Notice Board


Wednesday, August 9, 2017

Industrial Engineering Notes ( Unit 3)

The growing competition, frequent changes in customer's demand and the trend towards automation demand that decisions in business should not be based purely on guesses rather on a careful analysis of data concerning the future course of events. More time and attention should be given to the future than to the past, and the question 'what is likely to happen?' should take precedence over 'what has happened?' though no attempt to answer the first can be made without the facts and figures being available to answer the second. When estimates of future conditions are made on a systematic basis, the process is called forecasting and the figure or statement thus obtained is defined as forecast.
In a world where future is not known with certainty, virtually every business and economic decision rests upon a forecast of future conditions. Forecasting aims at reducing the area of uncertainty that surrounds management decision-making with respect to costs, profit, sales, production, pricing, capital investment, and so forth. If the future were known with certainty, forecasting would be unnecessary. But uncertainty does exist, future outcomes are rarely assured and, therefore, organized system of forecasting is necessary. The following are the main functions of forecasting:
  • The creation of plans of action.
  • The general use of forecasting is to be found in monitoring the continuing progress of plans based on forecasts.
  • The forecast provides a warning system of the critical factors to be monitored regularly because they might drastically affect the performance of the plan.
It is important to note that the objective of business forecasting is not to determine a curve or series of figures that will tell exactly what will happen, say, a year in advance, but it is to make analysis based on definite statistical data, which will enable an executive to take advantage of future conditions to a greater extent than he could do without them. In forecasting one should note that it is impossible to forecast the future precisely and there always must be some range of error allowed for in the forecast.
Dependent versus Independent Demand
Demand of an item is termed as independent when it remains unaffected by the demand for any other item. On the other hand, when the demand of one item is linked to the demand for another item, demand is termed as dependent. It is important to mention that only independent demand needs forecasting. Dependent demand can be derived from the demand of independent item to which it is linked.
Business Time Series
The first step in making a forecast consists of gathering information from the past. One should collect statistical data recorded at successive intervals of time. Such a data is usually referred to as time series. Analysts plot demand data on a time scale, study the plot and look for consistent shapes and patterns. A time series of demand may have constant, trend, or seasonal pattern ( Figure 1 ) or some combination of these patterns. The forecaster tries to understand the reasons for such changes, such as,
  • Changes that have occurred as a result of general tendency of the data to increase or decrease, known as secular movements.
  • Changes that have taken place during a period of 12 months as a result in changes in climate, weather conditions, festivals etc. are called as seasonal changes.
  • Changes that have taken place as a result of booms and depressions are called as cyclical variations.
  • Changes that have taken place as a result of such forces that could not be predicted (like flood, earthquake etc.) are called as irregular or erratic variations.
uantitative Approaches of Forecasting
Most of the quantitative techniques calculate demand forecast as an average from the past demand. The following are the important demand forecasting techniques.
  • Simple average method: A simple average of demands occurring in all previous time periods is taken as the demand forecast for the next time period in this method.
Simple Average :
A XYZ television supplier found a demand of 200 sets in July, 225 sets in August & 245 sets in September. Find the demand forecast for the month of october using simple average method.
The average demand for the month of October is

Simple moving average method: In this method, the average of the demands from several of the most recent periods is taken as the demand forecast for the next time period. The number of past periods to be used in calculations is selected in the beginning and is kept constant (such as 3-period moving average).
Simple Moving Average :
A XYZ refrigerator supplier has experienced the following demand for refrigerator during past five months.
Find out the demand forecast for the month of July using five-period moving average & three-period moving average using simple moving average method.
 Weighted moving average method: In this method, unequal weights are assigned to the past demand data while calculating simple moving average as the demand forecast for next time period. Usually most recent data is assigned the highest weight factor
Example 3
Weighted Moving Average Method :
The manager of a restaurant wants to make decision on inventory and overall cost. He wants to forecast demand for some of the items based on weighted moving average method. For the past three months he exprienced a demand for pizzas as follows:
Find the demand for the month of January by assuming suitable weights to demand data.
Exponential smoothing method: In this method, weights are assigned in exponential order. The weights decrease exponentially from most recent demand data to older demand data

Exponential Smoothing :
One of the two wheeler manufacturing company exprienced irregular but usually increasing demand for three products. The demand was found to be 420 bikes for June and 440 bikes for July. They use a forecasting method which takes average of past  year to forecast future demand. Using the simple average method demand forecast for June is found as 320 bikes (Use a smoothing coefficient 0.7 to weight the recent demand most heavily) and find the demand forecast for August.
Regression analysis method: In this method, past demand data is used to establish a functional relationship between two variables. One variable is known or assumed to be known; and used to forecast the value of other unknown variable (i.e. demand)
Example 5
Regression Analysis :
Farewell Corporation manufactures Integrated Circuit boards(I.C board) for electronics devices. The planning department knows that the sales of their client goods depends on how much they spend on advertising, on account of which they receive in advance of expenditure. The planning department wish to find out the relationship between their clients advertising and sales, so as to find demand for I.C board.
The money spend by the client on advertising and sales (in dollar) is given for different periods in following table :
Advertising (Xt)
Sales (Dt)

Error in Forecasting
Error in forecasting is nothing but the numeric difference in the forecasted demand and actual demand. MAD (Mean Absolute Deviation) and Bias are two measures that are used to assess the accuracy of the forecasted demand. It may be noted that MAD expresses the magnitude but not the direction of the error

The amount of material, a company has in stock at a specific time is known as inventory or in terms of money it can be defined as the total capital investment over all the materials stocked in the company at any specific time. Inventory may be in the form of,
  • raw material inventory
  • in process inventory
  • finished goods inventory
  • spare parts inventory
  • office stationary etc.
As a lot of money is engaged in the inventories along with their high carrying costs, companies cannot afford to have any money tied in excess inventories. Any excessive investment in inventories may prove to be a serious drag on the successful working of an organization. Thus there is a need to manage our inventories more effectively to free the excessive amount of capital engaged in the materials.
Why Inventories?
Inventories are needed because demand and supply can not be matched for physical and economical reasons. There are several other reasons for carrying inventories in any organization.
  • To safe guard against the uncertainties in price fluctuations, supply conditions, demand conditions, lead times, transport contingencies etc.
  • To reduce machine idle times by providing enough in-process inventories at appropriate locations.
  • To take advantages of quantity discounts, economy of scale in transportation etc.
  • To decouple operations i.e. to make one operation's supply independent of another's supply. This helps in minimizing the impact of break downs, shortages etc. on the performance of the down stream operations. Moreover operations can be scheduled independent of each other if operations are decoupled.
  •  To reduce the material handling cost of semi-finished products by moving them in large quantities between operations.
  • To reduce clerical cost associated with order preparation, order procurement etc.
Inventory Costs
In order to control inventories appropriately, one has to consider all cost elements that are associated with the inventories. There are four such cost elements, which do affect cost of inventory.
  • Unit cost: it is usually the purchase price of the item under consideration. If unit cost is related with the purchase quantity, it is called as discount price.
  • Procurement costs: This includes the cost of order preparation, tender placement, cost of postages, telephone costs, receiving costs, set up cost etc.
  • Carrying costs: This represents the cost of maintaining inventories in the plant. It includes the cost of insurance, security, warehouse rent, taxes, interest on capital engaged, spoilage, breakage etc.
  • Stockout costs: This represents the cost of loss of demand due to shortage in supplies. This includes cost of loss of profit, loss of customer, loss of goodwill, penalty etc.
If one year planning horizon is used, the total annual cost of inventory can be expressed as:
Total annual inventory cost = Cost of items + Annual procurement cost + Annual carrying cost  +  Stockout cost
Variables in Inventory Models
D = Total annual demand (in units)
Q = Quantity ordered (in units)
Q* = Optimal order quantity (in units)
R = Reorder point (in units)
R* = Optimal reorder point (in units)
L = Lead time
S = Procurement cost (per order)
C = Cost of the individual item (cost per unit)
I = Carrying cost per unit carried (as a percentage of unit cost C)
K = Stockout cost per unit out of stock
P = Production rate or delivery rate
dl = Demand per unit time during lead time
Dl = Total demand during lead time
TC = Total annual inventory costs
TC* = Minimum total annual inventory costs
Number of orders per year = 
Total procurement cost per year = S.D / Q
Total carrying cost per year = Carrying cost per unit * unit cost * average inventory per cycle
Cost of items per year = Annual demand * unit cost
                                          = D.C
Total annual inventory cost (TC) = 
The objective of inventory management team is to minimize the total annual inventory cost. A simplified graphical presentation in which cost of items, procurement cost and carrying cost are depicted is shown in Figure 1 . It can be seen that large values of order quantity Q result in large carrying cost. Similarly, when order quantity Q is large, fewer orders will be placed and procurement cost will decrease accordingly. The total cost curve indicates that the minimum cost point lies at the intersection of carrying cost and procurement cost curves.
Inventory Operating Doctrine
When managing inventories, operations manager has to make two important decisions:
  • When to reorder the stock (i.e. time to reorder or reorder point)
  • How much stock to reorder (i.e. order quantity)
Reorder point is usually a predetermined inventory level, which signals the operations manager to start the procurement process for the next order. Order quantity is the order size.
nventory Modelling
This is a quantitative approach for deriving the minimum cost model for the inventory problem in hand.
Economic Order Quantity (EOQ) Model
This model is applied when objective is to minimize the total annual cost of inventory in the organization. Economic order quantity is that size of the order which helps in attaining the above set objective. EOQ model is applicable under the following conditions.
  • Demand per year is deterministic in nature
  • Planning period is one year
  • Lead time is zero or constant and deterministic in nature
  • Replenishment of items is instantaneous
  • Demand/consumption rate is uniform and known in advance
  • No stockout condition exist in the organization
The total annual cost of the inventory (TC) is given by the following equation in EOQ model.
The graphical representation of the EOQ model is shown in
Figure 2: Economic Order Quantity Model (EOQ Model)

Example 1
ABC manufacturers produces 1,25,000 oil seals each year to satisfy the requirement of their client. They order the metal for the bushing in lot of 30,000 units. It cost them $40 to place the order. The unit cost of bushing is $0.12 and the estimated carrying cost is 25% unit cost. Find out the economic order quantity? What percentage of increases or decrease in order quantity is required so that the ordered quantity is Economic order quantity ?
Economic Production Quantity (EPQ) Model
In EOQ model supply was instantaneous, which may not be the case in all industrial applications. If supply of items is gradual to satisfy a continuous demand, then supply line will be depicted by a slanted line
Figure 3 : Economic Production Quantity Model (EPQ Model)
In this situation, when the order is placed, the supplier begins producing the units and supplies them continuously. While new units are added to inventory, other units are being used. Thus, if delivery rate (P) > demand rate (D), the net result will be a net increase in the inventory level. The slope of replenishment line will thus be (P-D). Simillarly the slope of demand line will be (-D). The average inventory carried per year is
Example 2
The XYZ Company produces wheat flour as one of their product. The wheat flour is produced in the pack of 1kg. The demand for wheat flour is 40,000 packs/year & the production rate is 50,000 packs/year. Wheat flour 1kg pack cost $0.50 each to make. The Procurement cost is $5. The carrying cost is high because the product gets spoiled in few week times span. It is nearly 50 percent of cost of one pack. Find out the operating doctrine.