust
capacity to match demand; mixed strategy Comprises elements of both strategies.
We forecasted 2018 demand using POMQM software using time series approach and multiplicative
decomposition as the demand is seasonal with trend; the actual demand for the previous 36 months to
make the forecast.
Conclusion:The reason of engagement is to help improving forecast accuracy, waste minimization,
inventory & quality management etc. This report is preparation is based on information that is given in a
case study. Consequently, findings are exclusively on data & information provided. We recommend the
company consider the following points
Prepare 2019 forecast with minimum growth of 30%.
 Make sales campaigns by giving quantity discount.
 Announce incentive policy for the top achievers.
 Improve delivery time by reducing process delays.
 Using EOQ on inventory management.
 Managing bottle necks that may arise.
Demand
Regular time
Capacity
Overtime
Capacity
Regular time
production
Overtime
production
Inventory
(end PD) Shortage (end PD)
Initial Inventory 0
Period 1 2026 2400 0 2400 0 374 0
Period 2 2060 2400 1000 2400 761 1475 0
Period 3 3190 2400 1000 2400 761 1446 0
Period 4 4410 2400 1000 2400 761 197 0
Period 5 5120 2400 1806 2400 761 0 1762
Period 6 2159 2400 0 2400 0 0 1521
Total(units) 18965 14400 4806 14400 3044 3492 3283
@$90 /unit @$100 /unit @$20 /unit @$0 /unit
Subtotal Costs $1,296,000 $304,400 $69,840 $0
Total Cost $1,670,240
23
Appendix:
2015 error analysis:
Error Measures
Bias (Mean Error) 4.17
MAD (Mean Absolute Deviation) 104.17
MSE (Mean Squared Error) 13541.67
Standard Error (denom=n-2=10) 127.48
MAPE (Mean Absolute Percent Error) 4.61%
ACTUAL FORECAST ERROR IERRORI ERROR^2 I%ERRORI
January 1500 1600 -100 100 10000 6.67%
February 1550 1600 -50 50 2500 3.23%
March 2250 2400 -150 150 22500 6.67%
April 3350 3200 150 150 22500 4.48%
May 4150 4000 150 150 22500 3.61%
June 1650 1600 50 50 2500 3.03%
July 1800 1650 150 150 22500 8.33%
August 2900 3100 -200 200 40000 6.90%
September 3850 3900 -50 50 2500 1.30%
October 1950 2000 -50 50 2500 2.56%
November 1750 1650 100 100 10000 5.71%
December 1800 1750 50 50 2500 2.78%
TOTALS 28500 50 1250 162500 55.27%
AVERAGE 2375 4.167 104.167 13541.67 4.61%
(Bias) (MAD) (MSE) (MAPE)
Std err 127.476
ACTUAL FORECAST ERROR CUM ERROR CUM ABS ERROR CUM ABS MAD TRACK SIGNAL
January 1500 1600 -100 -100 100 100 100 -1
February 1550 1600 -50 -150 50 150 75 -2
March 2250 2400 -150 -300 150 300 100 -3
April 3350 3200 150 -150 150 450 112.5 -1.333
May 4150 4000 150 0 150 600 120 0
June 1650 1600 50 50 50 650 108.333 0.462
July 1800 1650 150 200 150 800 114.286 1.75
August 2900 3100 -200 0 200 1000 125 0
September 3850 3900 -50 -50 50 1050 116.667 -0.429
October 1950 2000 -50 -100 50 1100 110 -0.909
November 1750 1650 100 0 100 1200 109.091 0
December 1800 1750 50 50 50 1250 104.167 0.48
24
2016 error analysis:
Error Measures
Bias (Mean Error) -4.167
MAD (Mean Absolute Deviation) 79.167
MSE (Mean Squared Error) 6875
Standard Error (denom=n-2=10) 90.83
MAPE (Mean Absolute Percent Error) 3.11%
ACTUAL FORECAST ERROR IERRORI ERROR^2 I%ERRORI
January 1750 1800 -50 50 2500 2.86%
February 1850 1800 50 50 2500 2.70%
March 2800 2700 100 100 10000 3.57%
April 3800 3700 100 100 10000 2.63%
May 4500 4600 -100 100 10000 2.22%
June 1950 1900 50 50 2500 2.56%
July 2000 2100 -100 100 10000 5%
August 3400 3500 -100 100 10000 2.94%
September 4550 4500 50 50 2500 1.10%
October 2300 2400 -100 100 10000 4.35%
November 1900 1950 -50 50 2500 2.63%
December 2100 2000 100 100 10000 4.76%
TOTALS 32900 -50 950 82500 37.33%
AVERAGE 2741.667 -4.167 79.167 6875 3.11%
(Bias) (MAD) (MSE) (MAPE)
Std err 90.83
ACTUAL FORECAST ERROR CUM ERROR CUM ABS ERROR CUM ABS MAD TRACK SIGNAL
January 1750 1800 -50 -50 50 50 50 -1
February 1850 1800 50 0 50 100 50 0
March 2800 2700 100 100 100 200 66.667 1.5
April 3800 3700 100 200 100 300 75 2.667
May 4500 4600 -100 100 100 400 80 1.25
June 1950 1900 50 150 50 450 75 2
July 2000 2100 -100 50 100 550 78.571 0.636
August 3400 3500 -100 -50 100 650 81.25 -0.615
September 4550 4500 50 0 50 700 77.778 0
October 2300 2400 -100 -100 100 800 80 -1.25
November 1900 1950 -50 -150 50 850 77.273 -1.941
December 2100 2000 100 -50 100 950 79.167 -0.632
25
2017 error analysis:
Error Measures
Bias (Mean Error) -416.667
MAD (Mean Absolute Deviation) 416.667
MSE (Mean Squared Error) 247500
Standard Error (denom=n-2=10) 544.977
MAPE (Mean Absolute Percent Error) 16.57%
ACTUAL FORECAST ERROR IERRORI ERROR^2 I%ERRORI
January 2000 2100 -100 100 10000 5%
February 1950 2100 -150 150 22500 7.69%
March 3100 3300 -200 200 40000 6.45%
April 4350 4400 -50 50 2500 1.15%
May 4900 5400 -500 500 250000 10.20%
June 2000 2300 -300 300 90000 15%
July 1950 2500 -550 550 302500 28.21%
August 3300 4000 -700 700 490000 21.21%
September 4350 5200 -850 850 722500 19.54%
October 2000 2800 -800 800 640000 40%
November 1700 2300 -600 600 360000 35.29%
December 2200 2400 -200 200 40000 9.09%
TOTALS 33800 -5000 5000 2970000 198.84%
AVERAGE 2816.667 -416.667 416.667 247500 16.57%
(Bias) (MAD) (MSE) (MAPE)
Std err 544.977
ACTUAL FORECAST ERROR CUM ERROR CUM ABS ERROR CUM ABS MAD TRACK SIGNAL
January 2000 2100 -100 -100 100 100 100 -1
February 1950 2100 -150 -250 150 250 125 -2
March 3100 3300 -200 -450 200 450 150 -3
April 4350 4400 -50 -500 50 500 125 -4
May 4900 5400 -500 -1000 500 1000 200 -5
June 2000 2300 -300 -1300 300 1300 216.667 -6
July 1950 2500 -550 -1850 550 1850 264.286 -7
August 3300 4000 -700 -2550 700 2550 318.75 -8
September 4350 5200 -850 -3400 850 3400 377.778 -9
October 2000 2800 -800 -4200 800 4200 420 -10
November 1700 2300 -600 -4800 600 4800 436.364 -11
December 2200 2400 -200 -5000 200 5000 416.667 -12
26
2017 repeated forecast using decomposition:
Measure Value
Future
Period
Unadjust
ed
Forecast
Seasonal
Factor Adjusted Forecast
Error Measures 25 2941.726 0.709 2085.939
Bias (Mean Error) 9.915 26 2972.827 0.699 2077.54
MAD (Mean Absolute Deviation) 48.046 27 3003.928 1.028 3086.974
MSE (Mean Squared Error) 5446.212 28 3035.029 1.384 4201.374
Standard Error (denom=n-2-12=10) 114.328 29 3066.13 1.704 5224.328
MAPE (Mean Absolute Percent Error) 1.83% 30 3097.231 0.699 2166.492
Regression line (unadjusted forecast) 31 3128.332 0.76 2376.982
Demand(y) = 2164.2 32 3159.433 1.213 3830.884
+ 31.101 * time 33 3190.534 1.59 5074.491
Statistics 34 3221.635 0.795 2560.268
Correlation coefficient 0.998 35 3252.736 0.704 2289.004
Coefficient of determination (r^2) 0.995 36 3283.837 0.715 2349.572
37 3314.938 0.709 2350.579
38 3346.039 0.699 2338.356
Demand(y) time CTD MA RATIO SEASONAL SMOOTHEDUnadjusted forecast (E-Ebar)^2 Error |Error| Error^2 |Pct Error|
January 1500 1 0.709 2115.397 2195.301 1556.659 -56.659 56.659 3210.235 3.78%
February 1550 2 0.699 2217.951 2226.402 1555.906 -5.906 5.906 34.881 0.38%
March 2250 3 1.028 2189.47 2257.503 2319.913 -69.913 69.913 4887.874 3.11%
April 3350 4 1.384 2420.006 2288.604 3168.102 181.898 181.898 33086.95 5.43%
May 4150 5 1.704 2435.613 2319.705 3952.508 197.492 197.492 39003.26 4.76%
June 1650 6 0.699 2358.851 2350.806 1644.373 5.627 5.627 31.664 0.34%
July 1800 7 2387.5 0.754 0.76 2368.969 2381.907 1809.831 -9.831 9.831 96.645 0.55%
August 2900 8 2410.417 1.203 1.213 2391.708 2413.008 2925.827 -25.827 25.827 667.042 0.89%
September 3850 9 2439.583 1.578 1.59 2420.648 2444.109 3887.315 -37.315 37.315 1392.387 0.97%
October 1950 10 2472.917 0.789 0.795 2453.723 2475.21 1967.076 -17.076 17.076 291.604 0.88%
November 1750 11 2506.25 0.698 0.704 2486.797 2506.312 1763.732 -13.732 13.732 188.579 0.79%
December 1800 12 2535.417 0.71 0.715 2515.738 2537.413 1815.508 -15.508 15.508 240.507 0.86%
January 1800 13 2558.333 0.704 0.709 2538.477 2568.514 1821.299 -21.299 21.299 453.645 1.18%
February 1800 14 2595.833 0.693 0.699 2575.685 2599.615 1816.723 -16.723 16.723 279.647 0.93%
March 2700 15 2647.917 1.02 1.028 2627.365 2630.716 2703.444 -3.444 3.444 11.858 0.13%
April 3700 16 2693.75 1.374 1.384 2672.842 2661.817 3684.738 15.262 15.262 232.942 0.41%
May 4600 17 2720.833 1.691 1.704 2699.715 2692.918 4588.418 11.582 11.582 134.143 0.25%
June 1900 18 2737.5 0.694 0.699 2716.253 2724.019 1905.432 -5.432 5.432 29.509 0.29%
July 2100 19 0.76 2763.797 2755.12 2093.406 6.594 6.594 43.474 0.31%
August 3500 20 1.213 2886.544 2786.221 3378.356 121.644 121.644 14797.33 3.48%
September 4500 21 1.59 2829.329 2817.322 4480.903 19.097 19.097 364.702 0.42%
October 2400 22 0.795 3019.967 2848.423 2263.672 136.328 136.328 18585.22 5.68%
November 1950 23 0.704 2771.003 2879.524 2026.368 -76.368 76.368 5832.078 3.92%
December 2000 24 0.715 2795.264 2910.625 2082.54 -82.54 82.54 6812.898 4.13%
TOTALS 61450 237.951 1153.099 130709.1 43.85%
AVERAGE 2560.417 9.915 48.046 5446.212 1.83%
Next period forecast 2085.939 (Bias) (MAD) (MSE) (MAPE)
Std err 114.328
27
Quality:
Demand(y) Forecast Error Cum error Cum abs error Cum Abs MAD Track Signal
January 1500 1556.659 -56.659 -56.659 56.659 56.659 56.659 -1
February 1550 1555.906 -5.906 -62.565 5.906 62.565 31.282 -2
March 2250 2319.913 -69.913 -132.478 69.913 132.478 44.159 -3
April 3350 3168.102 181.898 49.42 181.898 314.377 78.594 0.629
May 4150 3952.508 197.492 246.912 197.492 511.869 102.374 2.412
June 1650 1644.373 5.627 252.539 5.627 517.496 86.249 2.928
July 1800 1809.831 -9.831 242.709 9.831 527.327 75.332 3.222
August 2900 2925.827 -25.827 216.882 25.827 553.154 69.144 3.137
September 3850 3887.315 -37.315 179.567 37.315 590.469 65.608 2.737
October 1950 1967.076 -17.076 162.49 17.076 607.545 60.755 2.675
November 1750 1763.732 -13.732 148.758 13.732 621.278 56.48 2.634
December 1800 1815.508 -15.508 133.25 15.508 636.786 53.065 2.511
January 1800 1821.299 -21.299 111.951 21.299 658.085 50.622 2.212
February 1800 1816.723 -16.723 95.228 16.723 674.807 48.201 1.976
March 2700 2703.444 -3.444 91.784 3.444 678.251 45.217 2.03
April 3700 3684.738 15.262 107.047 15.262 693.513 43.345 2.47
May 4600 4588.418 11.582 118.629 11.582 705.096 41.476 2.86
June 1900 1905.432 -5.432 113.197 5.432 710.528 39.474 2.868
July 2100 2093.406 6.594 119.79 6.594 717.121 37.743 3.174
August 3500 3378.356 121.644 241.434 121.644 838.766 41.938 5.757
September 4500 4480.903 19.097 260.532 19.097 857.863 40.851 6.378
October 2400 2263.672 136.328 396.859 136.328 994.19 45.19 8.782
November 1950 2026.368 -76.368 320.491 76.368 1070.558 46.546 6.885
December 2000 2082.54 -82.54 237.951 82.54 1153.099 48.046 4.953
28
Economic order quantity:
Sample Mean Range 3 sigma (99.73%) X-bar Chart Range Chart
Sample 1 43.4 8 UCL (Upper control limit) 48.8244 15.228
Sample 2 44.8 8 CL (Center line) 44.67 7.2
Sample 3 43.2 7 LCL (Lower Control Limit) 40.5156 0
Sample 4 43.8 7
Sample 5 45.2 9
Sample 6 44.6 8
Sample 7 45 8
Sample 8 45.6 8
Sample 9 43.4 6
Sample 10 44.8 5
Sample 11 44.8 6
Sample 12 46.4 9
Sample 13 45.4 7
Sample 14 44 8
Sample 15 44.8 9
Sample 16 44.4 7
Sample 17 45.2 7
Sample 18 44.6 6
Sample 19 45.2 5
Sample 20 44.8 6
Averages 44.67 7.2
Parameter Value Parameter Value
Demand rate(D) 3000 xxxxxxx xxxxxxx Optimal order quantity (Q*) 500
Setup/ordering cost(S) 120 xxxxxxx xxxxxxx Maximum Inventory Level (Imax) 500
Holding/carrying cost(H) 4 xxxxxxx xxxxxxx Average inventory 250
Orders per period(year) 6
From To Price Annual Setup cost 720
1 1 349 250 Annual Holding cost 1000
2 350 449 240
3 450 499 230 Unit costs (PD) 660000
4 500 999999 220 Total Cost (including units) 661720
Range Q* (Square root formula) Order Quantity Total Setup Cost Total Holding Cost Total Unit Cost Total Cost
1 to 349 424.26 NA NA NA NA NA
350 to 449 424.26 424.26 848.53 848.53 720000 721697.1
450 to 499 424.26 450 800 900 690000 691700
500 to 999999 424.26 500 720 1000 660000 661720
29
Capacity planning:
Station Task Time (Seconds) Time left (Seconds) Ready tasks
A,B,C
1 A 20 B,C
B 10 C
C 5 D
D 30 E
E 10 F,G
F 30 G
G 30 H
H 45 I
2 I 90 J
J 30
Summary Statistics 93.33
Cycle time 213.33 Seconds
Min (theoretical) # of stations 2
Actual # of stations 2
Time allocated (cycle time * # stations) 426.67 Seconds/cycle
Time needed (sum of task times) 300 Seconds/unit
Idle time (allocated-needed) 126.67 Seconds/cycle
Efficiency (needed/allocated) 70.31%
Balance Delay (1-efficiency) 29.69%
Station Task Time (Seconds) Time left (Seconds) Ready tasks
1 A 20 160 B,C
B 10 150 C
C 5 145 D
D 30 115 E
E 10 105 F,G
F 30 75 G
G 30 45 H
H 45 0 I
2 I 90 90 J
J 30 60
Summary Statistics
Cycle time 180 Seconds
Time allocated (cyc*sta) 360 Seconds/cycle
Time needed (sum task) 300 Seconds/unit
Idle time (allocated-needed) 60 Seconds/cycle
Efficiency (needed/allocated) 83.33%
Balance Delay (1-efficiency) 16.67%
Min (theoretical) # of stations 2
current situation recommended
Cycle time 213.33 180 Seconds
Min (theoretical) # of stations 2 2
Idle time (allocated-needed) 126.67 60 Seconds/cycle
Efficiency (needed/allocated) 70.31% 83.33%
Balance Delay (1-efficiency) 29.69% 16.67%
30
Aggregate planning:
Measure Value Future Period
Unadjusted
Forecast Seasonal Factor Adjusted Forecast
Error Measures 37 2967.037 0.683 2025.482
Bias (Mean Error) 3.818 38 2984.584 0.69 2059.595
MAD (Mean Absolute Deviation) 115.235 39 3002.131 1.062 3189.308
MSE (Mean Squared Error) 20545.8 40 3019.678 1.46 4410.22
Standard Error (denom=n-2-12=22) 183.359 41 3037.225 1.686 5119.479
MAPE (Mean Absolute Percent Error) 4.57% 42 3054.771 0.707 2159.051
Regression line (unadjusted forecast) 43 3072.318 0.734 2256.281
Demand(y) = 2317.805 44 3089.865 1.206 3727.14
+ 17.547 * time 45 3107.412 1.592 4945.497
Statistics 46 3124.959 0.794 2479.666
Correlation coefficient 0.99 47 3142.506 0.674 2118.074
Coefficient of determination (r^2) 0.981 48 3160.052 0.712 2251.209
49 3177.599 0.683 2169.225
50 3195.146 0.69 2204.899
Demand(y)time CTD MA RATIO SEASONAL SMOOTHEDUnadjusted forecast (E-Ebar)^2 Error |Error| Error^2 |Pct Error|
January 1500 1 0.683 2197.282 2335.35 1594.255 -94.255 94.255 8883.914 6.28%
February 1550 2 0.69 2246.124 2352.9 1623.683 -73.683 73.683 5429.182 4.75%
March 2250 3 1.062 2117.95 2370.45 2518.238 -268.238 268.238 71951.38 11.92%
April 3350 4 1.46 2293.745 2387.99 3487.647 -137.647 137.647 18946.69 4.11%
May 4150 5 1.686 2462.063 2405.54 4054.724 95.276 95.276 9077.586 2.30%
June 1650 6 0.707 2334.531 2423.09 1712.588 -62.588 62.588 3917.29 3.79%
July 1800 7 2385.417 0.755 0.734 2451.012 2440.63 1792.378 7.622 7.622 58.102 0.42%
August 2900 8 2408.333 1.204 1.206 2404.152 2458.18 2965.171 -65.171 65.171 4247.214 2.25%
September 3850 9 2443.75 1.575 1.592 2419.077 2475.73 3940.158 -90.158 90.158 8128.501 2.34%
October 1950 10 2485.417 0.785 0.794 2457.456 2493.27 1978.421 -28.421 28.421 807.727 1.46%
November 1750 11 2518.75 0.695 0.674 2596.408 2510.82 1692.313 57.688 57.688 3327.848 3.30%
December 1800 12 2545.833 0.707 0.712 2526.684 2528.37 1801.199 -1.199 1.199 1.438 0.07%
January 1750 13 2566.667 0.682 0.683 2563.496 2545.91 1737.997 12.003 12.003 144.07 0.69%
February 1850 14 2595.833 0.713 0.69 2680.857 2563.46 1768.987 81.013 81.013 6563.116 4.38%
March 2800 15 2645.833 1.058 1.062 2635.671 2581.01 2741.928 58.072 58.072 3372.388 2.07%
April 3800 16 2689.583 1.413 1.46 2601.86 2598.55 3795.171 4.829 4.829 23.316 0.13%
May 4500 17 2710.417 1.66 1.686 2669.707 2616.1 4409.642 90.358 90.358 8164.552 2.01%
June 1950 18 2729.167 0.715 0.707 2758.992 2633.65 1861.409 88.591 88.591 7848.312 4.54%
July 2000 19 2752.083 0.727 0.734 2723.347 2651.19 1947.012 52.988 52.988 2807.719 2.65%
August 3400 20 2766.667 1.229 1.206 2818.66 2668.74 3219.16 180.84 180.84 32702.96 5.32%
September 4550 21 2783.333 1.635 1.592 2858.909 2686.29 4275.271 274.729 274.729 75476.02 6.04%
October 2300 22 2818.75 0.816 0.794 2898.538 2703.84 2145.502 154.498 154.498 23869.57 6.72%
November 1900 23 2858.333 0.665 0.674 2818.958 2721.38 1834.233 65.767 65.767 4325.294 3.46%
December 2100 24 2877.083 0.73 0.712 2947.798 2738.93 1951.203 148.797 148.797 22140.66 7.09%
January 2000 25 2877.083 0.695 0.683 2929.71 2756.48 1881.74 118.261 118.261 13985.54 5.91%
February 1950 26 2870.833 0.679 0.69 2825.768 2774.02 1914.291 35.709 35.709 1275.123 1.83%
March 3100 27 2858.333 1.085 1.062 2918.064 2791.57 2965.618 134.382 134.382 18058.54 4.34%
April 4350 28 2837.5 1.533 1.46 2978.445 2809.12 4102.696 247.304 247.304 61159.37 5.69%
May 4900 29 2816.667 1.74 1.686 2907.014 2826.66 4764.561 135.44 135.44 18343.85 2.76%
June 2000 30 2812.5 0.711 0.707 2829.735 2844.21 2010.23 -10.23 10.23 104.655 0.51%
July 1950 31 0.734 2655.263 2861.76 2101.647 -151.647 151.647 22996.73 7.78%
August 3300 32 1.206 2735.759 2879.3 3473.15 -173.15 173.15 29980.97 5.25%
September 4350 33 1.592 2733.243 2896.85 4610.384 -260.384 260.384 67799.72 5.99%
October 2000 34 0.794 2520.468 2914.4 2312.584 -312.584 312.584 97708.75 15.63%
November 1700 35 0.674 2522.225 2931.94 1976.153 -276.153 276.153 76260.73 16.24%
December 2200 36 0.712 3088.169 2949.49 2101.206 98.794 98.794 9760.243 4.49%
TOTALS 95200 137.453 4148.466 739649 164.49%
AVERAGE 2644.444 3.818 115.235 20545.8 4.57%
Next period forecast 2025.482 (Bias) (MAD) (MSE) (MAPE)
Std err 183.359
31
Demand(y)Forecast Error Cum error Cum abs error Cum Abs MAD Track Signal
January 1500 1594.255 -94.255 -94.255 94.255 94.255 94.255 -1
February 1550 1623.683 -73.683 -167.938 73.683 167.938 83.969 -2
March 2250 2518.238 -268.238 -436.175 268.238 436.175 145.392 -3
April 3350 3487.647 -137.647 -573.822 137.647 573.822 143.456 -4
May 4150 4054.724 95.276 -478.546 95.276 669.098 133.82 -3.576
June 1650 1712.588 -62.588 -541.134 62.588 731.687 121.948 -4.437
July 1800 1792.378 7.622 -533.512 7.622 739.309 105.616 -5.051
August 2900 2965.171 -65.171 -598.682 65.171 804.48 100.56 -5.953
September 3850 3940.158 -90.158 -688.84 90.158 894.638 99.404 -6.93
October 1950 1978.421 -28.421 -717.261 28.421 923.059 92.306 -7.77
November 1750 1692.313 57.688 -659.573 57.688 980.746 89.159 -7.398
December 1800 1801.199 -1.199 -660.772 1.199 981.945 81.829 -8.075
January 1750 1737.997 12.003 -648.769 12.003 993.948 76.458 -8.485
February 1850 1768.987 81.013 -567.756 81.013 1074.961 76.783 -7.394
March 2800 2741.928 58.072 -509.684 58.072 1133.033 75.536 -6.748
April 3800 3795.171 4.829 -504.856 4.829 1137.862 71.116 -7.099
May 4500 4409.642 90.358 -414.498 90.358 1228.22 72.248 -5.737
June 1950 1861.409 88.591 -325.907 88.591 1316.81 73.156 -4.455
July 2000 1947.012 52.988 -272.919 52.988 1369.798 72.095 -3.786
August 3400 3219.16 180.84 -92.079 180.84 1550.638 77.532 -1.188
September 4550 4275.271 274.729 182.65 274.729 1825.367 86.922 2.101
October 2300 2145.502 154.498 337.148 154.498 1979.865 89.994 3.746
November 1900 1834.233 65.767 402.914 65.767 2045.632 88.941 4.53
December 2100 1951.203 148.797 551.712 148.797 2194.429 91.435 6.034
January 2000 1881.74 118.261 669.972 118.261 2312.69 92.508 7.242
February 1950 1914.291 35.709 705.681 35.709 2348.398 90.323 7.813
March 3100 2965.618 134.382 840.063 134.382 2482.781 91.955 9.136
April 4350 4102.696 247.304 1087.367 247.304 2730.085 97.503 11.152
May 4900 4764.561 135.44 1222.807 135.44 2865.524 98.811 12.375
June 2000 2010.23 -10.23 1212.577 10.23 2875.754 95.858 12.65
July 1950 2101.647 -151.647 1060.93 151.647 3027.401 97.658 10.864
August 3300 3473.15 -173.15 887.78 173.15 3200.551 100.017 8.876
September 4350 4610.384 -260.384 627.396 260.384 3460.935 104.877 5.982
October 2000 2312.584 -312.584 314.812 312.584 3773.519 110.986 2.837
November 1700 1976.153 -276.153 38.659 276.153 4049.672 115.705 0.334
December 2200 2101.206 98.794 137.453 98.794 4148.466 115.235 1.193
32
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