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XYZ COMPANY FOR TEA PRODUCTION

OPERATIONS MANAGEMENT CASE STUDY

Name : Reham Ahmed Ghareeb

Reg.No. : 18225505

Proffesor : Mohammed Elmokadem

Class: MBA 2019-

:Executive summary

As an Operations Management Consultant, XYZ company has hired me to help determine

how to improve its operations management by improving forecasting accuracy, minimizing

. waste, developing supplierrelations, by using available resources in a cost effective manner

In Egypt, XYZ established its tea factory in Borg El Arab in 2000, to produce high quality tea.

The total sales for XYZ (the tea factory) were around 50 Million Egyptian pounds during year

2016. the growth in sales in years 2014 and 2015 reached around 10% annually. However,

the current economic situation in Egypt affected sales during 2016 with a less than 4 %

.% growth. Furthermore, the net profits from the tea business decreased by 5

The company's strategy is based on differentiation strategy, which aims to provide the best

tea tasting experience to its customers.

The company is having some operational management issues in different functions such

as: operations forecasting, inventory and order fulfillment, production process, quality

and relationship with the suppliers.

The main concern of the general manager that the current economic situation is expected

to continue during the upcoming period (2017 and 2018). It is still not clear how the

prevailing economic conditions will affect the company sales during 2017, which

highlighting the need to reassess the forecasting techniques that they usually use.

The companys' objectives:

Maintaining the growth in sales and cutting costs while keeping the same quality of their

famous tea brand.

The Core Competencies of XYZ Company:

1. They have been in the market for around 15 years.

2. Consumer needs are well defined and market surveys confirm that consumers are

generally happy with the company’s product.

3. All quality problems are resolved before reaching customers.

Key Operations Management Issues facing XYZ Co. :

Forecasting Approaches

Forecasting of year 2016

Naïve method

Error lErrorl Error^2 ] lErrorl÷Actual [ × 100 Forecast 2016 Actual 2015 Month Jan 230000 ---- ---- Feb 227000 230000 -3000 3000 9000000 1.32 % Mar 225000 227000 -2000 2000 4000000 0.89 % Apr 223000 225000 -2000 2000 4000000 0.9 % May 205000 223000 -18000 18000 324000000 8.8 % Jun 260000 205000 55000 55000 3025000000 21.2 % Jul 200000 260000 -7000 7000 49000000 3.5 % Aug 195000 200000 -5000 5000 25000000 2.56 % Sep 200000 195000 5000 5000 25000000 2.5 % Oct 205000 200000 5000 5000 25000000 2.4 % Nov 220000 205000 15000 15000 225000000 6.8 % Dec 235000 220000 15000 15000 225000000 6.4 % 58000 132000 3940000000 57.27 %

MAD = Ʃlel / n = 132000 / 11 = 12000

MSE = Ʃ e

2

/ n-1 = 3940000000 / 10 = 394000000

MAPE = Ʃ ] lel / Actual × 100 [ / n = 57.27 % / 11 = 5.2 %

Two – period Moving Average method

Error lErrorl Error^2 ] lErrorl÷Actual [ × 100 Forecast 2016 Actual 2015 Month Jan 230000 Feb 227000 Mar 225000 228500 -3500 3500 12250000 1.6 % Apr 223000 226000 -3000 3000 9000000 1.3 % May 205000 224000 -19000 19000 361000000 9.3 % Jun 260000 214000 46000 46000 2116000000 17.7 % Jul 200000 232500 -32500 32500 1056250000 16.3 % Aug 195000 230000 -35000 35000 1225000000 17.9 % Sep 200000 197500 2500 2500 6250000 1.25 % Oct 205000 197500 7500 7500 56250000 3.7 % Nov 220000 202500 17500 17500 306250000 8 % Dec 235000 227500 7500 7500 56250000 3.2 % -12000 174000 5204500000 80.25 %

MAD = Ʃlel / n = 174000 / 10 = 17400

MSE = Ʃe

2

/ n-1 = 5204500000 / 9 = 578277777.

MAPE = Ʃ ] lel / Actual × 100 [ / n = 80.25 % / 10 = 8 %

: Exponential smoothing

Error lErrorl Error^2 ] lErrorl÷Actual [ × 100 Forecast 2016 Actual 2015 Month Jan 230000 Feb 227000 225500 1500 1500 2250000 0.7 % Mar 225000 225650 -650 650 422500 0.3 % Apr 223000 225585 -2585 2585 6682225 1.2 % May 205000 225326 -20326 20326 413146276 10 % Jun 260000 223294 36706 36706 1347330436 14 % Jul 200000 226965 -26965 26965 727111225 13 % Aug 195000 224269 -29269 29269 856674361 15 % Sep 200000 221342 -21342 21342 455480964 10.7 % Oct 205000 219208 -14208 14208 201867264 7 % Nov 220000 217787 2213 2213 4897369 1 % Dec 235000 218008 16992 16992 288728064 7.2 % 173406 4304590684 80 %

MAD = Ʃlel / n = 173406 / 11 = 15764

MSE = Ʃe

2

/ n-1 = 4304590684 / 10 = 430459068.

MAPE = Ʃ ] lel / Actual × 100 [ / n = 80 % / 11 = 7.3 %

: Calculating Comparison between forecasting accuracy of the 3 methods

Exponential smoothing

Two – period Moving

Average method

Naïve method

MAD = 15764

MSE = 430459068.

% MAPE = 7.

MAD = 17400

MSE = 578277777.

% MAPE = 8

MAD = 12000

MSE = 394000000

% MAPE = 5.

Apparently, naïve method has the lowest tendency for forecasting errors among the three

methods.So, it is recommended to use the naïve approach for capturing the demand of

upcoming year 2017, keeping in mind presence of irregular variations in demand due to

. some changes in behavior of customers

. The company needs to keep monitoring the performance of the used forecasting technique

Comparison of the actual sales and the forecasted demand of year 2016

Sep 212000 210000 Oct 215000 212000 Nov 232000 215000 Dec 240000 232000 228916.7 245416. Average ( box/month) 2747000 2945000 Total annual sales ( box /year)

: Monitoring the forecast

Tracking signal:

It is computed period by period and it can be positive or negative, a value of zero would be

ideal; limits of ± 4 or ± 5 are often used for a range of acceptable values of tracking signal.

If a value is out of the range, then there is a bias in the forecast, which means persistent

tendency for forecasts to be greater or less than actual values

Followig is tracking signal values of the naïve forecasting method for year 2016

MADt TS Cumulative lel Cumulative Error Error lErrorl Actual Forecast Month Jan 235000 220000 15000 15000 15000 15000 15000 1 Feb 232000 235000 -3000 3000 12000 18000 9000 1. Mar 230000 232000 -2000 2000 10000 20000 6666.7 1. Apr 228000 230000 -2000 2000 8000 22000 5500 1. May 270000 228000 42000 42000 50000 64000 12800 3. Jun 225000 270000 -45000 45000 5000 109000 18166.7 0. Jul 218000 225000 -7000 7000 -2000 116000 16571.4 -0. Aug 210000 218000 -8000 8000 -10000 124000 15500 -0. Sep 212000 210000 2000 2000 -8000 126000 14000 -0. Oct 215000 212000 3000 3000 -5000 129000 12900 -0. Nov 232000 215000 17000 17000 12000 146000 13272.7 0. Dec 240000 232000 8000 8000 20000 154000 12833.3 1.

Values of tracking signal are within ± 4 which indicate a range of acceptable values of

tracking signal of the forecast.

: Product

Toll manufacture Buy a new line

Fixed cost None $ 100000

Variable cost $ 900/ton $ 150/ton

Cost-volume analysis is a widely used technique used for evaluating and comparing

capacity alternatives from an economic perspective, through examining the relationships

between cost, revenue and volume of output

Point of indifference : is the level of volume at which total costs, and hence profits, are the

same under both cost structures. At the cost indifference point, total costs (fixed cost and

. variable cost) associated with the two alternatives are equal

: At point of indifference

Total Cost for toll manufacture = Total Cost for buying a new line

Fixed cost + variable cost = Fixed cost + variable cost

0 + 900 Q = 1000000 + 150 Q

750 Q = 100000

Q pod = 1333 tons

According to the expected demand in 2017 (245417 tons/ month) which exceeds the actual

capacity of the production lines

If expected sales is less than the sales at POD, an alternative with low unit contribution is

preferred. But if expected sales is more than sales at POD, then an alternative which offers

higher unit contribution is more profitable. It is up to the decision maker to choose the

most profitable alternative.

At volume below 1333 tons , toll manufacture gives lower total costs (and higher profits);

above 1333 tons , buying a new line gives higher profits.

Suppliers

The main concern of the company is the supplying raw materials from international

vendors, that take long time with lead time reaching 30 days. But they found that it is very

difficult to change them, so they started to think for other options.

Recommendation:

It is very important while thinking for strategic alliances is to take in consideration some

aspects other than the cost such as quality, delivery, flexibility, service level, etc, need to be

further analyzed to take right decisions.

200000 – less than 250000 $ 2400

The economic order quantity can be bought at $2500 , then the total cost will be:

TC ( for 100000 tons) = Carrying cost + Ordering cost + Purchasing cost

= (Q /2)H + (D/Q)S + PD where P is unit price

= (100000/2)2 + (250000/100000)20000 + (2500 ×250000)

Because lower cost ranges exist, each must be checked against the min. cost generated

TC ( for 200000 tons) = Carrying cost + Ordering cost + Purchasing cost

= (200000/2)2 + (250000/200000)20000 + (2400× 250000)

TC ( for 350000 tons) = Carrying cost + Ordering cost + Purchasing cost

= (350000/2)2 + (250000/350000)20000 + (2300× 250000)

Therefore, because the order quantity range of (≥ 250000 tons ) yields the lowest total

cost , 350000 tons is the overall optimal order quantity.

Reorder point (ROP): occurs when the quantity on hand drops to a predetermined amount

ROP = d X LT where, d = demand rate( units per time) , LT= lead time

ROP = 250000 tons/ month X 1 month = 250000 tons/month

Thus, The company should carry additional inventory called, safety stock

Safety Stock : stock that is held in excess of expected demand due to variability in demand

rate and/or lead time.

ROP = expected demand during lead time + safety stock

If expected demand 250000 tons/ month and the desired amount of safety stock is

25000 tons/month, ROP would be 275000 tons

Recommendations:

The company should take in consideration the economic order measures in order

fulfillment as well as the quantity discounts to afford the lowest total cost and encourage

further sales in order to achieve as much profits as needed.

Inventory Safety stock strategy is useful to cover the probable variability in customer

demand. These strategies can help to lower the selling prices to the customer and hence

increase the rate of sales and profitability and increasing the market share.

Flow chart to study customer orders, starting from the arrival of the customer order until

the order delivery:

0.25 % of orders 1 hour on 1 hour on 3 hours on average dismissed average average Yes 30 min. 10 min. 0.2 % wrong orders 3-5 days 4 days on average Some orders being Delivered after 1 week

 Adding up the times required at each step, the average time between ordering and

delivering in-stock product ranges from 6090 min.( 101.5 hrs) to 10410 min. ( 173.

hrs). the delivery time consumes much more time than other processes.

 0.25 % of orders are dismissed before getting to the picking area.

 0.2 % of the survived orders, are being shipped with the incorrect products or

quantities.

Recommendations:

Analysis of causes of the delayed or defected activities in the process is needed in order

to find effective solutions to speed up the process and get customers satisfaction.

Quality:

A sample data was collected ( 20 samples each with 5 observations )about the moisture

content of the tea mixture.

Internal mail delivers orders Order sits in picking area Print orders Customer e-mails order Is product in ? stock Inspector checks order Worker picks order Order being processed Customer receives order Transport division delivers order

UCLR = D 3 R where, D 3 = control chart factor based on sample size, n UCL = 1.59 x 0.68 = 1. LCL = 0.41 x 0.68 = 0.

Results of 1 & 2 charts :

The average values of the samples observations are out of upper and lower control

limits, showing non-random variations, then the process is considered unstable. It is

recommended to eliminate the causes of non- randomness and achieve a stable process

3- Run test :

Two types a- Runs ups and down

b- Runs above and below the median

sample mean A/B U/D Sample Mean A/B U/D

1 7 A 11 6.98^ A D

2 6.78^ B D 12 6.5^ B D

3 6.76^ A D 13 6.2^ B D

4 6.2^ B D 14 7.04^ A U

5 6.44^ B U 15 6.76^ A D

6 6.58^ B U 16 7.04^ A U

7 7.3^ A U 17 7.18^ A U

8 6.2^ B D 18 7.12^ A D

9 6.44^ B U 19 6.96^ A D

10 7 A U 20 6.78^ A D

Where, median = 6.

1- A/B : 9 runs U/D : 9 runs

2- Expected number of runs for each test is:

E(r) med = (N/2) + 1 = (20/2) + 1 = 11 runs E(r) u/d = (2N-1) / 3 = {(2x20)-1} /3 = 39/3 = 13 runs

3- The standard deviations are :

σ med =

√ N − 1 4

√ 20 − 1 4

σ (^) u/d = √

16 N − 29

√^16 (^20 )−^29

The Ztest Values are : Zmed ¿^ r −{( N / 2 )+ 1 } σ med

2.18 =^ - 0.

Zu/d ¿^ r −{( 2 N − 1 )/ 3 } σ u / d

1.8 =^ - 2.

Since Z= ±2. Non-random variations are probably present in the data and hence,

the process is out of control.

Recommendations :

The organization should improve the processing quality through measuring the current

situation and analyzing why things are not working as planned by using quality tools for

problem solving and process improvement, and then taking action by controlling, improving

or redesigning the process. The company should confirm whether their products satisfy

customer's expectations or not.

Quality tools aid in data collection and interpretation and provide the basis for decision

making, such as:

1- Flow charts (process maps) : detailed specific activities that make up the information,

physical or monetary flow of a process. It gives the first complete picture of how the

process work.

2- Histogram : a graphical representation of the variation or distribution of data. It is

useful tool in getting a sense of the distribution observed values.

3- Pareto analysis ( 80/20 rule) : focus on the 20% activities (vital few) that are the

cause of 80% of problems (trivial many).Manager should examine each stuation