Tea is an aromatic beverage commonly prepared by pouring hot or boiling water over cured leaves of the Camellia sinensis (a species of evergreen shrub or small tree whose leaves and leaf buds are used to produce tea). After water, it is the most widely consumed drink in the world.
Tea processing is the method in which the leaves from the tea plant Camellia sinensis are transformed into the dried leaves for brewing tea. There are several categories of tea that are distinguished by how they are processed. In a very generic manner, tea processing involves different manners and degree of oxidation of the leaves, stopping the oxidation, forming the tea and drying it
The innate flavor of the dried tea leaves is determined by the type of cultivar of the tea bush, the quality of the plucked tea leaves, and the manner and quality of the production processing they undergo. After processing, a tea may be blended with other teas or mixed with different flavors to alter the flavor of the final tea.
XYZ Company is located in Egypt, and it is engaged in the manufacture of different brands of tea. The proprietors began their business in the year 1998 in a place known as Borg El Arab industrial region. The entire sales for the XYZ Company were about 15 Million Egyptian pounds in the year 2016. The company is, however, facing setoff issues that are related to the operational management in their different areas of business that are within their operational processes. The issues include capacity planning, quality, process layout, forecasting accuracy, and inventory and supplies relation among other issues.
After analyzing the information given, the key objectives of the company are identified as follows:
This paper makes a genuine effort in finding the operational management problems faced by the company. Further, while suggesting the plausible solutions, the paper also demonstrate application of solutions on the issue.
Forecast can be explained as prediction of future value of a variable of interest which can be anything such as demand, price, and cost, etc. Forecasts play a major role in price determination in the context of a company since under traditional cost accounting total cost is forecasted and a profit margin is added to such total cost to determine the total revenue to be generated which in turns is used to determine the price of the product. (Stevenson, 2012) The importance of forecasting to operations management cannot be overstated. The primary goal of operations management is to match supply to demand. Having a forecast of demand is essential for determining how much capacity or supply will be needed to meet demand. Any Company should adhere to the elements that make up a good forecast when planning or even bringing up one. The elements are essential as they result in the best forecast. The forecast obtained is likely to be an effective part in an organization as they in one way or the other resolve and even prevent future issues from occurring in the society (Gilliland, 2010). . First, a good forecast should be timely. The forecast should be allocated a ce0072tain time in which it can to the information contained in the planning. The timing cover allocated to the forecasting will enable the organization to implement the changes within the planning made in case there is a need for any. As a result, the forecast made should be accurate, and of degree, that is started to be accurate. Secondly, good forecasting should consist of reliable qualities. Reliability should outlay factors which will enable the planning to be of use to the organization. Therefore, the planning should be designed in a way that it acts as the backbone of the organization. In case a poor technique after applied during the time at which forecasting is being planned, there will a creating of fear that will be pulling the stakeholders whenever they are advised to apply the foresting planning method.
The third element which should be contained in good forecasting is the presence of the expression which ought to be presented in the units that are meaningful to the users. For instance, the financial planners must account on the amount of money required to establish the plan. The production planners on the hand are obliged to know the number of units required to accomplish the plan being worked on (Company, 2016). Also, the schedulers are also required to understand the types of machines as well as the skills to be applied when working with the obtained plan. It is, therefore, advice able to choose units from the needs of the customers targeted by the organization in question. The fourth element which should be put into close consideration is the means in which the forecasting is presented. Qualified forecasting is supposed to be presented in writing. Written forecasting is said to increase the likelihood of those in concern applying the planning in their areas of work. As a result, there will be a room for adjustments and changes as the ideas written as not fixed (Sasaki & Hutchins, 2014). The other key element which should be put into consideration is simplicity when it comes to an understanding and ease of use. The forecasting made using the techniques that are sophisticated are normally feared by the users. This is because the information indicated in such forecasting is normally hard to understand. The users, therefore, end up misusing the techniques or even obtaining the wrong message from the technique. Finally, the forecasting being applied should be cost-effective. Among all the other qualities of good forecasting, the planners should ensure that the benefits expected to regenerate from the forecasting greatly out ways the cost of setting and implementing the forecasting.
Once the company adheres to the elements outlined above before planning to forecast, there will be a good start in the organization. Also, the company will be in a position in which it can avoid the errors which are likely to occur as a result of poor planning. During the planning of the forecasting, there should be focused on the three main measurements of errors in forecasting (Delacruz, 2010). The first one is measured in the form of the mean absolute deviation abbreviated as MAD. The second one is the mean squared error abbreviated as MSE. Finally, there is the mean absolute percentage error abbreviated as MAPE. 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 the absolute value of each error (Sherbrooke, 2004). MAD can be calculated by below formula. Mean Absolute Percentage Error is 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. MAPE is simple and easy to know that’s the reason why it is well known. But in order for it to be considered as the best form of calculation, it has to contain quality —clarity of presentation, measurement validity, ease of interpretation, support of statistical evaluation demand reliability as MAPE meets most of above.Mean Squared Error 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.
When it comes to providing the analysis of the data provided in the table which tried to compare and contrast the data obtained by the company in the years 2015, 2016 and 2017, the final analysis drawn was that in the year 2015, the technique applied during forecasting consisted of the good elements. In the year 2016, the tracking signal applied during forecasting worked right. As a result, there was a great outcome which came as a result of correct forecasting. The results obtained in the year 2017 were poor (Mu?ller, 2011). This meant that the tracking technique which was applied in this year was not the right one. Therefore, it was not accepted. Therefore, its outcome turned out to be the poor results. Poor forecasting ended up cause a drop in the sales rate of the XYZ Company. When it comes to the 2018 forecast, the XYZ is bound to use different techniques while planning the forecasting to avoid the repetition of history.
The technique which is likely to be put into consideration when working on 2018 forecasting is the multiplicative technique also known as seasonal. This is considered to be the best technique when it comes to the forecasting of seasonal data. Therefore, the technique that is recommended for XYZ to apply in future as it guarantees increases a forecast that will increase the sales of the company (Narayan & Subramanian, 2008).
Approaches to forecasting
Mainly there are two approaches to 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 which depend on analyzing subjective inputs collected from different sources like surveys.
Time-series forecasts which are 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 usually 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 behaviour. The time frame of forecasting: There are three times frames is considered on business context; short-term (less than two months), medium term (3 months to 2 years), “they are primarily used to determine production demand delivery schedules demand to establish inventory levels” (Maramba, J. (2012 pg.98). 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”.
To sum up, it is noted with much concern that XYZ Company is not new to the matters dealing with forecasting. This is because it applied the technique in the years 2015 and 2016 and the turnout was impressing. But as a result of the change of technique, it ended up messing its sales up in the year 2017 (Narayan & Subramanian, 2008). Therefore, it should just adhere to the technique which has been proposed to produce good results.
Capacity refers to the ability to produce (in the context of operations management). (Managementstudyguide.com, n.d.) Capacity planning refers to the process of determining the anticipated production capacity which is required by the management for timely matching of supply with the demand.
Quality Management is an operation in which the activities and tasks are overseen as to whether the company can maintain desired level (Investopedia, n.d.).
The production process is nothing but a group of activities which are directed towards transforming a given set of inputs into pre-determined output (LLP, n.d.). The production process of XYZ is given as under:
Inventory management: Inventory management is an operation which is concerned with process of ordering, storing, dispatch and protection of company’s inventory.
The model of inventory and order which is currently within the organization is not in any way applying economic calculation of the quantity order of economy. The company ought to have understood that inventory should be considered as a critical form in the business field. This is because inventories are essential for operations (Gilliland, Tashman & Sglavo, 2015). Also, inventory plays a great role in the contribution of the provision of satisfaction to their customers.
In order to effectively manage inventory managers 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 and shortage costs and finally a classification system for inventory . Since inventories are used mainly to satisfy demand, then the manager must have a reliable forecast of demand amount and timing. Also, managers should know how lead time which is the time between submitting and receiving orders and demand varies as the greater the variability, the greater the need for more stocking (Keillor, 2007). Inventory has different types of cost related to it as mentioned previously, purchasing cost. In inventory management, ordering policies are very important issues deciding on how much & when to place an order is a crucial issue.
When it came to the issue of the invention, the company should put up with the acts of attempting to the amount that the company had set aside for invention. This is because invention deals with the customers’ satisfaction and it are also vital when it comes to operations. XYZ Company should get to understand that inventory should be considered to be the aim which is of most important especially when it comes to customer’s satisfactory levels (Bateman, 2012).
The existing mechanism of order fulfillment is as follows –
The company has scrutinized its performance in ‘tea manufacturing segment’ which of course contributes 50 million Egyptian Pounds to the company which can be considered as substantial and makes the performance in the segment strategically important to the company. During the board meeting, the following issues were identified that are to be addressed for a prosperous future of tea business of the company.
The economic situation of country (Egypt) as a whole turned out to be unstable owing to which the growth rate of the company came down to 4% during the year 2016 when compared to a comfortable and promising 10% during the year 2015. Further, it was highlighted that the net profit from the segment came down to 5% during the same year. To add further the main reason as identified by the General Manager was that the instability of economy has caused the operating costs of the segment grow at a higher rate than the selling prices. This could possibly mean that the economy of Egypt is experiencing higher inflation. Let us analyse the historical information of the sales given to understand the above statement.
The trend of sales as a % of growth in a month when compared to corresponding month in preceding year results as follows:
Analysis of trend in % of change in sales |
||||||
|
||||||
Month |
2014 |
2015 |
% of change |
2015(2) |
2016 |
% of change (2) |
Jan |
190,000 |
230,000 |
21.05% |
230,000 |
235,000 |
2.17% |
Feb |
198,000 |
227,000 |
14.65% |
227,000 |
232,000 |
2.20% |
Mar |
200,000 |
225,000 |
12.50% |
225,000 |
230,000 |
2.22% |
Apr |
203,000 |
223,000 |
9.85% |
223,000 |
228,000 |
2.24% |
May |
205,000 |
205,000 |
0.00% |
205,000 |
270,000 |
31.71% |
Jun |
190,000 |
260,000 |
36.84% |
260,000 |
225,000 |
-13.46% |
Jul |
250,000 |
200,000 |
-20.00% |
200,000 |
218,000 |
9.00% |
Aug |
180,000 |
195,000 |
8.33% |
195,000 |
210,000 |
7.69% |
Sep |
175,000 |
200,000 |
14.29% |
200,000 |
212,000 |
6.00% |
Oct |
190,000 |
205,000 |
7.89% |
205,000 |
215,000 |
4.88% |
Nov |
200,000 |
220,000 |
10.00% |
220,000 |
232,000 |
5.45% |
Dec |
220,000 |
235,000 |
6.82% |
235,000 |
240,000 |
2.13% |
Total |
2,401,000 |
2,625,000 |
10.19% |
2,625,000 |
2,747,000 |
5.19% |
Average |
200,083 |
218,750 |
|
218,750 |
228,917 |
|
From both the figures (1.1 and 1.2) above, it can be observed that the economic conditions of the country has effected the sales from the beginning month itself where it can be clearly seen that the company has registered a growth in sale of 2.17% in 2016 when compared to sales in corresponding month of 2015 while the same was 21.05% in 2015 when compared to sales in corresponding month of 2014. If we further look at the graph in figure 1.1, we can notice the gap in level of sales between 2014 and 2015 (represented by blue and orange lines respectively) is more than the gap between 2015 and 2016 (represented by grey line).
Another concern pointed out by the operations manager was the practical hindrances which are posing challenge to reduction of operation costs even after continuous efforts towards the same. The following are the hindrances as confessed by the operations director.
While the exact issues with the suppliers were unstated, owing to the unstable economic conditions and the fact that the operational costs are raising, it can be inferred that the local suppliers may be increasing the price of material due to inflation and further, the inflation in turn is also effecting the exchange rate of currency negatively thereby making the company pay more than what the company would pay with a stable exchange rate.
The company has adopted ‘Fixed order quantity’ model for procuring raw materials. This is indicated by the fact that the company has an order quantity of 350000 tons per order. Further the usage of point-of-use replenishment suggests that the company has no separate department for inventory management for majority of items and hence the company may not have set up re-order level owing to which the company would have failed to order the raw material in time resulting in delay of availability of raw material.
As identified by the Operations Director, while the company’s aim is reduction of operating costs without affecting the existing quality of its reputed tea brand, the company is concerned with the level of defects owing to moisture. The problem persisted even after training the employees on the quality concepts and usage of quality tools.
Operation Director also expressed his doubts on the existing capacity of the company band claimed that the company has barely managed to meet the demand with its existing capacity during the years 2015 and 2016. Let us analyze the required capacity with the existing capacity.
Existing Capacity vs Required Capacity |
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|
|||||
Particulars |
Existing Capacity |
Requirement |
|||
2014 |
2015 |
2016 |
|||
Per Month |
230,000 |
200,083 |
218,750 |
228,917 |
|
Per Annum |
2,760,000 |
2,401,000 |
2,625,000 |
2,747,000 |
As per figure 2.1, we can see that the existing capacity was greater than the required capacity till latest year. However, the existing capacity mentioned in the table was maximum capacity and not the normal capacity. Therefore the company should have ideally maintained more capacity especially during 2016. Any abnormal halt of production during the year would have ended up loss of sales as the margin was only 13,000 bags of tea as suggested by the table. Further assuming that the company maintains the existing growth rate of 4.65% the expected requirement during the year 2017 would be 2,874,736 units which clearly exceeds the existing capacity.
The basic structure of Order fulfillment mechanism adopted by XYZ Company if depicted in form of Business Mapping would look as follows:
Since the company XYZ is operating under unfavorable economic conditions and such conditions are expected to last till coming 2 years, the sales growth is assumed to be similar to that of year 2016. Let us analyze the growth rate of sales to determine whether the rate exhibits any trend or seasonality.
Now as observed in the figure 3.1, while it is hard to establish whether the growth rate displayed a seasonality it is obvious that the growth rate of sales is displaying a trend (a non-linear trend) from year to year. In every year there is a peak period of time lasting for 2 months where the sales growth increased dramatically followed by a month where there was negative growth followed by a steady increase and then an irregular variation. Even if the trend is assumed to be a seasonal variation, Time Analysis Forecast method adjusted to seasonal variation cannot be adopted owing to non-linear trend. Further, predictive analysis using Regression technique cannot be applied owing to lack of supportive date of predictor variable. Hence a simple ‘3-period moving average’ technique can adopted to forecast the sales.
The Operations director has suggested an alternative for existing tradition auction method of procuring international supplies (of tea) which is to have a strategic alliance with large farmers and meet the requirement directly from their farms. Since the costs of each of the alternative is given, indifference analysis should be conducted to find out the better option from the both.
If the company should ever reach its objective of reducing operating costs, the company should consider adopting economic order quantity model for determining the quantity to be ordered each time. However, since discounts are involved, we should also consider Quantity Discount Model of Economic Order Quantity. Further if the company aims at eliminating delay in availability of raw material, then it should adopt the Re-Order Level which will be computed at a later part of the report.
The company is concerned with the quality of the finished goods and in order to check whether the production process is in-control (or) out-of control, let us analyze the Statistical Process Control by computing the Mean and Range of the samples. Then let us construct X-Bar Chart and Range Chart through which we can calculate the upper control limits and lower control limits. If all the samples are within the control limits, the process is said to be randomly variable and is in-control. If the samples are not within the control limits, then the process is said to be non-randomly variable or out-of-control.
In order to address the capacity concerns raised by the operations director, he also proposed 2 alternatives to meet the increasing requirement. To analyze the best suited method, indifference analysis can be utilized.
As it is observed that the company is taking a minimum of 2 days 5 hours and 10 minutes for completion of a sale order while it is taking as long as 7 days 5 hours 10 minutes. Within this time, there is a waiting time of 1 and half hour in queue and hence can be avoided. Further strict surveillance of transportation may also considerably reduce the extended delivery time which is 4 days in some of the cases. Further with proper internal controls in place, and a robust ERP system, the company can also avoid mistakes such as cancellation of sales order, and dispatching the goods to wrong address.
By adopting the 3 period moving averages method, the sales are forecasted as follows:
Moving Averages |
||
|
||
n |
Values for t |
Forecasted figures |
t-3 |
215000 |
|
t2 |
232000 |
|
t-1 |
240000 |
|
T |
229000 |
229000 |
t+1 |
233667 |
233667 |
t+2 |
234222 |
234222 |
t+3 |
232296 |
232296 |
t+4 |
233395 |
233395 |
t+5 |
233305 |
233305 |
t+6 |
232999 |
232999 |
t+7 |
233233 |
233233 |
t+8 |
233179 |
233179 |
t+9 |
233137 |
233137 |
t+10 |
233183 |
233183 |
t+11 |
233166 |
233166 |
Total |
2794780 |
By looking into the figure 4.2, it can be observed that the forecast has not followed the trend of the historical data. This is because the historical data itself is obtained from conditions which resulted in sales which is significantly different from the trend that was experience in past 2 years preceding the year 2016. It is therefore pertinent to note that the quantitative forecast is only of little use under the following circumstances:
Therefore the company may consider using qualitative methods of forecast such as collecting –
Indifference Point Analysis |
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|
|||
Particulars |
Alternative 1 |
Alternative 2 |
Difference |
Fixed Costs (a) |
100000 |
0 |
100000 |
Variable Costs per ton (b) |
800 |
2500 |
1700 |
Indifference Point = (a) / (b) |
59 |
||
Expected Cost for annual requirement = (c ) x 3,000,000 tons |
2,400,100,000 |
7,500,000,000 |
5,099,900,000 |
Alternative 1 – Building alliance |
|||
Alternative 2 – Traditional Auction |
From the table in figure 4.3, we can conclude that the indifference point is 59 tons of tea which implies that if the annual demand of tea by XYZ Ltd is less than 59 tons, the company should go with the alternative with lowest fixed cost (i.e., Alternative – 2) and if the demand for tea by XYZ Ltd is more than 59 units, the company should go with the alternative that results in lowest variable cost p.u. (i.e., Alternative – 1). Since the annual demand is 3,000,000 tons the company should go with alternative – 1 (i.e., Building alliance with local farmers) if financial results alone are concerned. However XYZ Ltd should also consider the following non-financial factors while choosing the alternative.
The analysis is as follows:
Step 1: Computation of Basic Economic Quantity –
Basic Economic Order Quantity |
|
|
|
Particulars |
Result |
Total Quantity required per month (a) |
250,000 |
Total Quantity required per annum (b) = (a) x 12 |
3,000,000 |
Ordering Costs per Order (c ) |
20,000 |
Carrying Cost per unit per month (d) |
2 |
Carrying Cost per unit per annum (e) = (d) x 12 |
24 |
Economic Order Quantity = [(2 x (b) x (c )) / (e)]^1/2 |
70,711 |
Step 2: Analysis of the effect of discount on total cost of inventory
Total cost (TC) = Carrying Costs + Order Costs + Purchase Cost
Total Inventory Costs at various levels of ordering |
||||
|
||||
Tons per Order |
EOQ |
240000 tons |
250000 units |
350000 units |
Carrying Costs |
|
|||
No. of Units (a) |
70711 |
240000 |
250000 |
350000 |
Carrying Cost per unit per order (b) |
24 |
24 |
24 |
24 |
Total Carrying Costs (A) => ((a) / 2) x (b) |
848532 |
2880000 |
3000000 |
4200000 |
Ordering Costs |
|
|||
No. of units required (a) |
3,000,000 |
3000000 |
3000000 |
3000000 |
No. of units per order (b) |
70711 |
240000 |
250000 |
350000 |
No. of orders required (c ) |
42 |
13 |
12 |
9 |
Ordering Cost per Order (d) |
20,000 |
20,000 |
20,000 |
20,000 |
Total Ordering Cost (B) = (c ) * (d) |
848,524 |
250,000 |
240,000 |
171,429 |
Purchase Costs |
|
|||
Total units to be purchased (a) |
3,000,000 |
3,000,000 |
3,000,000 |
3,000,000 |
Price per unit (b) |
2500 |
2400 |
2300 |
2300 |
Total Purchase Costs (C) = (a) x (b) |
7,500,000,000 |
7,200,000,000 |
6,900,000,000 |
6,900,000,000 |
Total Inventory Costs = (A) + (B) + (C ) |
7,501,697,056 |
7,203,130,000 |
6,903,240,000 |
6,904,371,429 |
From table in figure 4.5 although the EOQ computed initially was 70,711 units per order, owing to the effect of discounts on big orders by the suppliers, 250,000 units per order is the optimum level of quantity to be ordered to reduce the overall inventory costs. However, from the analysis we can conclude that the current level of order of 350,000 units’ results in more cost than the optimum level decided at 250,000 units. However while the concept of EOQ (Quantity Discount Model in given case) assures that the overall cost of inventory will be lower, it will not guarantee availability of sufficient quantity of material throughout the year. Therefore if the company is concerned about timely availability of raw material, it should also consider analyzing Re-order quantity. The re-order quantity is computed as under:
Re-order Quantity |
|
|
|
Particulars |
Result |
Reorder Quantity |
|
Lead time (a) |
30 days |
Average consumption during month (b) |
250000 |
Lead time consumption (c )= (a) x (b) / 30 |
250000 |
Safety days (assumed to be 25% of original days) (d) |
7.5 |
Safety Stock (e) = (d) x (b) / 30 |
62500 |
Total Reorder Quantity (f) = (e) + (c ) |
312500 |
From the table in figure 4.7, it can be observed that the re-order quantity is 312,500 and is higher than the EOQ. While this results in slightly higher inventorial cost than EOQ, it also reasonably ensures that the company maintains enough stock at any point of time thereby matching the production with forecasted demand. Further, it can also be observed that, while the Re-order Quantity certainly results in higher costs than EOQ, the same however will not exceed the cost incurred with the current level of order.
Re-order Level: Re-order Level (or Re-order point) is the level of inventory reaching which the company should order the stock in order to maintain stock levels which enable the company to continue its operations without any haul (Accountingexplained.com, n.d.). The Re-order level is computed as follows:
ROP = d X LT
Where
D = Demand rate (units per month) = 250,000
LT = Lead time in months = 30 days (or) 1 month
Therefore, ROP => 250,000 x 1 month = 250,000 units
The following table consists of 20 samples each containing 5 observations from the material of XYZ.
|
1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
11 |
12 |
13 |
14 |
15 |
16 |
17 |
18 |
19 |
20 |
1 |
7 |
6.7 |
6.9 |
6 |
6.5 |
6.7 |
7.9 |
6 |
6.5 |
7.7 |
6.9 |
6 |
6.5 |
6.7 |
7.9 |
6.9 |
6.8 |
6.5 |
6.7 |
6.9 |
2 |
7.2 |
6.6 |
7 |
6.1 |
6.6 |
6.6 |
7 |
6.1 |
6.6 |
7.6 |
6.5 |
6.1 |
7.6 |
6.6 |
7 |
7.1 |
6.7 |
6.6 |
6.6 |
7 |
3 |
7 |
6.7 |
6.7 |
6.2 |
6 |
6.7 |
7.4 |
6.2 |
6 |
6.7 |
6.4 |
6.2 |
7 |
6.7 |
7.4 |
7.2 |
7.2 |
7 |
6.7 |
7.4 |
4 |
6.9 |
6.9 |
6.7 |
6.4 |
6.4 |
6.9 |
7.3 |
6.4 |
6.4 |
6.9 |
6.3 |
6.4 |
7.4 |
6.9 |
7.3 |
7.4 |
7.4 |
7.4 |
6.9 |
6.7 |
5 |
6.9 |
7 |
6.5 |
6.3 |
6.7 |
6 |
6.9 |
6.3 |
6.7 |
6 |
6.4 |
6.3 |
6.7 |
6.9 |
7.4 |
7.3 |
7.5 |
7.3 |
7 |
6.8 |
Let us apply Statistical Process Control to find out whether the control chart of the finished product is random in control or conversely random out of control.
Step 1: Computation of Mean and Range of data:
|
1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
11 |
12 |
13 |
14 |
15 |
16 |
17 |
18 |
19 |
20 |
1 |
7.00 |
6.70 |
6.90 |
6.00 |
6.50 |
6.70 |
7.90 |
6.00 |
6.50 |
7.70 |
6.90 |
6.00 |
6.50 |
6.70 |
7.90 |
6.90 |
6.80 |
6.50 |
6.70 |
6.90 |
2 |
7.20 |
6.60 |
7.00 |
6.10 |
6.60 |
6.60 |
7.00 |
6.10 |
6.60 |
7.60 |
6.50 |
6.10 |
7.60 |
6.60 |
7.00 |
7.10 |
6.70 |
6.60 |
6.60 |
7.00 |
3 |
7.00 |
6.70 |
6.70 |
6.20 |
6.00 |
6.70 |
7.40 |
6.20 |
6.00 |
6.70 |
6.40 |
6.20 |
7.00 |
6.70 |
7.40 |
7.20 |
7.20 |
7.00 |
6.70 |
7.40 |
4 |
6.90 |
6.90 |
6.70 |
6.40 |
6.40 |
6.90 |
7.30 |
6.40 |
6.40 |
6.90 |
6.30 |
6.40 |
7.40 |
6.90 |
7.30 |
7.40 |
7.40 |
7.40 |
6.90 |
6.70 |
5 |
6.90 |
7.00 |
6.50 |
6.30 |
6.70 |
6.00 |
6.90 |
6.30 |
6.70 |
6.00 |
6.40 |
6.30 |
6.70 |
6.90 |
7.40 |
7.30 |
7.50 |
7.30 |
7.00 |
6.80 |
x |
7.00 |
6.78 |
6.76 |
6.20 |
6.44 |
6.58 |
7.30 |
6.20 |
6.44 |
6.98 |
6.50 |
6.20 |
7.04 |
6.76 |
7.40 |
7.18 |
7.12 |
6.96 |
6.78 |
6.96 |
R |
0.30 |
0.40 |
0.50 |
0.40 |
0.70 |
0.90 |
1.00 |
0.40 |
0.70 |
1.70 |
0.60 |
0.40 |
1.10 |
0.30 |
0.90 |
0.50 |
0.80 |
0.90 |
0.40 |
0.70 |
y substituting the values of X and R in X –bar charts of POM-QM, we obtained
UCL (Upper control limit) |
6.9014 |
1.071 |
CL (Center line) |
6.779 |
.68 |
LCL (Lower Control Limit) |
6.6566 |
.2815 |
From the range chart above in figure 4.11, it can be observed that the process of production is at times, out of control. An out-of-control situation reflects involvement of non-random variation which is caused by definite and specific causes called as assignable causes. These assignable causes make the process out-of-control or become statistically unstable. Therefore the company should investigate the matter further and find out the assignable cause. Once such cause is identified, it should be eliminated and only random variable due to common causes remains. Thus the entire process becomes stable and return to an in-control situation (Winspc.com, n.d.). Since the data (rather the process) is already proven to be non-random, runs test need not be conduct the random variability of the data.
Indifference Point Analysis |
|||
|
|||
Particulars |
Alternative 1 |
Alternative 2 |
Difference |
Fixed Costs (a) |
100000 |
0 |
100000 |
Variable Costs per ton (b) |
150 |
900 |
750 |
Indifference Point = (a) / (b) |
133 |
||
Alternative 1 – Buy a new line |
|||
Alternative 2 – Toll manufacture |
As evident from figure 4.13, the indifference point between both the alternatives (viz., Buying a new line and toll of manufacture of incremental requirement) turned out to be 133 bags. Therefore if additional capacity required is less than 133 bags, the company should adopt option with lesser fixed costs (i.e., Alternative – 2) and if the additional capacity required by the company is expected to be more than 133 bags, the company should go with option with lower variable costs (i.e., Alternative – 1). Assuming that the company maintains same rate of growth of sales as in the previous year 2016, (i.e., 4.65%), the total expected demand would be 2,874,736 bags which exceeds the existing capacity of 2,760,000 bags by 114,736 bags (which is greater than the indifference point of 133 bags). Since the expected additional capacity required exceeds the indifference point, the company should prefer acquiring additional line of production instead of tolling the requirement to the third party.
The company can either adopt moving averages method or qualitative method for forecasting. If the company adopts qualitative method, the company can consider that the growth rate would remain the same as was in the case of 2016 depending upon the information available.
If financial aspects alone are considered, the company is suggested to go with the alternative of forming an alliance with the large farmers for procurement of raw material.
The company should go with the alternative of acquiring new line of production rather than outsourcing the additional expected demand.
Conclusion:
After critical analysis of the operations of the company, it can be concluded as follows:
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