Mba III Operations Management [10mba33] Notes

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OPERATIONS MANAGEMENT 10MBA33 Syllabus MODULE -1 Introduction and Break even analysis Break even analysis- Break even analysis in terms of physical units Sales value and percentage of full capacity Break even for Multi Product situations Capacity expansion decisions

Production process Make or Buy decisions Equipment Selection decisions Production process selection decisions Managerial uses of break even analysis Limitations of Breakeven analysis.

MODULE –II FORECASTING Forecasting as a planning tool Forecasting time horizon Short and long range forecasting Sources of data Types of forecasting qualitative forecasting techniques Quantitative forecasting modelsLinear regression Moving average Weighted moving average Exponential smoothing Page 1

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Exponential smoothing with trends, Measurement of errors Monitoring and controlling forecasting models. MODULE – III Facility planning Facilities location decisions, Factors affecting facility location decisions Relative importance for different types of facilities. Facility location models. MODULE-IV Employee productivity Employee Productivity: - Productivity and work study Productivity and the standard of living, Productivity and the organization productivity, variables affecting lab our productivity, work content and time, work study and related working conditions and human factors. Method Study Introduction ;to Method study, Data collection, recording, examining, and improving work, Material flow and material handling study, Worker flow study, worker area study. Work measurement:Introduction to Work Measurement, Work sampling study, Time study and setting standards. Numerical problems on productivity measurements, time study and work standards.

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MODULE V Capacity planning Concept and overview of aggregation, demand and capacity options and strategies in production and services, capacity and value, financial impact of capacity decisions, aggregate planning types and procedure, capacity requirement planning, concepts of yields (productivity) and its impact on capacity. Capacity requirement planning, Materials requirement planning. Planning hierarchies in operations, aggregate planning, purpose, necessity and importance of aggregate planning, Managerial importance of aggregate plants, alternatives for managing demand d and supply, capacity augmentation strategies, Matching demand and capacity, demand chase aggregate planning, level production aggregate planning, capacity planning and steps. Resource requirements planning system, materials requirement planning, objectives of MRP, BOM, benefits of MRP. MODULE –VI MATERIALS MANAGEMENT Role of Materials- Management –materials and profitability, purchase functions, procurement procedures including bid systems. Vendor selection and development, vendor rating, ethics in purchasing. Roles and responsibilities of purchase professionals, concepts of lead time, purchase requisition, purchase order, amendments, and forms used and records maintained. Inventory management, ABC VED, and FSN analysis,. Inventory costs, Inventory models –EOQ, safety stocks, Re order point, Quantity discounts. Stores-types, functions, roles responsibilities, Inventory records, Numerical. Problems on vendor rating, ABC analysis, Inventory models, discounts

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MODULE-VII Quality management -1 Basic concepts of quality of products and services, dimensions of quality, Relationships between quality, productivity, costs, cycle time and value. Juan‘s quality trilogy. Impact of quality on costs-quality costs. Deming‘s 14 principles. Quality improvement and cost reduction- 7QC tools and 7 new QC tools, PDCA cycle, Quality circles, quality Function Deployment and its benefits. Quality systems- Need, benefits, linkage with generic strategies, ISO-9000- 2000 clauses, coverage, QS 9000 clauses, coverage, linkages with functional domains like production marketing. Six sigma concepts, organizing for continuous improvement. Excellence models, awards and standards awards- MBNQA, Deming‘s prize, Balbriggan award, their main focus. Role of management in implementing quality systems. MODULE – VIII Quality management –II Control charts – meaning and definition Types of control charts Control charts for variables Control charts for attributes Numerical problems on control charts

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CONTENTS Module

Chapter Name

PAGE NOS.

1

Introduction and Break even analysis

6-11

2

Forecasting

12-28

3

Facility planning

29-36

4

Employee productivity

37-44

5

Capacity planning

45-58

6

Materials management

59-71

7

Quality management –I

72-90

8

Quality management –II

91-98

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MODULE –I Introduction and Break even analysis INTRODUCTION TO BREAK –EVEN ANALYSIS:The term ‗Operations ‗refers to a function or system that transforms Inputs into outputs of

a greater value. Operations are often defined as a Transformation or conversion process wherein Inputs such as Materials, Machines, Labour and capital are transformed into outputs. Operations management: - It is defined as the design, operation, and Improvement of the systems that create and deliver the firm‘s primary products and services.

FACTORS AFFECTING PRODUCTION AND OPERATIONS:a) Reality of Global competition. b) Quality, Customer service and cost challenges. c) Rapid Expansion of advanced production Technology. d) Continued growth of service sector. e) Scarcity of production resources. f) Social responsibility Issues PRODUCTIVITY:The term productivity describes how well a production manager achieves productivity use of the resources of the firm. Productivity is an index or measure of the effective use of resource. It is expresses as

out put Input

Productivity measures includes:a) Labour productivity b) Machine Productivity c) Capital Productivity. d) Energy Productivity. Page

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Competitiveness: - Competitiveness is a crucial factor in determining the survival and growth of a firm. Competitiveness is how effectively an organization meets the needs of customers relative to other firm which offer similar goods or services. The dimensions of competitiveness that Measure the effectiveness of the production function are – a) Cost or price. b) Quantity. c) Product / service differentiation d) Dependability as a supplier. e) Flexibility. f) Time to perform certain activities. Break-Even analysis:Break even- analysis, also called profit analysis or cost-volume-profit (CVP) analysis is used to determine the number of units of a product (volume) to sell or produce that will equate total sales revenue with total production cost. The three components of a break-even-analysis are volume, cost and profit. Profit Revenue cost Of profit

TR (Total Revenue) TC (Total Cost) Loss

CV (Total variable cost)

C1 (Fixed Cost)

Quantity –Q (B E Volume) Page

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Application of Break- Even analysis a) Evaluating products or Services Break –Even analysis can be used to evaluate the profit potential of a new or existing product or service. This technique provides answers to following Questions:a) Is the projected sales volume of the product service sufficient to break-even? b) How low the variable cost must be per unit to break-even? c) How low must the fixed cost be to break-even? d) How do price levels affect the Break-even volume?

Q=

Where Q = F= P= C=

F P-C

Break –Even Volume Fixed Cost Unit Selling Price Variable Cost / Unit

b) Evaluating alternative Processes (Make –or – buy) Operations managers are faced with problem of choosing between two or more processes or between an Internal Process (Make) and buying products / components from outside vendors (buy) this is usually referred as Make –or-buy decision. The assumption is that either decision does not affect revenues. The operation manager must study all the costs and advantage of each approach and find quantity at which total Costs equal to Total revenues.

Fm -Fb Q =

Cb - Cm

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Fm = Fixed cost for make option Fb = Fixed cost for buy option Cb = Variable cost for buy option. Cm = Variable cost for make option.

B-E-point

Total cost (buy)

Cost Total cost (make) Fixed cost (make) Fixed cost (buy) Qty

Q (B.E.volume)

Break-Even analysis for Multi product cases

Most Manufacturers or sellers have a variety of offerings (goods or services). Each offering may have different selling price and variable cost. B.E analysis can be used to reflect the proportion of sales for each product. The formula uses the ‗Weighting‘ factor indicating each products contribution by its proportion of sales F = Fixed cost Vi = Variable cost / unit P i= Unit selling price

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Wi = Percentage of sales of 1th product of Total Sales.

Major Process Decisions:

1. Process Choice- Determines whether resources are organized around products or processes in order to implement the flow strategy. It depends on the volumes and degree of customization to be provided. 2. Vertical Integration: - Is the degree to which a firm‘s own production system handles the entire supply chain starting from procurement of raw materials to distribution of finished goods.

3. Resource Flexibility: - Is the case with which equipments and workers can handle a wide variety of products, levels of output, duties and functions.

4. Customers involvement:- Refers to the ways in which customers become part of production process and the extent of their participation.

5. Capital Intensity:- Is the mix of equipment and Human Skills in a production process. Capital Intensity will be high if the relative cost of equipment is high when compared to the cost of Human Labour.

MANAGERIAL USES OF BREAK-EVEN ANALYSIS 1. It helps in arriving at the Fair value of the profits of the production firm. 2. It helps in understanding the relationship between costs, volume and profits. 3. It suggests the volume of sales required to earn the desired level of profit. 4. It helps in formulating the pricing policies for the firm. 5. It evaluates performance for the purpose of control. 6. It helps in understanding the effect of changes in sales mix on the profits. 7. It assists the management in taking strategic decisions such as make or buy, product add or Drop, proper sales Mix, acceptance or rejection of an offer etc

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LIMITATIONS OF BREAK-EVEN ANALYSIS:1. The analysis assumes a linear revenue function and a linear cost function. 2. The analysis assumes that whatever is produced will be sold. 3. The analysis assumes that fixed and variables cost can be accurately Identified. 4. For multiple product analysis, the sales mix is assumed to be known and constant. 5. The selling price and costs are assumed to be known with certainty.

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MODULE –II FORECASTING A Forecast is an Estimate about the future.‖ Forecasting: - It is defined as ―estimating the future demand for products and services and the

resources necessary to produce and services and the resources necessary to produce these outputs. Forecasting is the art and science of predicting future Events. It is not a mere guess or prediction about the future without any rational basis. It involves Data processing and Data mining Techniques to come out with conclusions which are more accurate and reliable. Forecasting as a planning Tool:-

Managerial Decision making is often complicated due to an element of uncertainty in variables affecting the Decision-making process. It is very important to device a Mechanism which enables the planning process to happen more comprehensive by suitable Techniques. Forecasting is that branch of operations Management which addresses such issues provides the Manager with a set of Tools and Techniques for the Estimation Process. Forecasts are Estimates of Timing and Magnitude of the occurrence of future events. Key functions of Forecasting: It is an Estimation Tool.  A way of addressing the complex and uncertain environment surrounding business decision-making.  A Tool for predicting events related to operations planning and control.  A vital prerequisite for the planning process in organization

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Why do we forecast? 1. We are operating in a Dynamic and complex Environment. 2. Short term fluctuations are seen is production systems. 3. To ensure Better Materials Management. 4. To Incorporate Rationalized Manpower decisions. 5. To establish a basis for planning & scheduling for strategic Decision making. Forecast Time Horizon The forecast Time Horizon can be classified into three sectionsa. Short-term b. MediumTerm c. Longterm Parameters

Short-term

Medium-term

Long-term

Duration

1 – 3 Months

12-18 Months

5-10 Years

nature of Decision

Purely tactical

Tactical as well as

Purely strategic

strategic Considerations Research Methods

Random (short-term effect)

Seasonal & cyclical

Long-term trends &

effects

business cycles

Extrapolation of Trends,

Collective opinion , Time

Technological

Judgment, Exponential

series ,Regression

Economic,

smoothing

Judgment

Demographic Marketing studies Judgment

nature of Data

Mostly Quantitative

Subjective and

Largely subjective

Quantitative Degree of

Low

Significant

High

Revising qtly production

Annual production plan,

New product

Uncertainty Examples

plan, rescheduling supplies

capacity augmentation,

Introduction,

of Raw Material

New Business

Facilities Location

Development

Decisions Page 13

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Sources of Data Forecasting is often only as good as the quantity and quality of Data available.

1. Sales force Estimates: - The sales force constitutes sales representative and other field operations staff. The information includes - Actual consumption - Changing patterns in consumption - Performance of competitor brands. - Over all patterns in the Market share and Market growth. Organizations can make end-use analysis. This data is very useful in short term forecasting and mid- course corrections in production and sales planning. 2. Point of sales (POS) Data Systems:This system captures data at the point of sale using POS system. With this Technology, as a customer buys a unit of an organization‘s product at a retail counter. The information is captured and instantaneously transferred to a common data base. The organization shall periodically analyse these data base for better Inventory management and sales planning. Eg, Wat-mart. 3. Forecast from supply chain patterns:Distributors and Retailers form supply chain pattern obtaining POS data is often not easy. Therefore it is necessary to rely on supply chain patterns to obtain actual data on actual sales within a period. These estimates are crucial for accurate forecasting pf future Demand. 4. Trade Industry Association Journals:These are handy in case of Long-term forecasting. They provide syndicated and researched data pertaining to the sector in which the organization is operating. Several Market

Research firms such as ORG- MARG and Management consultancy firms also provide classified Information.

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5. B2B portals / Market places:B2B (Business 2 Business) portals provide necessary information about company‘s profile, product range, Market share, latest developments in R & D and other vital inputs about Market. Internet is the best source of B2B inputs. 6. Economic surveys and Indicators:Macro Economic Trends indicate the emerging Trends in consumption patterns of several classes of goods and services. Eg, HDTV (High Definition TVs) Demand for HDTV are influenced by Income level distribution in the population. Prevailing taxation policies. Disposable Income. Literary levels. Rate of urbanization. For Economic surveys. a) Central statistical organization (CSO) Centre for Monitoring Indian Economy (CMIE) TYPES OF FORECASTING:a) Technological Forecasts. b) Economic Forecasts. c) Demand Forecasts.

a)

Technological Forecasts: - are concerned with rates of Technological Progress.

Technological charges will provide many companies with new products and materials to offer for sale. Even if the products can be developed with a new or improved technology using machinery and equipment.

b) Economic forecasts: - are statements of expected future business conditions published by government agencies. These forecasts address the business cycle by predicting. Inflation rates, Money supplies, Housing Statistics and other Economic Indicators such as Tax revenues, level of Page

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employment, GNP. These forecasts give ideas about long range intermediate range business growth to business organizations. c) Demand Forecasts:- are projections of Demand for a company‘s product or services. These forecasts are also called a sales forecast which gives the expected level of Demand for a company‘s goods or services throughout some future period and usually provide the basis for company‘s planning and control decisions.

Forecasting Approaches 1. Qualitative Approach 2. Quantitative Approach. Forecasting Approach Qualitative Approach

Quantitative approach

Time series Model

Casual Methods

Jury of Executive opinion Sales

Naïve approach

Trend projection

force composite Method Market

Moving averages

Research Method Delphi

Exponential smoothing Correlation Method

Technique

linear regression

a) Jury of Executive Opinion: - It is a forecasting Technique in which the opinions of a small group of high-level executives (Managers) are taken. Based on which a group estimate of demand is obtained as the forecast.

Advantages:1. Uses experience and knowledge of experts. 2. Can be used for Technological forecasting. 3. Can be used for forecasting demand for new product. 16 OPERATIONS MANAGEMENT

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4. Can be used to modify existing forecast to account for unusual circumstances.

Disadvantages:1. It is costly process. 2. It sometimes gets out of control or gets delayed. 3. Difficult to obtain consensus opinion of several experts. b) Sales Composite Method: - This is also known as ―pooled sales force estimate‖. Each sales

person estimates what sales will be in his / her territory. These estimates are then reviewed to ensure that they are realistic.

Advantages:-

This is more practical, realistic and updated information.

-

It helps in Inventory Management, distribution and sales force staffing.

Disadvantages:-

It may affect the sales forecast.

-

Sales people may be unable to distinguish between what customers would like to do and what they actually will do.

-

Sales people may be overly influenced by their recent experiences.

Market Research Methods (Consumer Survey Method) This is a systematic approach to determine consumer interest in a product or service by conducting a consumer survey and sample consumer opinions. This method may be used to forecast Demand for he short, Medium and long term. Advantages:This is better compared to sales force composite method as the customers directly gives their opinion. This information is more primary in nature and cannot be obtained in other methods.

Disadvantages:1. It is time consuming. 2. It is very costly affair / Expensive.

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3. The response rate for Mailed Questionnaire is very poor. 4. The survey result may not reflect the opinions of the market.

1. Delphi Method: - In this method opinions are solicited from a number of other managers and staff personnel. The decision makers consist of group of 5 to 10 experts who will be making actual forecast. The staff personnel assist decision makers by preparing, distributing, collecting and summarizing a series of questionnaires and survey results. The managers whose judgments are valid are the respondents. This group provides input to the decision makers before forecast is made. Response of each respondent are kept anonymous which tends to encourage honest responses. Each new questionnaire developed using the information extracted from the previous ones. Thus enlarging the scope of information on which participants can base their judgment. The goal is to achieve consensus forecast. Advantages:-

This method can be used for long-range forecast product demand.

-

A panel of experts may be used as participants.

Disadvantages:-

Process can take a long time.

-

Responses may be less Meaningful.

-

High accuracy may not be possible.

-

Poorly designed Questionnaire will result in ambiguous or false conclusions.

Quantitative Approach:-

Naïve Approach: - The simplest way to forecast is to assume that forecast of Demand in the next period is equal to the actual Demand in the most recent period. Eg. If the demand for a product in Jan 2009 is 160. Then demand for the product in Feb-2009 is also 160.

Moving averages Method:(i) Simple Moving average. (ii) Weighted Moving average.

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∑ Demand in previous n periods Moving average = n ii) Exponential smoothing Method It is a sophisticated weighted moving average method that is still relatively easy to understand and use. It requires three items of data -

This period‘s forecast.

-

Actual Demand for the period.

-

Smoothing constant.

Ft = F t-1 + (A t-1 + F t-1) Ft = Forecast for the period ―t‖ F t-1 = Forecast for the previous period (t-1) = Smoothing constant (0.05 – 0.5)

A t-1 = Actual Demand for the previous period. Exponential smoothing with trend Adjustment.

F I T (t) = Ft + Tt F I T = Forecast Including trend. Ft

= exponentially smoothed forecast.

Tt

= exponentially smoothed trend.

Ft = (At-1) + (1- ) (F t-1 + T t-1)

(Ft – Ft-1) + (1- ) (Tt-1)

Tt = Forecast Error (FE)

FE= Actual Demand – Forecast Demand. 19 OPERATIONS MANAGEMENT

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Mean Absolute Deviation (MAD) MAD = ∑ (forecast errors)

MONITORING AND CONTROLLING FORCASTS

Once a forecast has been completed, its need to be monitored and corrected periodically by determining why actual demand differed significantly from that projected. This can be done by setting upper and lower limits on how much the performance characteristic of a forecasting model can deteriate before we change the parameters of the model TRACKING SIGNAL A measurement of how well the forecast is predicting actual values. Tracking signal=

Running sum of forecast errors Mean absolute deviation

Four possible components: Trend (secular trend) -- Long term pattern or direction of the time series

Cycle ( cyclical effect) -- Wavelike pattern that varies about the long-term trend, appears over a number of years e.g. business cycles of economic boom when the cycle lies above the trend line and economic recession when the cycle lies below the secular trend.

Seasonal variation -- Cycles that occur over short periods of time, normally < 1 year e.g. monthly, weekly, daily. Random variation (residual effect) --Random or irregular variation that a time series shows Could be additive: Yi = Ti + Ci + Si + Ii 20 OPERATIONS MANAGEMENT

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Or Multiplicative: Yi = Ti x Ci x Si xI Forecasting using smoothing techniques

The two commonly used smoothing techniques for removing random variation from a time series are moving averages and exponential smoothing. Moving average: (MA) Moving averages involve averaging the time series over a specified number of periods. We usually choose odd number of periods so we can center the averages at particular periods for graphing purposes. If we use an even period, we may center the averages by finding two-period moving averages of the moving averages. Moving averages aid in identifying the secular trend of a time series because the averaging modifies the effect of cyclical or seasonal variation. i.e. a plot of the moving averages yields a ―smooth‖ time series curve that clearly shows the long term

trend and clearly shows the effect of averaging out the random variations to reveal the trend.

Moving averages are not restricted to any periods or points. For example, you may wish to calculate a 7-point moving average for daily data, a 12-point moving average for monthly data, or a 5-point moving average for yearly data. Although the choice of the number of points is arbitrary, you should search for the number N that yields a smooth series, but is not so large that many points at the end of the series are "lost." The method of forecasting with a general L-point moving average is outlined below where L is the length of the period. Forecasting Using an L-Point Moving Average

1. Select L, the number of consecutive time series values Y1, Y2. . . YL that will be averaged. (The time series values must be equally spaced.)

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2. Calculate the L-point moving total, by summing the time series values over L adjacent time periods. 3. Compute the L-point moving average, MA, by dividing the corresponding moving total by L

4. Graph the moving average MA on the vertical axis with time i on the horizontal axis. (This plot should reveal a smooth curve that identifies the long-term trend of the time series.) Extend the graph to a future time period to obtain the forecasted value of MA Exponential smoothing: One problem with using a moving average to forecast future values of a time series is that values at the ends of the series are lost, thereby requiring that we subjectively extend the graph of the moving average into the future. No exact calculation of a forecast is available since the moving average at a future time period t requires that we know one or more future values of the series.

Exponential smoothing is a technique that leads to forecasts that can be explicitly calculated. Like the moving average method, exponential smoothing de-emphasizes (or smoothes) most of the residual effects.

To obtain an exponentially smoothed time series, we first need to choose a weight W, between 0 and 1, called the exponential smoothing constant. The exponentially smoothed series, denoted Ei, is then calculated as follows: Ei= W Yi+(1- W)Ei-1 (for i>=2) where Ei = exponentially smoothed time series

Yi = observed value of the time series at time i Ei-1 = exponentially smoothed time series at time i-1 W = smoothing constant, where 0