Chapter 1 and 2 Materials Management PDF

Unit 1. INTRODUCTION 1.1. Definition and scope of materials management The following figure shows general supply chain concept material flows: 6 Ms in operations management Material: The materials are those products which are utilized for creating the final product. Materials can be raw or even s...

Unit 1. INTRODUCTION 1.1. Definition and scope of materials management The following figure shows general supply chain concept material flows: 6 Ms in operations management Material: The materials are those products which are utilized for creating the final product. Materials can be raw or even semi-finished products that are used for making the end-product that the company sells. Example: In cotton mills, cotton is considered as raw materials. Yarn or cloth sheets derived from these factories are then sent to the clothing factories. These sheets of cotton are viewed as raw materials in the clothing factory. The cotton is sewed, tailored and altered to make a desirable outfit. A final product for one factory can be the raw material for another.. What are materials: Materials are the matter or substance that objects are made from. These might include: metal plastic wood glass ceramics synthetic fibers composites (made from two or more materials combined together) Different materials have different features, or properties, which make them suitable for different uses. Material resources are materials found in the natural world that have practical use and value for humans. Material resources include wood, glass (which comes from sand), metals, edible plants, and plastics (which are made from natural chemicals  Physical versus chemical P hysical properties refer to properties that can be observed or measured without changing the composition of the material. Examples include color, hardness and smell and freezing, melting and boiling points. Chemical properties are discovered by observing chemical reactions. They include combustion point, reactivity with acids and toxicity. Types of Materials The various types of materials to be managed are: (i) Purchased materials: They are raw materials, components, spare parts, oils, grease, cotton waste, consumables and tools. (ii) (ii) Work in process (WIP) materials: These are semi- finished and finished parts and components lying on the shop floor. (iii) (iii) Finished goods: These are the final products either waiting to be assembled in the assembly lines or in stores which are stocked for final delivery waiting to sell. The various costs involved in these materials are basic price, purchasing costs, inventory carrying cost, transportation cost, materials handling cost, office cost, packing cost, marketing cost, obsolescence and wastages.  Materials management : It is concerned with planning, organizing and controlling the flow of materials from their initial purchase through internal operations to the service point through distribution. OR Material management is a scientific technique, concerned with Planning, Organizing &Control of flow of materials, from their initial purchase to destination. ´Materials management was a consolidation of functions required to purchase, manage inbound transportation for, receive, store, inventory, and schedule material flows. Physical distribution then took the finished product to market either directly or through warehouses Physical distribution take the finished product to market either directly or through warehouses Logistics= materials management +physical distribution Other factors such as information and process flows were as important as (if not more so than) the physical flow of goods. Hence“supply chain management” to indicate this complex web of relationships, processes, and information flows from the supplier to the final customer Scope of materials management 1. Materials planning and control This involves estimating the individual requirements of parts, preparing materials budget, forecasting the levels of inventories, scheduling the orders and monitoring the performance in relation to production and sales. 2. Purchasing includes the selection of sources of supply finalization in terms of purchase, placement of purchase orders, follow-up, maintenance of smooth relations with suppliers, approval of payments to suppliers, evaluating and rating suppliers. 3. Stores management or management involves physical control of materials, preservation of stores, minimization of obsolescence and damage through timely disposal and efficient handling, maintenance of store records, proper location and stocking. A store is also responsible for the physical verification of stocks and reconciling them with book figures. A store plays a vital role in the operations of a company. 4. Inventory control or management Inventory/the materials in stock/the idle resource of an enterprise. Inventories either stocked for sale, in the process of manufacturing or they are in the form of materials, which are yet to be utilized. It is necessary to hold inventories of various kinds to act as a buffer between supply and demand for the efficient operation of the system. Thus, effective control on inventory is a must for the smooth and efficient running of the production cycle with the least interruptions. 5. Other functions of materials management a. 3S Meaning i.Standardization: Standardization means producing a maximum variety of products from the minimum variety of materials, parts, tools, and processes. It is the process of establishing standards or units of measure by which extent, quality, quantity, value, performance, etc. may be compared and measured. ii.Simplification: Simplification is the process of reducing the variety of products manufactured. Simplification is concerned with the reduction of product range, assemblies, parts, materials, and design. iii.Specifications: It refers to a precise statement that formalizes the requirements of the customer. It may relate to a product, process or service.  b. Value analysis: concerned with the costs added due to inefficient or unnecessary specifications and features. It makes its contribution to the last stage of the product cycle, namely, the maturity stage c. Ergonomics (Human Engineering): The human factors or human engineering is concerned with the man- machine system. Ergonomics is “the design of human tasks, man-machine system, and effective accomplishment of the job, including displays for presenting information to human sensors, controls for human operations and complex man-machine systems.”  Objectives of materials management The famous 5 Rs of Materials Management, are acquisition of materials and services of the right quality in the right quantity at the right time from the right source at the right price  UNIT 2 – FORECASTING Introduction Forecasting is the technique of estimating the relevant future events and problems on the basis of past and present behavior or happenings Forecasting depends upon an analysis of past events and current conditions Meaning and Definition – Meaning: Forecasting: the systematic guessing of future course of events with the help of analysis of present and past events Definition of Forecasting –According to Webster‟s new Collegiate Dictionary, „A forecast is a prediction and its purpose is to calculate some future events or conditions  2.1. Why Forecasting? Pivotal role in an Organization –Planning is the backbone of effective functioning of an organization – Planning is based on forecasting Primacy to planning – Planning can not be done without the forecasting  Cont… Development of a business – Business is established in order to achieve specified objective Implementation of project – Forecasting is an important factor which enable the entrepreneur to get success  Cont… Co-ordination – Forecasting helps management executives in effective coordination Indirectly Effective control – Forecasting can provide adequate information for exercising effective control 2.2. Features of Good Forecasting Forecasting is concerned with the future events Impact of future events has to be considered in the planning process Forecasting considers all the factors which affect the organizational functions 2.3. Steps in Forecasting Process 1. Thorough preparation of foundation –Detailed investigation and complete analysis of the company are necessary for forecasting – Forecasting is based on the foundation  Cont… 2. Estimation of future – The prosperity of the future can be estimated with the help of past experience and performance 3. Collection of results – All the information can be collected –Nothing can be omitted and irrelevant information can be avoided while collecting results  Cont… 5. Comparison of results – The actual results are compared with estimated results to know derivations 6. Refining the forecast – The forecast can be refined in the light of derivations which seem to be more realistic  2.4. Types of Forecasting 2.4.1. Qualitative Methods projections based on judgment, intuition, and informed opinions; are subjective 1.Expert opinion method: pooling of views of group of expected future sales and combining them into a sales estimate. The major advantage of this method is the pooling of expertise knowledge in the forecasting process. 2.Delphi method: involves converting the views of a group of experts, who do not interact face –to – face, into a forecast through an iterative process.  Cont…. ⚫ The processes of Delphi method may include the following steps : 1. A group of experts is sent a questionnaire by mail and asked to express their view. 2. The response received from the experts are summarized without disclosing the identity of the experts, send back to the experts, along with a questionnaire meant to probe further the reasons for extreme views expressed in the first round. 3. The process may be continued for one or more rounds till a reasonable agreement emerges in the view of the experts. 2.4.2. Quantitative Methods Time series/Intrinsic Techniques Use historical data to forecast. These data are usually recorded in the company and are readily available. are based on the assumption that what happened in the past will happen in the future  DECOMPOSITION OF A TIME SERIES Patterns that may be present in a time series 1. Trend: Data exhibit a steady growth or decline over time. 2.Seasonality: Data exhibit upward and downward swings in a short to intermediate time frame (most notably during a year). 3.Cycles: Data exhibit upward and downward swings in over a very long time frame. 4.Random variations: Erratic and unpredictable variation in the data over time with no discernable pattern. Take the following pattern of historical demand Trend component od historical demand Seasonal component of historical demand Cycle component of historical demand Random component in historical demand Time series techniques: Cont--- Cont… Assume that the monthly demand for a particular item over the past year is as shown. Forecast demand for January of coming year. 1. Naïve: a. Demand this month will be the same as last month. 84 b. Demand this month will be the same as demand the same month last year. 92 Note: Rules based on a single month or past period, are of limited use when there is much random fluctuation in demand.  2. Simple average Demand for January: Total demand number of periods 952/12 = 79.333 3. Simple moving average Moving averages are best used for forecasting products with stable demand where there is little trend or seasonality Required: Forecast demand for January using a moving average of three months. Demand for JNUARY= 63+91+84 =79.33 3  4. Weighted moving average Use weights as follows: The most recent month , 5; moth before that is 3; month before that is 2. 5*84+3*91+2*63 = 420+273+126 5+3+2 10 = 81.9  5. EXPONENTIAL SMOOTHING METHOD Exponential smoothing method: The new forecast for next period (period t) will be calculated as follows: New forecast = Last period‟s forecast + σ(Last period‟s actual demand – Last period‟s forecast) New forecast = σ (Last period‟s actual demand) + (1- σ) (Last period‟s forecast)  Example The old forecast for May was 220, and the actual demand for May was 190. If alpha (α) is 0.15, calculate the forecast for June. If June demand turns out to be 218, calculate the forecast for July.  Answer June forecast = (0.15)(190) + (1- 0.15)(220) = 215.5 July forecast = (0.15) (218) + (0.8) (215.5) = 215.9  6. Seasonal Index A useful indication of the degree of seasonal variation for a product This index is an estimate of how much the demand during the season will be above or below the average demand for the product Seasonal index =period average demand average demand for all periods The period can be daily, weekly, monthly, or quarterly depending on the basis for the seasonality of demand.  Example For example, swimsuit demand might average 100 per month, but in July the average is 175, and in September it‟s 35. Required Calculate seasonal indexes for the months of July and September. Answer: 1.75 for July; 0.35 for September  The average demand for all periods is a value that averages out seasonality. This is called the deseasonalized demand. The previous equation can be rewritten as: Season index = period average demand deseasonalized demand  Example A product that is seasonally based on quarterly demand and the demand for the past three years is shown in Figure below. There is no trend, but there is definite seasonality. Average quarterly demand is 100 units (128+102+75+95) /4=100 Figure below also shows a graph of actual seasonal demand and average quarterly demand. The average demand shown is the historical average demand for all periods. Remember we forecast average demand, not seasonal demand Sales seasonal history Seasonal index Note that the total of all seasonal indices equals the number of seasons  Seasonal forecast The company in the previous problem forecasts an annual demand next year of 420 units. Calculate the forecast for quarterly sales.  Deseasonalized Demand Forecasts do not consider random variation. Forecasts are made for average demand, and seasonal demand is calculated from the average using seasonal indices. The forecast average demand is also the deseasonalized demand Seasonal Demand Deseasonalized Demand  Example A company selling tennis rackets has a January demand of 5200 units and a July demand of 24,000 units. If the seasonal indices for January were 0.5 and for June were 2.5, calculate the deseasonalized January and July demand. How do the two months compare? Answer: Deseasonalized January demand = 5200/ 0.5 = 10,400 units Deseasonalized June demand = 24,000/ 2.5 = 9600 units June and January demand can now be compared. On a deseasonalized basis, January demand is greater than June demand. Deseasonalized data must be used for forecasting. Forecasts are made for average demand, and the forecast for seasonal demand is calculated from the average demand using the appropriate season index. The rules for forecasting with seasonality follow: Use only deseasonalized data to forecast. Forecast deseasonalized demand (base forecast), not seasonal demand. C alculate the seasonal forecast by applying the seasonal index to the base forecast. Example A company uses exponential smoothing to forecast demand for its products. For April, the deseasonalized forecast was 1000, and the actual seasonal demand was 1250 units. The seasonal index for April is 1.2 and for May is 0.7. If α is 0.1, calculate the following: a. The deseasonalized actual demand for April. b. The deseasonalized May forecast. c. The seasonal forecast for May.  A ns wer a. b. Deseasonalized May forecast = α (latest actual) + ( 1 – α ) (previous forecast) = 0.1 ( 1 0 4 2 ) + 0.9 (1 0 0 0 ) = 1004 C. Seasonalized May forecast = (seasonal index)(deseasonalized forecast) = 0.7 ( 1 0 0 4 ) = 703 ASSOCIATIVE FORECASTING METHOD Associative forecasting models (causal models) assume that the variable being forecasted (the dependent variable) is related to other variables (independent variables) in the environment. In its simplest form, linear regression is used to fit a line to the data. That line is then used to forecast the dependent variable for some selected value of the independent variable. The Regression Method. In regression models, the quantity to be forecast in the demand function is the dependent variable, and the determinants of demand are the independent or explanatory variables Suppose we want to forecast the demand for Y, for example, for particular periods on the basis of some past data, we would estimate the regression equation of the form: Y = a + bX… Where Y represents the quantity of to be demanded; and, X represents the single variable, population, and a and b are constants. The parameters a and b can be estimated, using the past data, Consider the following hypothetical past data on the demand for sugar for the years 2000 to 2006. Using the hypothetical data develop the regression demand model for sugar. Year Population (millions) Quantity of sugar demanded (000’s) 2000 10 40 2001 12 50 2002 15 60 2003 20 70 2004 25 80 2005 30 90 2006 40 100 TRACKING THE FORECAST/ MEASURING FORECAST ACCURACY Tracking the forecast is the process of comparing actual demand with the forecast. Fo r e c a s t E r r o r Forecast error is the difference between actual demand and forecast demand. Error can occur in two ways: bias and random variation.  Cont.. Bias exists when the cumulative actual demand varies from the cumulative forecast. This means the forecast average demand has been wrong. In the example in Figure below, the forecast average demand was 100, but the actual average demand was 720 † 6 = 120 units. Figure: Forecast and actual demand with bias Figure: Forecast and average demand with bias Bias is a systematic error in which the actual demand is consistently above or below the forecast demand. When bias exists, the forecast should be evaluated and possibly changed to improve its accuracy. The purpose of tracking the forecast is to be able to react to forecast error by planning around it or by reducing it. When an unacceptably large error or bias is observed, it should be investigated to determine its cause. Random variation Forecast and actual data with out bias G raphic presentation of forecast and actual data without bias  Measuring error 1. Mean Absolute Deviation (MAD) Is one method of measuring forecast error. Calculate MAD from the following table Uses of mean absolute deviations 1. Tracking signal: for tracking quality of forecast Cont…  Cont.. 2.Contingency planning: MAD can be used to cope with the possible extra demand 3.Safety stock: MAD can be used to set safety stock END