Financial Analytics

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Financial analytics is a field that gives different views of a company’s financial data.

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Module 2 — Part 1 FINANC'AL
Course Plan Understanding Risk, credit risk analysis, fraud detection and prevention analytics, analytics in banking and financial services - analytics in retail banking and wealth management
Financial Analytics Financial analytics is a field that gives different views of a company's financial data. It helps to gain in depth knowledge and take action against it to improve the performance of your business. Financial analytics has its effect on all parts of your business. Financial analytics plays a very important role in calculating the profit of a business. Financial analytics helps you to answer all your business questions related to your business and also lets you to forecast the future of your business.
Uses of Financial Analytics Understand the performance of an organization Measure and manage the value of tangible and intangible assets of an organization Manage the investments of the company Forecast the variations in the market Increase the functionalities of information systems Improve the business processes and profits
Reasons for using Financial Analytics 1. Business Models There are three new business models which form the basis of financial analytics i. Business to Business ii. Business to Consumer iii. Business to Employee 2. Changing role of the financial department Most of the finance functions are automatic and requires only fewer resources to manage them. This enables the finance executives to concentrate more on the business goals rather than just focusing on processing and reconciling transactions.
3. 4. Reasons for using Financial Analytics Business Processes Businesses are becoming more complex these days due to the advancement of technologies. Lot of questions arise in the mind of the business people. Analytics provide the answers to all these questions. Financial analytics lets the managers and executives in an organization to have access to more accurate and detailed financial information of the organization. Integrated Analytics These days companies use integrated financial analytics to face the competition in the financial analytics market place. Because of using such integrated financial analytics companies will be able to analyze and share the information to the sources inside and outside the organization. Organizations should use integrated financial analytics to survive in the new economy.
• Risk Analysis A process that helps identify and assess potential threats that could affect the success of a business or project. Includes means to measure, mitigate and control risks effectively. An essential tool when the work involves threats and risks. Many industries have recognized the increasing importance of risk analysis such as: Medical Food and beverage Automotive Transportation Military Aerospace Nuclear
Risk Analysis Many companies have established risk management functions and procedures to perform risk analysis on a continual basis. These companies may need to assess: Their financial stability. The financial feasibility of an investment. The impact of new government policies. The impact of new competitors coming into the market. Used to assess the potential health effects resulting from human exposures to hazardous agents or situations. Widely used in manufacturing environments to improve safety and manage potential risks in production lines. Used in all types of engineering of sophisticated systems to ensure safety and reliability of systems, processes and products.
Risk Analysis A key process area in project management. Helps deciding whether to proceed with a project or not. • Ensures only projects with the highest chance of success are selected. Used in project planning and during project implementation to evaluate how a project can be successfully completed. If risks are not considered and controlled, you will not be able to minimize their impact on the schedule, scope, cost or quality. It is possible for a project to be stopped for example if the availability of resources become an issue, or the potential benefits might not be sufficient.
Benefits of Risk Analysis • Saves time and money. Reduces the level of uncertainty. Decreases the impact of negative events. • Improves project controls. • Improves organizational learning. 10
l. 2. 3. 4. Stages of Risk Analysis Risk identification. Risk assessment. Response planning and implementation. Risk monitoring and control
Risk Identification Determining and documenting the potential risk that could occur. An iterative process: As new risks may evolve or become known as the project progresses. Identified risks and their characteristics are recorded in the risk register. 12
Risk Identification Methods and Sources • People who have gone through similar projects or events • Expert opinions • Failure history analysis SWOT analysis Assumption logs Observations and checklists • 1--lazard analysis • Scenario analysis Brainstorming.
Risk Assessment Helps to evaluate the significance of each risk. Highlight the risks that present the greatest threat on the overall objectives. Risks should be prioritized according to their potential impact and probability of occurring. Risk Impact is the effect the risk will cause if it occurs. Risk probability is a measure of the likelihood of the risk occurring.
Risk Response Planning and Implementation • You need now to respond to the assessed risks by developing options and actions to reduce the probability or impact. Here you will apply strategies to deal with them effectively. This process should be: Realistic Cost effective Agreed upon by key stakeholders Owned by a responsible person.
Risk Response Planning and Implementation Response Strategies: l. 2. 3. 4. Avoiding the risk Transferring the risk Mitigating the risk Accepting the risk altogether 16
Risk Response Planning and Implementation Avoiding the risk : It usually involves changing in the project plan such as: Extending the schedule Reducing the scope Spending money or hiring resources to eliminate the risk. An example is when you hire a more skilled resource who is likely to get the tasks done in less time. 17
Risk Response Planning and Implementation Transferring the risk: Sharing the risk with someone else. It is simply handling off the risk to another team, organization or a third party. • Examples are: Outsourcing a service Buying an insurance 18
Risk Response Planning and Implementation Mitigating the risk: • Involves carrying out work now to reduce the probability and/or impact of a risk to be within the acceptable threshold limits. It may include preventive, detective or testing possible ways to reduce the risk. • Examples are: Backing up the data to an offsite location Choosing a more stable supplier 19
Risk Response Planning and Implementation Accepting the risk altogether: An acceptable risk is the one that is tolerated because: There is nothing you can do to prevent or mitigate it. It is costly. It is difficult to implement. • One of the common acceptance strategies is to come up with a contingency plan to cope with its consequences. 20
Controlling Risks • Improves the efficiency of the risk analysis process. . Involves: Monitoring and re-assessing risks overtime. Identifying new risks. Evaluating the effectiveness of the risk response strategies. • Performance information should be reviewed regularly: Schedule progress Costs incurred. Risks and risk response plans should be reviewed in regular meetings to ensure plans are being implemented. • In these meetings, key risks should be given more attention and new risks should be raised and discussed
Credit Risk The default risk on a debt that arises from a borrower who fails to make the required payments is called Credit Risk. Any lender would include this as a first resort which includes principal and interest along with disruption to cash flows and the collection cost. The loss may be partial or even complete in many cases. Higher borrowing costs are always associated with higher credit risk levels in an efficient market. Due to this reason, the cost of borrowing can be used to conclude credit risks based on the assessment by the participants of the market. A credit check is performed by the lender to reduce this credit risk on the prospective borrower and it may require the borrower to take insurance which guarantees from a third party of the payment to the lender. Credit risk increases when the borrowers, willingly or unwillingly, are unable to pay. 22
Credit Risk Analysis The risks are calculated on the borrower's ability to repay the loan. • To assess the risk credit risk the lenders, look at the five C's of the borrower. The five C's are credit history, capacity to repay, capital, the loans condition, and associated collateral. • Some companies have a dedicated department only for assessing the credit risk of its current and potential consumers. • Due to the help of technology businesses can now analyze the data quickly and assess customers risk profile. If an investor is evaluating to buy a bond, he will review the credit rating of the bond before the purchase is made. If the rating is low then the issuer is considered to have a high risk of default and alternatively, if it has a high rating then it is considered to be a safe investment. 23
Types of Credit Risk 1) Credit Default Risk The risk of loss which arises from the debtor being unlikely to repay the amount in full or when the debtor is more than 90 days past is the due date of credit payment, it gives rise to credit default risk. The Credit default risk impacts all the sensitive transactions which are based on credit like loans, derivatives or securities. Credit default risk is also checked by banks before approving any credit cards or personal loan.
Types of Credit Risk 2) Concentration Risk This is the type of credit risk which is associated with exposure of any single or group with the potential to produce large losses to threaten the core operations of a bank. It may arise in the single form of single name concentration even industry concentration. 25
Types of Credit Risk 3) Country Risk The risk which arises from a sovereign state when it freezes the payments for foreign currency overnight defaults or its obligation which is termed as sovereign risk. Country risk is exclusively associated with the performance of macroeconomics of a country and is also closely related to the political stability in the country. • Sudden instability, which tends to happen during the elections, results in high country risk. 26
Mitigation of Credit Risk 1. Risk-Based Pricing The lenders usually charge a higher rate of interest to borrowers who are defaulters. This practice is known as risk- based pricing. The lenders take into consideration the factors such as on purpose credit rating and loan to value ratio. 2. Credit insurance and credit derivatives Bondholders hedge the risk by purchasing credit derivatives or credit insurances. These contacts ensure the transference of the risk from the gender to the server against a specific amount of payment. Credit default swap is the most common form of credit derivative used in the market. 27
Mitigation of Credit Risk 3. Covenants Stipulations may be written by lenders to the borrowers which are called covenants. These are usually written into loan agreements such as a periodic report about the financial condition, refrain from paying dividends or further borrowing of amount or any other specific action that affect the company's financial position in a negative way or repayment of the full loan at the request of the gender in events such as borrower changes or changes in debt to equity ratio or change in interest coverage ratio. 4. Diversification Lenders diversify their borrower pools and reduce the risk. 28
l. 2. 3. Calculating Credit Risk A standardized credit score such as FICO score is determined of the borrower. The FICO score helps in determining the credit history, repayment capacity and creditworthiness of an individual. On one hand, the FICO score indicates the way in which an individual makes the repayment of his debts, it does not ensure repayment in the future. The next step in calculating credit risk would be to calculate Debt- to-income ratio. This is calculated by monthly recurring debts of a company and divided by gross monthly income. The individuals who have a score of less than 35% are considered as acceptable credit risks. The last step is to factor in the potential loan of the borrower. The potential loan would be the debt which can be taken by the borrower on the basis of his credit cards and other general creditworthiness. This gives a potential of loan and payment capacity of the borrower. 29
Fraud Wrongful or criminal deception intended to result in financial or personal gain. Fraud is an uncommon, well-considered, imperceptibly concealed, time-evolving and often carefully organized crime which appears in many types of forms. Credit card fraud Insurance fraud Corruption Counterfeit Product warranty fraud Healthcare fraud Telecommunications Fraud Money laundering Click fraud Identity theft Tax evasion Plagiarism 30
Fraud Triangle This basic conceptual model explains the factors that together cause or explain the drivers for an individual to commit occupational fraud. The model has three legs that together institute fraudulent behavior: Pressure Fraud Triangle Opportunity atjonalization 31
1. 2. 3. Fraud Triangle Pressure is the first leg and concerns the main motivation for committing fraud. An individual will commit fraud because a pressure or a problem is experienced of financial, social, or any other nature, and it cannot be resolved or relieved in an authorized manner. Opportunity is the second leg of the model, and concerns the precondition for an individual to be able to commit fraud. Fraudulent activities can only be committed when the opportunity exists for the individual to resolve or relieve the experienced pressure or problem in an unauthorized but concealed or hidden manner. Rationalization is the psychological mechanism that explains why fraudsters do not refrain from committing fraud and think of their conduct as acceptable.
Fraud Detection and Prevention Fraud detection refers to the ability to recognize or discover fraudulent activities, whereas fraud prevention refers to measures that can be taken to avoid or reduce fraud. The former is an ex post approach whereas the latter an ex ante approach.
Benefits of Fraud Detection and Prevention Reduced exposure to fraudulent activities Reduced costs associated with fraud Find out the vulnerable employees at risk to fraud Have organizational controls • Improves the results of the organization Gains the trust and confidence of the shareholders of the organization
l. 2. 3. 4. Methods of Fraud Detection Sampling Ad-Hoc Repetitive or Continuous Analysis Benford's Law 35
Sampling • Sampling is mandatory for certain processes of fraud detection. • Sampling will be more effective where there a lot of data population involved. Disadvantage : Sampling may not be able to fully control the fraud detection as it takes only few population into consideration. Fraudulent transactions do not occur randomly therefore an organization need to test all the transactions to effectively majority detect fraud. 36
Ad-Hoc Ad-Hoc is nothing but finding out fraud by means of a hypothesis. Test the transactions and find out if there are any opportunities for fraud to take place. Have a hypothesis to test and find out if there is any fraudulent activity occurring and then you can investigate on the same. 37
Repetitive or Continuous Analysis Repetitive or Competitive Analysis means creating and setting up scripts to run against big volume of data to identify the frauds as they occur over a period of time. • Run the script every day to go through all the transactions and get periodic notification regarding the frauds. This method can help in improving the overall efficiency and consistency of your fraud detection processes. 38
Benford's Law Benford's law can often be used as an indicator of fraudulent data. Benford's distribution is non-uniform with smaller digits more likely than the larger digits. Using Benford's law you can test certain points and numbers and identify those which appear frequently than they are supposed to and therefore they are the suspect. 39
Other Fraud Detection Methods Data Matching — This method will find out if there is any data which exactly matches with another data. • Sounds like — This is another powerful method where it identifies variations of valid company employee names. • Duplicates This is another method which is most commonly used by a lot of organizations to identify fraud as well as any error occurring within all the business transactions. Gaps — In this method you can find out the missing sequential data. For example if you have purchase orders which is issued by the company in sequential order and if anything is missing you can easily find out. 40
1. 2. 3. Example — Fraud Detection Three fraud detection methods used by Insurance company Social Network Analysis (SNA) - SNA method follows the hybrid approach to detect fraud. The hybrid approach includes organizational business rules, statistical methods, pattern analysis and network linkage analysis. Fraud Detection Predictive Analytics for big data - Predictive analytics uses text analytics and sentiment analysis to look at big data_for fraud detection. Predictive analysis has been widely used by a lot of organizations as it helps in proactively detecting frauds. Social Customer Relationship Management (CRM) - Social CRM is a process of fraud detection program. Linking social media to CRM increases the transparency with the customers. This transparency gains the customers trust over the organization. This customer centric eco system benefits the business to a great extent and also see through that the customers are in control. 41
1. 2. 3. 4. 5. 6. 7. 8. 9. 10. Steps of Fraud Detection Perform SWOT Build a dedicated fraud management team Build or buy option Clean data Lay out relevant business rules Setting the threshold Predictive Modeling Using SNA Build an integrated case management system leveraging social media Forward looking analytics solutions
Steps of Fraud Detection 1. Perform SWOT: Many organizations have realized the increasing the importance of fraud analytics. • But in a hurry they are opting for expensive fraud detection solutions that do not match with the company's strengths and weaknesses. Therefore organizations should do SWOT analysis before starting with fraud detection program in order make it work to the fullest. 2. Build a dedicated fraud management team: Traditional companies do not have a specific team for fraud detection. • But these days it is important to have a dedicated team who works to find and prevent frauds in the organization. The team should have a proper flow and a proper reporting fraud detection system. 43
Steps of Fraud Detection 3. Build or buy option: • Once SWOT analysis is over and team allocation is done it is important for the companies to decide how they want to implement analytics and what resources are required. Companies need to know whether they are capable of building an analytics solution for themselves or should they purchase an analytical fraud detection solution from an vendor. If there is a need to purchase then the company should do a research about the different fraud detection vendors and their products available in the market that fits their company. There are few important factors to be considered while purchasing fraud analytics solution like cost, user interface, scalability, ease of integration and others.
Steps of Fraud Detection 4. Clean data: Integrate all the databases in the organization and remove all unwanted things from the databases. 5. Lay out relevant business rules: Companies should come up with business rules after doing a research on the resources and expertise of the company. There are different types of fraud and few of which are specific to particular industry. The external vendor cannot build a robust fraud detection solution without getting the proper inputs from the organization or company. 45
Steps of Fraud Detection 6. Setting the threshold: Whether the solution is in-built or purchased from outside the company should provide boundary values for different anomalies. Thresholds are set using anomaly detection. If boundaries are set too high then there are chances of frauds to slip through in between. If the boundaries are set too low then a lot of time and resources are wasted. Therefore an organization should be very clever in determining the thresholds 7. Predictive Modeling: Data mining tools are used to build models that produce fraud propensity scores which is linked to unidentified metrics. After the scoring is done automatically, the results are established for review and further analysis. 46
Steps of Fraud Detection 8. Using SNA: SNA has proved to be the most effective fraud detection program by modeling relationships between various entities. 9. Build an integrated case management system leveraging social media Case management system lets an investigator to know about all the important findings that are relevant to an investigation and it can be either structured or unstructured data. Metrics are the indicators of fraud and it can be helpful for comparison at the organizational level or network level. 10. Forward looking analytics solutions Companies should always look out for any additional sources of data and should integrate them with the current fraud detection program to build the most efficient and effect fraud detection program. This will help you to eradicate any new frauds that might develop in the future.
l. 2. 3. 4. 5. 6. Methods of Fraud Prevention Neural Nets Bayesian Models Regression Analysis Monitoring Historical analysis "What-if' Shadowing 48
1. Methods of Fraud Prevention Neural Nets: Fraud detection methods based on neural network are the most popular ones. The advantages of neural networks over other techniques are that these models are able to learn from the past and thus, improve results as time passes. neural networks are non-linear statistical data modeling tools. They can be used to model complex relationships between inputs and outputs or to find patterns in data. NNs can produce best result for only large transaction dataset. And they need a long training dataset. 49
2. Methods of Fraud Prevention Bayesian Models: Bayesian networks are directed cyclic graphs which have nodes representing variables which may be observable parameters or unknown variables. Each node is associated with a probability function that takes as input a particular set of values for the node's parent variables and gives the probability of the variable represented by the node. The Bayesian Network provides a compact representation of joint probability distributions. The advantage is drawn from its representation power and generalization. They also offer good generalization with limited training data 50
3. Methods of Fraud Prevention Regression Analysis: Regression analysis is a mathematical technique which differs from other fraud detection techniques in that it involves processing comparatively low volumes of summary data through programs employing complex logic. The mathematics involved in regression analysis can be extremely complicated and the computations involved, if performed manually, would be tedious. However, the underlying principles are easy for the auditor to understand and the use of one of the many statistical software packages now available will isolate the auditor from the mathematics. 51
Fraud Analytics Fraud analytics is the combination of analytic technology and Fraud analytics techniques with human interaction which will help to detect the possible improper transactions like fraud or bribery either before the transaction is done or after the transaction is done. When analytics is added to traditional methods, it enhances the fraud detection capabilities and gives a new dimension to the fraud detection techniques. Fraud analytics also helps to measure the performance which will help you to standardize and have a control for constant improvement. 52
Benefits of Fraud Analytics Identify 1--lidden Patterns - Fraud analytics identify new patterns, trends and scenarios under which frauds take place, whereas traditional approaches miss such things. Data Integration - It combines data from various sources and public records that can be integrated into a model. • Enhance existing efforts - Fraud analytics does not replace the traditional rules based methods but it just adds up to your existing efforts to bring you more improved results Harnessing unstructured data - Fraud analytics helps in deriving the best value from unstructured data. Improve the performance - With the use of fraud analytics we can easily identify what is working for your organization and what is not working for your organization 53
Fraud Analytics Process Model Identify Business Problem Identify Data Sources Select the Data Preprocessing Clean the Data Transform the Data Analyze the Data Analytics 0 Intepret, Evaluate, and Deploy the Model post. processing
Fraud Analytics Process Model As a first step, a thorough definition of the business problem is needed to be solved with analytics. Next, all source data must be identified that could be of potential interest. This is a very important step, as data are the key ingredient to any analytical exercise and the selection of data will have a deterministic impact on the analytical models that will be built in a subsequent step. All data will then be gathered in a staging area that could be a data mart or data warehouse. Some basic exploratory analysis can be considered here using for instance OLAP facilities for multidimensional data analysis (e.g., roll-up, drill down, slicing and dicing). 55
Fraud Analytics Process Model This will be followed by a data cleaning step to get rid of all inconsistencies, such as missing values and duplicate data. Additional transformations may also be considered, such as binning, alphanumeric to numeric coding, geographical aggregation, and so on. • In the analytics step, an analytical model will be estimated on the preprocessed and • transformed data. In this stage, the actual fraud-detection model is built. Finally, once the model has been built, it will be interpreted and evaluated by the fraud Experts 56
Analytics in Banking & Financial Services Assignment 57
Analytics in Retail Banking Assignment 58
Ol,e - ional Analyti cs
Module 2 — Part 2 Demand Planning Forecasting Model building, Supply planning - Procurement and Strategic Sourcing, Inventory Modeling — Aggregate planning and resource allocation decisions, Make/Buy decision 60
Demand Planning Demand planning is the process of analyzing, evaluating and projecting the future requirements of customers within an IT environment. Demand planning deals with the overall use of IT infrastructure and resources by customers or external users and aims to predict future demand accordingly. Typically demand planning is used in product-oriented IT companies to ensure that the product development or production meet the demand of the users. Demand planning uses statistical analysis, best practices, and past and current demand cycles to evaluate future customer demand. It also serves as an input to capacity planning to provision required IT resources based on current and expected future demand.
Demand Forecasting Vs Demand Planning • • Forecasting is the process of predicting future events. Forecasting is one of the most important business activities because it drives all other business decisions. Decisions such as which markets to pursue, which products to produce, how much inventory to carry, and how many people to hire are all based on forecasts. The consequences can be costly in terms of lost sales or excess inventory that cannot be sold. l. 2. 3. Planning is the process of selecting actions in response to the forecast. Planning involves the following decisions: Scheduling existing resources Determining future resource needs Acquiring new resources 62
1. 2. 3. Principles of Forecasting Forecasts are rarely perfect: There are too many factors in the business environment that cannot be predicted with certainty. Forecasts are more accurate for groups than for individual items: Higher degree of accuracy can be obtained when forecasting for a group than for individual items , their individual high and low items cancel each other out. Forecasting are more accurate for shorter than longer time horizons: Data does not change much in the short run.
Need of Forecasting Type Short-T erm L Ong-Term Forecas Proper Prod uction Planning Red ucing Costs of the Firm Determining appropriate Price Policy Setting Sales Targets and establishing ontrols and incentives Forecasting Short-Term Financial Requirements Planning of a new unit or expansion of Production planning Planning long- Term Financial Planning M anpower Requirernents Implementation of Project Effective Control 64
Forecasting Model Building Qualitative Methods 1. Characteristics Based on human judgment, 2. Strengths 3. Weaknesses opinions; subjective and nonmathematical. Can incorporate latest changes in the environment and "inside information." Can bias the forecast and reduce forecast accuracy. Quantitative Methods Based on mathematics; quantitative in nature. Consistent and objective; able to consider much information and data at one time. Often quantifiable data are not available. Only as good as the data on which they are based. 65
Forecasting Model Building u a Consumer's Survey Method Sales Force Opinion Method Delphi Method Past Analogy Executive Opinion Nominal Group Techniques uan a ve orecas n etho Linear Regression analysis Methods Time Series Methods Trend Projection Method Econometric Model 66
1. 2. 3. Consumer's Survey Methods The most direct method of estimating demand in the short- run is to conduct the survey of buyers' intentions. The consumers are directly approached and are asked to give their opinions about the particular product. It is of three types: Complete Enumeration method Sample Survey method End-Use Method
1. 2. 3. Consumer's Survey Methods Complete enumeration method Under this method all the consumers of the product are interviewed and on the basis of the information collected, the demand forecast is made. Sample survey method When the number of consumers is large, this method is used. In this method few selected consumers are interviewed. The selection of the consumer is done through random, stratified sampling technique. This method is based on the assumption that the selected sample represent the population. End — use method The consumers end use method is used to obtain use wise or sector — wise demand forecasts. In this method it is possible to forecast demand separately for different sectors. 68
Consumer's Survey Methods Advantages This method is free from any personal bias of the forecaster. The forecast is based on the first hand information from the consumers. The sample survey is less costly and less time consuming than complete enumeration, but equally reliable if the sample is representative in character. Disadvantages The Complete enumeration method is very costly It is time consuming The manpower required for the survey is also large The method cannot be used if the number of consumers is very large and scattered The method is not reliable because, In the case of household there is no consumers, regularity of intentions Faced with multiple choices or alternatives, they cannot predict their own choices. 69
Sales Force Opinion Method This method is also known as "Collective Method" or "Sales Force Polling" or "Reaction Survey" method. • Under this method the responsibility for estimating the expected sales is placed on salesmen. • Salesmen being closest to the consumers have the knowledge of the requirements of the consumers, their reactions to the product. These estimates of individual salesmen are consolidated to find out the total estimated sales. 70
Sales Force Opinion Method Advantages The method is simple and easy to be used. It involves minimum of statistical work and hence, does not require any technical expertise. It does not cost much. It is realistic because it is based on personal and first hand knowledge of salesmen. It is useful in forecasting the sales of new products. Disadvantages Being Subjective, the forecast is likely to be influenced by the personal bias of the salesmen. Salesmen may understate the forecast if their sales quotas are to be based on it. Its usefulness is limited to the short period only. The salesmen may not be aware of wider economic changes which affect demand.
Delphi Method This technique was developed by Olaf Helmer, Dalkey and Gordon in the late 1940s. Under this method a panel of internal and external experts are selected and they kept physically away from each other. There is a coordinator who acts as an intermediary among the panelists. Internal Experts Coordinator External Experts Coordinator prepares a questionnaire and sends it to the panelists. They express their views anonymously. Each expert will be given an opportunity to react the reasons advanced by others. The process will be repeated until some sort of unanimity is among all experts or issue causing the disagreement are clearly defined. It is more popular in forecasting non-economic rather than economic variables. 72
Delphi Method Advantages It does not take much time. The cost is low. The method is useful for new products • Disadvantages The opinions are subjective Good and Bad estimates are given equal weight age
• Past Analogy It is forecasting technique based on sales history that is analogous to current situation used with or without demand data like sales history of a similar product, buying behavior of consumer for product x history of feedback of consumer for product b. This analogy means past data used for future forecasting and using that past pattern to predict future sales or demand. Management can do estimation based on historical data. This technique is very simple as generally used to launch new product in market by surveying initial demand pattern or customer profile. Old data on similar product or service can be analyzed on past records and further will be used to develop forecast for new product. For example, one way to predict the sales of cold drink "xa" is to analyze an existing product "X" which is nearly similar the new product in terms of customer profile, demand pattern , buying pattern for sales of the product. 74
Executive Opinion Method • Under this method opinions are sought from the executives of different discipline i.e., marketing, finance, production etc. and estimates for future demands are made. Thus, this is a process of combining, averaging or evaluating in some other way the opinions and views of the top executives. 75
Executive Opinion Method Advantages The techniques is quite easy and simple • No need of elaborate statistics • Only feasible method to follow Disadvantages No factual basis of such forecast No accuracy Responsibility for the accuracy of data cannot be fixed on any one 76
Nominal Group Technique The nominal group technique is a decision making method for use among groups of many sizes, who want to make their decision quickly, as by a vote, but want everyone's opinions l. 2. 3. 4. 5. taken into traditional voting. Introduction and Explanation Silent Generation of Ideas Sharing Ideas Group Discussion Voting and Ranking dvcussjon and Output ranked ist Ot items 77
Quantitative Forecasting Methods ve orecas Linear Regression analysis Methods Tinxe Series Methods Trend Projection Method Least Square Method Econometric Model 78
Linear Regression Analysis Linear regression analysis establishes a relationship between a dependent variable and one or more independent variables. • In simple linear regression analysis there is only one independent variable. If the data is a time series, the independent variable is the time period. The dependent variable is whatever we wish to forecast. 79
Linear Regression Analysis Regression Equation: Y = a + bX Where, Y = dependent variable X = independent variable a = y-axis intercept b = slope of regression line . Solution: a = (EX2) ( EY) -(EX)2 -(EX)2
Linear Regression Analysis Example : The data of a firm relating to sales and advertisement is given below .1f the manager decides to spend Rs 30 mill in the year 2005 what will be the prediction for sales. YEAR 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 10 12 10 15 18 20 21 25 45 50 55 58 58 72 70 85 78 85
Linear Regression Analysis ADÆXmi11 SALES('OOOO 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 N-IO 10 12 10 15 18 20 21 144 ) units 45 55 EY-656 25 100 144 IOO 225 324 441 625 2448 225 550 696 580 1080 1260 1700 1638 2125 EX Y-10254
Linear Regression Analysis • Using formula the caluculated values of a & b are : a 34.54 2.15 Therefore, for Y =a+ b x Y=34.54 +2.15 x , Y =34.54+2.15 99 Thousand
Time Series Method 1. Naive me Series Models 2. Moving Avera e a) simple b wei hted . Exponential Smoothin
Time Series Method What is a Time Series? • Set of evenly spaced numerical data - Obtained by observing response variable at regular time periods • Forecast based only on past values —Assumes that factors influencing past, present, & future will continue . Example Year: 1995 1996 1997 1998 1999 Sales:78.7 63.5 89.7 93.2 92.1 — Time series data is a sequence of observations — • collected from a process • with equally spaced periods of time 85
Time Series Method Time series consist of four components: 1. 2. 3. 4. Seasonal variations that repeat over a specific period such as a day, week, month, season, etc., Trend variations that move up or down in a reasonably predictable pattern, Cyclical variations that correspond with business or economic 'boom-bust' cycles or follow their own peculiar cycles, and Random variations that do not fall under any of the above three classifications. Cycl is—a I Trend 86
Time Series Method - Naive Approach Demand in next period is the same as demand in most recent period May sales = 48 June forecast = 48 Usually not good This approach takes a temporary change as a permanent change. 87
Time Series Method - Simple Moving Average A moving-average forecast uses a number of historical actual data values to generate a forecast. It is useful if we can assume that market demands will stay fairly steady over time. An n-period moving-average model: At _ 1 + At _ 2 + + At _ n n Usually the n is 3, 6, 9 periods. n : no: of periods to be averaged F t : Forecast for period t A : Actual demand for period t-l 88
Time Series Method - Simple Moving Average EXAMPLE: a) b) c) Compute a three-week moving average forecast for the arrival of medical clinic patients in week 4. The numbers of arrivals for the past three weeks were as follows: Week 2 3 Patient Arrivals 400 380 411 If the actual number of patient arrivals in week 4 is 415, what is the forecast error for week 4? What is the forecast for week 5? 89
Time Series Method - Simple Moving Average SOLUTION a. The moving average forecast at the end of week 3 is 411 +380 + 400 2 3 Patient krivals 411 = 397.0 3 b. The forecast error for week 4 is c. The forecast for week 5 requires the actual arrivals from weeks 2 through 4, the three most recent weeks of data 415+ 411 + 380 = 402.0 3 go
Time Series Method - Weighted Moving Average Weights can be used to place more emphasis on recent values or older values. A n-period weighted moving average: wt-1At-1 + wt-2At-2 + A t n t-n wt-l + wt-2 + + wt-n w i : Weight attached to the actual demand in period i
Time Series Method - Weighted Moving Average EXAMPLE: • Suppose we use the 3-period weighted moving average method to make demand forecasts. An importance weight of 0.6 is assigned to the most recent actual demand, 0.3 to the second most recent actual demand, and 0.1 to the oldest observation. Then what are the 3-period forecasts for demands in April and May? Month Actual Sales Forecasts Jan Feb March April Mav 10 12 13 16 19
Time Series Method - Weighted Moving Average • We first calculate the forecast for April. Let t = 4. Then, period 1 is Jan. Period 2 is Feb. And period 3 is Mar in this case. W3A3 1 = 12.4 0.6+0.3+0.1 • We now calculate the forecast for May. Let t = 5. W4A4 + W2A2 = 14.7 0.6+0.3+0.1 93
Limitations Of Moving Averages Increasing the size of n makes the method less sensitive to real changes in the data. Moving averages cannot predict changes to either higher or lower levels. They lag the actual values. Moving averages require extensive records of past data.
Time Series Method - Exponential smoothing • Exponential smoothing is a sophisticated weighted- moving- average method. Call a a smoothing constant, valued between 0 an 1. Usually from 0.05 to 0.50 for business application. That is, the forecast for period t is the sum of the forecast for period t -1 and the amount of adjustment determined by the forecast error in period t-1 and the smoothing constant. 95
Time Series Method - Exponential smoothing EXAMPLE: Let a=0.2. Then what are the forecasts for the demands in Feb, Mar, and April using Exponential Smoothing? Month Actual Sales Jan Feb March April May 10 12 13 16 19 Forecast 8 96
Time Series Method - Exponential smoothing Let Jan be period I , Feb be period 2, Mar be period 3, and so on. Then the forecast for Feb can be computed as follows: + a. (Al — Fl) = 8+02 The forecast for March: . •(12-84) = 9.12 +02 The forecast for April: • -h) = 9.12+0.2 • (13 - 9.12) = 9.896 97
Trend Projection Method — Freehand Method This is the simplest method of drawing a trend curve. Plot the values of the variable against time on a graph paper and join these points. Then the trend line will be drawn in such a way that the numbers of fluctuations on either side are approximately the same. The trend line will be a smooth curve. Disadvantages of Free Hand Method: Depends on individual judgment 05 06 07 og 09 10 11 12 Cannot be used for predicting the trend as the drawing is arbitrary. 98
Econometric Model It is assumed that demand is determined by one or more variables e.g. income, population, exports, etc. Demand is forecast on the basis of systematic analysis of economic relations by combining economic theory with mathematical and statistical tools. Advantages: Produces reliable and accurate results Forecasts not only the direction but also the magnitude of the change Disadvantages: Uses complex calculations Costly and time consuming 99
Process of selecting Forecasting Model Determining objectives of Forecasting • Identifying variables to be forecast Deciding Time Horizon • Data Gathering • Selection and Evaluation of Forecasting Model 5 • Feedback 100
Process of selecting Forecasting Model 1. Determining Objectives of Forecasting: Who needs the forecast? All organizations operate in the atmosphere of uncertainty. Decisions to be made affects future of the organization. 2. Identifying Variables to be Forecast: The item to be forecasted. Dependent variable to be studied. 3. Deciding Time Horizon: Short-range forecast - Up to I year (Purchasing, job scheduling, job assignments) Medium-range forecast - 1 year to 3 years (Sales and production planning) • Long-range forecast - 3+ years (New product planning, research and development) 101
Process of selecting Forecasting Model 4. Data Gathering: • One of the most difficult and time consuming part of forecasting is the collection of valid and reliable data. Data can be collected from- primary source and secondary source 5. Selection and Evaluation of Forecasting Model: • • • 1. 2. 3. 4. Qualitative Methods - Used when situation is vague and little data exist (New products, New technology) Quantitative Methods - Used when situation is 'stable' and historical data exist (Existing products, Current technology) Four criteria can be applied to the determination of whether the data will be useful: Data should be reliable and accurate Data should be relevant Data should be consistent Data should be timely 102
Process of selecting Forecasting Model 6. Data Analysis and Interpretation: Since available data can be either too much or too less, data reduction is necessary. Decide which data is most complete, valid and reliable to increase data accuracy. • Some times accurate data may be available but only in certain historic periods. 7. Forecast Presentation: It can be written report, oral presentation or both. 8. Feedback: • Feedback is needed because forecasting is a continuous activity of organization. 103
Supply planning/Supply Chain Analytics • Supply Chain Analytics aims to improve operational efficiency and effectiveness by enabling data-driven decisions at strategic, operational and tactical levels. It encompasses virtually the complete value chain: sourcing, manufacturing, distribution and logistics. 104
1. 2. 3. 4. Types Of Supply Chain Analytics Descriptive analytics. Provides visibility and a single source of truth across the supply chain, for both internal and external systems and data. Predictive analytics. Helps an organization understand the most likely outcome or future scenario and its business implications. Prescriptive analytics. Helps organizations solve problems and collaborate for maximum business value. Cognitive analytics. Helps an organization answer complex questions in natural language — in the way a person or team of people might respond to a question. 105
1. 2. 3. 4. 5. Five "Cs" Of Supply Chain Analytics Connected. Being able to access unstructured data from social media, structured data from the Internet of Things (IOT) and more traditional data sets available through traditional ERP and B2B integration tools. Collaborative. Improving collaboration with suppliers increasingly means the use of cloud-based commerce networks to enable multi-enterprise collaboration and engagement. Cyber-aware. The supply chain must harden its systems from cyber-intrusions and hacks, which should be an enterprise-wide concern. Cognitively enabled. The Al platform becomes the modern supply chain's control tower by collating, coordinating and conducting decisions and actions across the chain. Comprehensive. Analytics capabilities must be scaled with data in real time. Latency is unacceptable in the supply chain of the future. 106
Benefits of Supply Chain Analytics • Gain a significant return on investment Better understand risks Increase accuracy in planning Achieve the lean supply chain. Prepare for the future. 107
Challenges of Supply Chain Analytics Lack of synchronization between planning and execution. Lack of real-time data visibility, with no common view across all businesses and channels. Irregular reviews of safety stock levels, causing frequent stock-outs or excess inventory. Lack of flexibility in the network and distribution footprint, so that decision-makers find it difficult to prioritize between cost to serve and customer service levels, resulting in less profitability. Price volatility and difficulty in de-risking. Production line imbalance and suboptimal batch sizes, creating asset underutilization. 108
Procurement Analytics Procurement involves the process of selecting vendors, establishing payment terms, strategic vetting, selection, the negotiation of contracts and actual purchasing of goods. Procurement is concerned with acquiring (procuring) all of the goods, services, and work that is vital to an organization. Procurement analytics is the process of using quantitative methods to derive actionable insights and outcomes from data. The majority of the key decisions of procurement struggle due to problems in managing the data. The future of the procurement is really big. It is becoming more important. 109
Applications of Procurement Analytics • Contract management: This optimizes the discount levels and forecast the liabilities in terms of finance. Vendor evaluation: it factors the deliveries that have been done timely, quality of materials and time and effort to bring resolution to the problematic order including lowest cost. • Supplier Relationship management: This counts the vendor score , purchase order value and the PO volume. Spend analytics: the procurement analytics examine multiple types of data sets like tax data, invoice data and creates a different look at the elements in the data form. This decreases the maverick spend . Demand forecasting: Average of the whole cycle volume is generated using the data . 110
Process of Procurement Need Recognition • • Specific Need Source Options • • Price and Terms Purchase Order • Delivery Expediting • Receipt and Inspection of Purchases • Invoice Approval and Payment • Record Maintenance • 10 111
Process of Procurement Step 1: Need Recognition The business must know it needs a new product, whether from internal or external sources. The product may be one that needs to be reordered, or it may be a new item for the company. Step 2: Specific Need The right product is critical for the company. • Some industries have standards to help determine specifications. Other industries have no point of reference. The company may have ordered the product in the past. If not, then the business must specify the necessary product by using identifiers such as color or weight. 112
Process of Procurement Step 3: Source Options The business needs to determine where to obtain the product. The company might have an approved vendor list. If not, the business will need to search for a supplier using purchase orders or research a variety of other sources such as magazines, the Internet or sales representatives. The company will qualify the suppliers to determine the best product for the business. Step 4: Price and Terms The business will investigate all relevant information to determine the best price and terms for the product. This will depend on if the company needs commodities (readily available products) or specialized materials. Usually the business will look into three suppliers before it makes a final decision. 113
Process of Procurement Step 5: Purchase Order The purchase order is used to buy materials between a buyer and seller. It specifically defines the price, specifications and terms and conditions of the product or service and any additional obligations. Step 6: Delivery The purchase order must be delivered, usually by fax, mail, personally, email or other electronic means. • Sometimes the specific delivery method is specified in the purchasing documents. The recipient then acknowledges receipt of the purchase order. Both parties keep a copy on file. 114
Process of Procurement Step 7: Expediting • Expedition of the purchase order addresses the timeliness of the service or materials delivered. It becomes especially important if there are any delays. The issues most often noted include payment dates, delivery times and work completion. Step 8: Receipt and Inspection of Purchases • Once the sending company delivers the product, the recipient accepts or rejects the items. Acceptance of the items obligates the company to pay for them. 115
Process of Procurement Step 9: Invoice Approval and Payment Three documents must match when an invoice requests payment - the invoice itself, the receiving document and the original purchase order. The agreement of these documents provides confirmation from both the receiver and supplier. Any discrepancies must be resolved before the recipient pays the bill. Usually, payment is made in the form of cash, check, bank transfers, credit letters or other types of electronic transfers. Step 10: Record Maintenance • In the case of audits, the company must maintain proper records. These include purchase records to verify any tax information and purchase orders to confirm warranty information. Purchase records reference future purchases as well. 116
Strategic Sourcing Strategic sourcing is an approach to supply chain management that formalizes the way information is gathered and used so that an organization can leverage its consolidated purchasing power to find the best possible values in the marketplace. • Strategic sourcing requires analysis of what an organization buys, from whom, at what price and at what volume. • Strategic sourcing differs from conventional purchasing because it places emphasis on the entire life-cycle of a product, not just its initial purchase price. 117
Advantages of Strategic Sourcing Establish Partnerships with Suppliers Contact with New Suppliers Reduce Costs Share Best Practices Specifications • Improve Operating Efficiency • Increase Quality • Standardize Prices 118
7 Step Strategic Sourcing Process 4 5 6 • • • • • Profile the category Supply market analysis Develop the strategy Select the sourcing process Negotiate and select suppliers Implement and integrate Benchrnarking and tracking results 119
7 Step Strategic Sourcing Process Step 1: Profile the category • Understand everything about the spend category as the first step in the strategic sourcing process. This means defining the category and commodities in it. What is the current quantity used, types and sizes. Who are the users, where are they located, what are the processes used and who else is involved in the supply chain. Data must be documented in as much detail as possible as changes may be needed. Step 2: Supply market analysis Identify potential new global and local suppliers. Study the cost components of the product or service, and analyze the suppliers' marketplace for risks and opportunities. Key raw material prices and other variables such as labor and transportation must be priced and calculations done of the suppliers' 120 cost elements.
7 Step Strategic Sourcing Process Step 3: Develop the strategy. Deciding where to buy while minimizing risk and costs is how you develop the strategic sourcing strategy. Using a cross functional project team is a must. The strategy will depend on what real alternatives there are to the current suppliers, how competitive the supplier marketplace is and importantly, how open the users are to new suppliers. Step 4: Select the sourcing process The most common method of sourcing is to use a Request for Proposal process for soliciting bids. It includes product or service specifications, delivery and service requirements, pricing breakdown and legal and financial terms and conditions. Often the evaluation criteria are also stated. 121
7 Step Strategic Sourcing Process Step 5: Negotiate and select suppliers The first round of the negotiation process, after reducing the bids to the valid ones, is conducted with many suppliers asking for clarifications and more detail where needed. A good strategic sourcing strategy is to conduct multiple rounds of negotiations to get to a short list. The final selection is usually done by the team and signed off as per the approval process. Step 6: Implement and integrate • Notify the successful suppliers and ensure that they are involved in the implementation process. Implementation plans vary depending on the degree of changes. The communication plan in the strategic sourcing strategy will include any improvement to specifications or process, changes in delivery or service requirements or pricing. 122
7 Step Strategic Sourcing Process Step 7: Benchmarking and tracking results This is a key element of the sourcing management process. It is the start of a continuous cycle, starting with benchmarking the current status of the commodity or category, monitoring the results and ensuring that full value is being achieved. Back to Step 1 to review the supply market again and restart the process in a constantly evolving marketplace. 123
Inventory Modeling The purpose of all inventory models is to determine how much to order and when to order. As we know, inventory fulfills many important functions in an organization. • But as the inventory levels go up to provide these functions, the cost of storing and holding inventory also increases. Thus, we must reach a fine balance in establishing inventory levels. A major objective in controlling inventory is to minimize total inventory costs. 124
1. 2. 3. Inventory Related Costs Set-up cost: This is the cost associated with the setting up of machinery before starting production. The set-up cost is generally assumed to be independent of the quantity ordered for. Ordering cost: This is the cost incurred each time an order is placed. This cost includes the administrative costs (paper work, telephone calls, postage), transportation, receiving and inspection of goods, etc. Purchase (or production) cost: It is the actual price at which an item is purchased (or produced). It may be constant or variable. It becomes variable when quantity discounts are allowed for purchases above a certain quantity. 125
4. 5. Inventory Related Costs Carrying (or holding) cost: The cost includes the following costs for maintaining the inventory: i) Rent for the space; ii) cost of equipment or any other special arrangement for storage; iii) interest of the money blocked; iv) the expenses on stationery; v) wages of the staff required for the purpose; vi) insurance and depreciation; and vii) deterioration and obsolescence, etc. Shortage (or Stock-out) cost: This is the penalty cost for running out of stock, i.e., when an item cannot be supplied on the customer's demand. These costs include the loss of potential profit through sales of items demanded and loss of goodwill in terms of permanent loss of the customer 126
Inventory Modeling Terminology Demand - number of units required per period and may either be known exactly or known in terms of probabilities. • Selling price - The amount which one gets on selling an item. Order cycle - The period between placement of two successive orders. Time horizon - The period over which the time cost will be minimized and inventory level will be controlled. Rate of replenishment - The rate at which items are added to the inventory. Lead time - The time gap between placing an order for an item and actually receiving the item into the inventory. Reorder level - The level between maximum and minimum stock. 127
Types of Inventory Models vento • • o s Deterministic Demand Models 1. EOQ Models 2. EOQ Models without Shortages 3. EOQ Models Shortages Models without Quantity Discount Dynamic Demand Models Safety Buffer Sock 2. Reorder Level Point Models with Quantity Discount price breaks Probabilistic Demand Models I. Single Period Probabilistic Model 2. Multi-Period Probabilistic Model 128
Types of Inventory Models Models without Quantity Discount: 1. 2. 3. Also called independent demand model It considers that the demand of one product is different from that of others Classified into 3: Deterministic Demand Models: it is based on the assumption that all parameters and variables associated with an inventory stock are known and that there is no uncertainty associated with demand and replenishment of inventory stock. Dynamic Demand Models: It covers the concept of safety stock and reorder level Probabilistic Demand Models: It recognize the fact that there is always some degree of uncertainty associated with the demand pattern and lead times for inventory stock. Models with Quantity Discount: Also called dependent demand model Works on the principle that the demand of the product is based on the existing production plans or operating schedule. 129
Deterministic Demand Models: EOQ Models Economic Order Quantity (EOQ) is the order quantity that minimizes total inventory costs. Order Quantity is the number of units added to inventory each time an order is placed. • Total Inventory Costs is the sum of inventory acquisition cost, ordering cost, and holding cost. Ordering Cost is the cost incurred in ordering inventory from suppliers excluding the cost of purchase such as delivery costs and order processing costs. • Holding Cost, also known as carrying cost, is the total cost of holding inventory such as warehousing cost and obsolescence cost. 130
Deterministic Demand Models: EOQ Models The lowest total cost occurs where order costs and holding costs are the same. This is illustrated in the following graph where the lowest point of the total cost curve matches the point where order cost and holding costs connect. Total cost 8 131
Deterministic Demand Models: EOQ Models calculated the •nay also followeing forrnula: 200 EOQ (Q) = where. D Dernanded Annual quantity (in units) O = Cost Of order-ing/placing (fixed cost) Wic = Cost Of holding one unit/ Annual carrying Cost per unit- Calculation or Nun.ber Of Orders Annual Nurnber Of Order per Year EOQ or Q Calculation Of Total Inventory Cost 2) 3) Particulars Cost Of Material Ordering Cost per Carrying Cost per Annurn Total Inventory Cost EOQ EOQ 2 xxx 132
Deterministic Demand Models: EOQ Models Assumptions EOQ model assumes a constant demand. EOQ calculation assumes that ordering costs and holding costs will remain constant. Limitations Since no fluctuation in demand is considered in the EOQ calculation, business losses due to potential shortage of inventory are ignored. EOQ model does not take into account the seasonal fluctuations in the cost of inventory. EOQ model does not take into account purchase discounts that could be obtained by buying inventory in bulk. 133
Deterministic Demand Models: EOQ Models Example 1: A company annually uses 24,000 units o raw material costing Z2.5per unit. Considering each order costs e 30 and the carrying costs are 15% per year per unit of the average inventory. Find the EOQ. Solution: Here, Annual Consumption (D) = 24,000 units, Ordering Costs (O) = e 30 per unit Inventory Carrying Costs (ic) = e 2.5 per unit Now, = ic = 15% per year per unit of average inventory = 0.15 x 2.5 = 0.375 h = 1960 Units 0.375 134
Deterministic Demand Models: EOQ Models Example 2: From the following information, find out EOQ and total variable cost associated with the the ordering policy: ual Demand (D) = esoooo ordering Cost (O) = e 200 per order Inventory carrying cost 40% of average inventory (ic) solution: EOQ (in rupees) Total cost T(Q) = value 2 DO ic 2 x 50000 x 200 0.40 135
Deterministic Demand Models: EOQ Models Example 3: Calculate EOQ from the follovzing information : Annual Usage, 20,000 units Cost of placing and receiving one order 100 Cost of materials per unit Z50 Annual carrying cost Of one unit: 10% Of inventory value. 2DO EOQ — Annual consumption in units (D) = 20,000 units Cost of placing an order (O) 100 per order Inventory carrying cost of one unit (h) = e 50 x 10% unit 20,000 x 100 894 units 5 136
Deterministic Demand Models: EOQ Models Example 4: Follovving infor-rnation relating to a type of material is available- 1 ) Annual 2) Unit price 4800 units e2.40 3) Ordering cost per order e 8-00 4) Storage cost 5) Interest rate 6) Lead tirne 10% per annun• Half rnonth EOQ and total annual inventory cost frorn the ve inforrnation 200 h Demand (D) = 4800 units g Cost (O) z: 8 per order 10 2 Cost (h) 1 oo 100 0-048 + 0.24 = 0.288 137
Deterministic Demand Models: EOQ Models = 516 units EOQ= 0.288 Annual Demand 4800 Number Of Orders 516 EOQ = 9.30 Total Inventory Cost = Cost of Material + Ordering Cost + Carrying Cost é!fxo.288 2 516 = 11,520 + 74 + 74 = 1,668 138
Deterministic Demand Models: EOQ Models Without Shortages Assumptions of this model: l. 2. 3. 4. 5. 6. Demand is constant and known Replenishment is instantaneous Lead time is zero Purchasing cost is constant and known Carrying cost and ordering cost are constant and known; and The supply is expeditious, whenever the level of stock holding reaches at zero. As a result, there are no instances of surplus or shortages of stock. 139
Deterministic Demand Models: EOQ Models With Shortages Assumptions of this model: l. 2. 3. 4. 5. 6. Demand is constant and known Replenishment is instantaneous Lead time is zero Purchasing cost is constant and known Carrying cost and ordering cost are constant and known; and The striking feature of this model is the condition that if there is no stock of the items at the time of ordering, the supply would be made at a later date with a penalty. This feature is known as 'Backordering' 140
Dynamic Demand Model — Safety/Buffer Stock • Safety stock (also called buffer stock) is the level of extra stock that is maintained to mitigate risk of stock outs due to uncertainties in supply and demand • Safety stock act as a buffer stock in case the sales are greater than planned and or the supplier is unable to deliver the additional units at the expected time. The less accurate the forecast, the more safety stock is required to ensure a given level of service. A common strategy is to try and reduce the level of safety stock to help keep inventory costs low once the product demand becomes more predictable. This can be extremely important for companies with a smaller financial cushion or those trying to run on lean manufacturing, which is aimed towards eliminating waste throughout the production process. 141
Dynamic Demand Model — Safety/Buffer Stock Reasons for keeping safety stock: 1. 2. 3. 4. Safety stocks are mainly used in a "Make To Stock" manufacturing strategy. This strategy is employed when the lead time of manufacturing is too long to satisfy the customer demand at the right cost/quality/waiting time. The main goal of safety stocks is to absorb the variability of the customer demand. Creating a safety stock will also prevent stock-outs from other variations, like an upward trend in customer demand. 142
Dynamic Demand Model — Re-order Level or Ordering Point or Ordering Level This is that level of materials at which a new order for supply of materials is to be placed. • In other words, at this level a purchase requisition is made out. This level is fixed somewhere between maximum and minimum levels. Order points are based on usage during time necessary to requisition order, and receive materials, plus an allowance for protection against stock out. The order point is reached when inventory on hand and quantities due in are equal to the lead time usage quantity plus the safety stock quantity. 143
Dynamic Demand Model — Re-order Level or Ordering Point or Ordering Level The following two formulas are used for the calculation of reorder level or point. Ordering point or re-order level = Maximum daily or weekly or monthly usage x Lead time The above formula is used when usage and lead time are known with certainty; therefore, no safety stock is provided. When safety stock is provided then the following formula will be applicable: Ordering point or re-order level = Maximum daily or weekly or monthly usage x Lead time + Safety stock 144
Dynamic Demand Model — Re-order Level or Ordering Point or Ordering Level Example 1: Minimum daily requirement Time required to receive emergency supplies Average daily requirement Minimum daily requirement Time required for refresh supplies Calculate ordering point or re-order level Calculation: 800 units 4 days 700 units 600 units One month (30 days) Ordering point = Ordering point or re-order level = Maximum daily or weekly or monthly usage x Lead time = 800 x 30 = 24,000 units 145
Dynamic Demand Model — Re-order Level or Ordering Point or Ordering Level Example 2: Tow types of materials are used as follows: Minimum usage Maximum usage Normal usage Re-order period or Lead time Material A: Material B Calculate reorder point for two types of materials. Calculation: 20 units per week each 40 units per week each 60 units per week each 3 to 5 weeks 2 to 4 weeks Ordering point or re-order level = Maximum daily or weekly or monthly usage x Maximum re- order period A: 60 x 5 = 300 units B: 60 x 4 = 240 units 146
Single Period Probabilistic Models These models deal with the inventory situation of the items- such as perishable goods, spare parts and seasonal goods requiring one time purchase only. The demand for such items may be discrete or continuous. Since purchases are made only once, the lead time factor is least important in these models. • In single period models, the problem is studied using marginal (or incremental) analysis and the decision procedure consists of a sequence of steps. • In such cases, there are two types of costs involved, namely (a) Over-stocking cost, and (b) Under-stocking cost. These two costs represent opportunity losses incurred when the number of units stocked is not exactly equal to the number of units actually demanded. 147
Single Period Probabilistic Models Cl = Over-stocking cost (also known as over-ordering cost). This is an opportunity loss associated with each unit left unsold. C2 = Under-stocking cost (also known as under-ordering cost). This is an opportunity loss due to not meeting the demand. = S-C-Ch/2+Cs • where C is the unit cost price; Ch, the unit carrying cost for the entire period; Cs, the shortage cost; S, the unit selling price and, V, the salvage value. 148
Multi-period Probabilistic Models • In single period models, only demand is the major variable factor and lead time does not play any role in the decision process. • But, in multi-period models, both demand and lead time play major role in the decision process. These factors may be changing according to certain laws of probability. The variation in demand and/or in lead time imposes risks. We cushion the effects of demand and lead time variation by absorbing risks in carrying larger inventories, called buffer stocks or safety stocks. The larger we make these safety stocks, the greater our risk, in terms of the funds tied up in inventories, the possibility of obsolescence and so on. However, we minimize the risk of running out of stock. While minimizing the risk of out of stock we can minimize the risk of inventories by reducing the buffer inventories which in turn lead to increase in the risk of poor inventory service. 149
Aggregate Planning Aggregate planning is the process of developing, analyzing, and maintaining a preliminary, approximate schedule of the overall operations of an organization. The aggregate plan generally contains targeted sales forecasts, production levels, inventory levels, and customer backlogs. This schedule is intended to satisfy the demand forecast at a minimum cost. The process of determining output levels of product groups over the coming 6 to 18 months on a weekly or monthly basis ; the plan identifies the overall level of outputs in support of the business plan. Aggregate planning involves translating long-term forecasted demand into specific production rates and the corresponding labor requirements for the intermediate term. 150
Objectives of Aggregate Planning Minimize cost / maximize profits • Maximize customer service Minimize inventory investment Minimize changes in production rates Minimize changes in workforce levels • Maximize utilization of plant and equipment 151
Aggregate Planning Strategies 1. Level Aggregate Strategy: Maintains a constant workforce • Sets capacity to accommodate average demand Often used for make-to-stock products like appliances Disadvantage- builds inventory and/or uses back orders 2. Chase Aggregate Strategy: Produces exactly what is needed each period • Sets labor/equipment capacity to satisfy period demands Disadvantage- constantly changing short term capacity 152
Developing the Aggregate Plan Step l- Choose strategy: level, chase, or Hybrid Step 2- Determine the aggregate production rate Step 3- Calculate the size of the workforce Step 4- Test the plan as follows: Calculate Inventory, expected hiring/firing, overtime needs Calculate total cost of plan Step 5- Evaluate performance: cost, service, human resources, and operations 153
Techniques for Aggregate Planning 1. Graphical Method/ Trial and Error Approach to Aggregate Planning 2. Mathematical Approach to Aggregate Planning
1. 2. 3. 4. 5. Graphical Method/ Trial and Error Approach Graphical methods in aggregate planning are techniques that work with a few variables at a time to allow planners to compare projected demand with existing capacity. It is a trial and error approach which does not guarantee optimization, but requires computations. In this approach even the clerical staff can follow the steps: Determine the demand in each period Determine capacity for regular time, overtime and subcontracting Find labor costs, hiring and layoff costs, and inventory holding costs Consider company policies that may apply to workers, and Develop alternative plan and examine their total costs. 155
Mathematical Approach to Aggregate Planning l. 2. 3. 4. 5. 6. 7. Linear Programming. Mixed-integer Programming. Linear Decision Rule. Management Coefficients Model. Search Decision Rule. Simulation. Functional Objective Search Approach. 156
Linear Programming Linear programming is an optimization technique that allows the user to find a maximum profit or revenue or a minimum cost based on the availability of limited resources and certain limitations known as constraints. A special type of linear programming known as the Transportation Model can be used to obtain aggregate plans that would allow balanced capacity and demand and the minimization of costs. 157
Mixed-integer Programming • For aggregate plans that are prepared on a product family basis, where the plan is essentially the summation of the plans for individual product lines, mixed-integer programming may prove to be useful. Mixed-integer programming can provide a method for determining the number of units to be produced in each product family. 158
Linear Decision Rule Linear decision rule is another optimizing technique. It seeks to minimize total production costs (labor, overtime, hiring/lay off, inventory carrying cost) using a set of cost-approximating functions (three of which are quadratic) to obtain a single quadratic equation. Then, by using calculus, two linear equations can be derived from the quadratic equation, one to be used to plan the output for each period and the other for planning the workforce for each period. 159
Management Coefficients Model The management coefficients model, formulated by E.H. Bowman, is based on the suggestion that the production rate for any period would be set by this general decision rule: P F aWt_,- + cFt+1+ K, where P the production rate set for period t w t _ 1 = the workforce in the previous period I the ending inventory for the previous period F 1 = the forecast of demand for the next period a, b, c, and Kare constants It then uses regression analysis to estimate the values of a, b, c, and K. The end result is a decision rule based on past managerial behavior without any explicit cost functions, the assumption being that managers know what is important, even if they cannot readily state explicit costs. Essentially, this method supplements the application of experienced 160 judgment.
Search Decision Rule The search decision rule methodology overcomes some of the limitations of the linear cost assumptions of linear programming. The search decision rule allows the user to state cost data inputs in very general terms. It requires that a computer program be constructed that will unambiguously evaluate any production plan's cost. It then searches among alternative plans for the one with the minimum cost. However, unlike linear programming, there is no assurance of optimality. 161
Simulation A number of simulation models can be used for aggregate planning. By developing an aggregate plan within the environment of a simulation model, it can be tested under a variety of conditions to find acceptable plans for consideration. These models can also be incorporated into a decision support system, which can aid in planning and evaluating alternative control policies. These models can integrate the multiple conflicting objectives inherent in manufacturing strategy by using different quantitative measures of productivity, customer service, and flexibility. 162
Functional Objective Search Approach The functional objective search (FOS) system is a computerized aggregate planning system that incorporates a broad range of actual planning conditions. It is capable of realistic, low-cost operating schedules that provide options for attaining different planning goals. The system works by comparing the planning load with available capacity. After management has chosen its desired actions and associated planning objectives for specific load conditions, the system weights each planning goal to reflect the functional emphasis behind its achievement at a certain load condition. The computer then uses a computer search to output a plan that minimizes costs and meets delivery deadlines. 163
Resource Allocation Decisions Means to complete project activities are called RESOURCES. Examples are People, Machinery, Material, Capital, Time, etc. Peak demands of resources over short periods is undesirable. Resources may be limited or unlimited in nature from project to project The degree to which a resource may be used is measured in terms of a Resource Utilization Factor. Mathematically Usable Resources x Days Used x 100 R.U.F. Usable Resources x Days available 164
Resource Allocation Decisions Resource allocation is used to assign the available resources in an economic way. • In project management, resource allocation is the scheduling of activities and the resources required by those activities while taking into consideration both the resource availability and the project time. 165
Tools used in Resource Allocation Decisions • Multi attribute Utility Theory Decision Tree Analysis Monte Carlo Simulation Method Influence Diagram 166
Multi attribute Utility Theory Multi-attribute utility theory (MAUT) combines a class of psychological measurement models and scaling procedures which can be applied to the evaluation of alternatives which have multiple value relevant attributes. • For example, MAU T can be used to analyze preferences between cars described by the attributes cost, comfort, prestige, and performance. MAU T may also be applied as a decision aiding technology for decomposing a complex evaluation task into a set of simpler subtasks. • For example, the decision maker might be asked to assess the utility of each alternative with respect. to each attribute and to assign importance weights to each attribute. Then an appropriate combination rule is used to aggregate utility across attributes. 167
Multi attribute Utility Theory Advantages: Takes uncertainty into account can incorporate preferences Disadvantages: Needs a lot of input preferences need to be precise Areas of Application: Economics finance actuarial water management energy management agriculture 168
• l. 2. 3. 4. Decision Tree Analysis A decision tree is a chronological representation of the decision process. It utilizes a network of two types of nodes: decision (choice) nodes (represented by square shapes), and states of nature (chance) nodes (represented by circles). a step-by-step description of how to build a decision tree: Draw the decision tree using squares to represent decisions and circles to represent uncertainty, Evaluate the decision tree to make sure all possible outcomes are included, Calculate the tree values working from the right side back to the left, Calculate the values of uncertain outcome nodes by multiplying the value of the outcomes by their probability (i.e., expected values). 169
Decision Tree Analysis - 3000 2000 667 S 00 1000 — 6000 3 0 00 B -208 m 66 2000 -12S 6000 3000 _ 849 1 134 2000 — 6 000 3000 B 0-0 S21-7 000 6000 170
Monte Carlo Simulation Method Monte Carlo Simulation is a computerized mathematical technique that allows people to account for risk in quantitative analysis and decision making. It furnishes the decision-maker with a range of possible outcomes and the probabilities with which they will occur for any choice of action. Monte Carlo simulation performs risk analysis by building models of possible results by substituting a range of values—a probability distribution for any factor that has inherent uncertainty. It then calculates results over and over, each time using a different set of random values from the probability functions. Depending upon the number of uncertainties and the ranges specified for them, a Monte Carlo simulation could involve thousands or tens of thousands of recalculations before it is complete. Monte Carlo simulation produces distributions of possible outcome values. 171
• • Monte Carlo Simulation Method During a Monte Carlo simulation, values are sampled at random from the input probability distributions. Each set of samples is called iteration, and the resulting outcome from that sample is recorded. Monte Carlo simulation does this hundreds or thousands of times, and the result is a probability distribution of possible outcomes. In this way, Monte Carlo simulation provides a much more comprehensive view of what may happen. Ardyze data Cmvare mot* d&.a and r&te 172
Influence Diagrams Influence diagrams are also used for the development of decision models and as an alternate graphical representations of decision trees. The following figure depicts an influence diagram: Consult ant Report Start Hire f Don't Rates Of Retums Probability Amessrnent Outcomes In the influence diagram above, the decision nodes and chance nodes are similarly illustrated with squares and circles. Arcs (arrows) imply relationships, including probabilistic ones. 173
l. 2. 3. 4. Influence Diagrams Influence diagram provide effective method of decision- making because it: Clearly lay out the problem so that all options can be challenged Allow us to analyze fully the possible consequences of a decision Provide a framework to quantify the values of outcomes and the probabilities of achieving them Help us to make the best decisions on the basis of existing information and best guesses 174
Make Or Buy Decision It is the determination whether to produce a component internally or to buy it from the outside supplier The decision is based on the cost. The cost for both the alternatives should be calculated and the alternative with less cost is to be chosen OUTSOURCING IN-HOUSE SOLUTION 175
Make Or Buy Decision Criteria For Make: The product can be made cheaper by the firm. The finished product is being manufactured only by limited firms The part needs extremely close quality control The part can be manufactured from the existing facilities with experienced operators Criteria For Buy: High investments required for making • Does not have facilities for making. Skilled workers not available Demand is either temporary or seasonal • Patents or legal formalities prevent from making the product. 176
Stages of Make/Buy Decisions Preparation Data Collection Data Analysis Feedback Team creation and appointment of the team leader the requirements and analysis Team briefing and asßct/area destitution Collecting Information on vanous aspects of make- or.buv wukshops on wetghtitv. ratings, and cost for both make-or-buy Analysis Of data gathered Feedback on the decision made 177
Stages of Make/Buy Decisions 1. Preparation Team creation and appointment of the team leader Identifying the product requirements and analysis Team briefing and aspect/area destitution 2. Data Collection Collecting information on various aspects of make-or-buy decision Workshops on weightings, ratings, and cost for both make-or- buy 3. Data Analysis Analysis of data gathered 4. Feedback Feedback on the decision made • 178
Approaches For Make Or Buy Decision The following are the approaches (l) Simple cost analysis (2) Economic analysis (3) Break Even Analysis 179
Simple Cost Analysis It is concerned with finding the actual expenditure incurred on a given product. Finding the total value of economic resources used to produce a product Example: A company has been buying a part of machinery for Rs.lOOO/- each. It has an extra capacity that can be used to produce the same. The annual fixed cost of the unused capacity is Rs. 10, 00, 000/-. If the company decided to make the product it will incur material cost ofRs.350/- per unit, labour cost of Rs 300 per unit and variable overhead cost of Rs 100/- per unit. The future demand is estimated as 5000 units. Which decision is profitable for the company. 180
Simple Cost Analysis Solution: Given data : 1. Fixed cost : Rs. 2. Labour cost : Rs. 300/unit 3. Material cost : Rs. 350/unit 4. Overhead cost : Rs 100/unit 5. Demand : 5000 units 6. Buying price : Rs. 1000 each 181
Simple Cost Analysis SOLUTION Cost of making • Total Cost = FC + VC • VC/unit = material cost + Labour cost + Overhead cost + 350 + 100 = Rs. 750/unit • Demand = 5000 units • Total Variable cost = 5000 x 750 = Rs. • Total cost FC + VC = + = Cost of buying : • FC+ Buying cost = + (5000 x 1000) = Rs. Decision : Since the cost of making is < cost of buying it is decided to make the product 182
Economic Analysis The following models are used (a) Purchase model (b) Manufacturing model Purchase Model 2CoD DC QIXCc TC = DXP + 2 Manufacturing Model cc DC TC = DXP + + Cc (k-r) —2 D — Danand / year, P-Purchase price / year, Cc — Carrying cost / unit /year, Co- Ordering cost / order or Setup cost / setup, k-production rate( No. of units "year), r-demand/year, Q,-Economic order size, Qz- production size, TC- Total Cost per year 183
Economic Analysis PROBLEM: A part of the machine has a yearly demand of 3000 units. The different costs in respect of make or buy are as given below Details Buy Make Item cost/unit Rs.10 Rs.8.O Procurement cost/order Rs.150 - Setup cost/setup Rs.80/- Annual carrying cost/year Rs.2.O Rs.1.50/- Production rate/year - 10,000 units Details Item cost/u nit Procurement cost/order Setup cost/setup Annual carrying cost/year Production rate/year Buy Rs.10 Rs.150 Rs.2.O Make Rs.8.O Rs.80/- Rs.1.50/. 10,000 units 184
Economic Analysis SOLUTION Purchase Model D = 3000 Units/Year Co = Rs.150/order Cc = Rs.2.0/item/year P = Rs. 10/unit 1 670.82 units TC 3000 x 10 + 3000 x 150/670.82 + 670.82 2/2 Rs. 31,341.64 185
Economic Analysis SOLUTION • Manufacturinq Model 2Cor - 676.12 TC = 3000 x 8 + 3000 x 80/676.12 + x 10000 = Rs. 24,709.93 Decision : Since the cost of making the item is < the cost of Producing it is decided to make the product 186
Break-Even Analysis Sales t$) poir•R 3 Pfafir total Cost Cose - ----Fixed 6 Break-even point can be described as a point where there is no net profit or loss Break-even point is the number of units (N) produced which make zero profit. Revenue — Total costs = O Total costs = Variable costs * N + Fixed costs Revenue = Price per unit * N Price per unit * N — (Variable costs * N + Fixed costs) So, break-even point (N) is equal N = Fixed costs I (Price per unit - Variable costs) 187
Example: Break-Even Analysis TC(manufacturing) = $50,000 + $125*Q TC(outsourcing) = $175*Q Breakeven Point: TC(manufacturing) = TC(outsourcing) $50,000 + $125 x Q = $175 x Q $50,000 = 50 x Q Q = 1,000 General Formula Outswrciq ssaæo 188
Break-Even Analysis Problem: A manufacturer of TV buys TV cabinet at Rs 500 each. In case the company makes it within the factory the fixed and variable costs would be Rs.4,OO,OOO and Rs 300 per cabinet respectively. Should the manufacturer make or buy the cabinet if the demand is 1500 TV cabinet Solution: Selling price / unit = Rs. 500/- Variable cost / unit = Rs. 300/- Fixed cost = Rs. BEP = = 2,000 units Decision : Since the demand is < the BEP the company should buy 189

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