GBS 660 Production and Operations Management

Transcription

1 Master of Business Administration (MBA) GBS 660 Production and Operations Management Course Lecturer Prof Levy Siaminwe, Phd 1

2 Production and Operations Management Module Contents Unit 1: Insight into Production and Operations Management Unit 2: Demand Forecasting Unit 3: Inventory Management Unit 4: Aggregate Planning Unit 5: Master Production Schedule Unit 6: Material Requirement Planning Unit 7: Shop-Floor Planning and Control Unit 8: Total Quality Management Unit 9: Capacity Requirement Planning Learning Objectives Upon completing this module, students should be able to: understand the strategic role of operations management in organisations understand key concepts and issues of operations management in both manufacturing and service organisations understand the interdependence of the operations function with the other key functional areas of an organisation apply analytical skills and problem-solving tools to the planning and control of production operations 2

3 Recommended Textbook Norman Gaither (1992) Production and Operations Management, Sixth Edition, The Drysen Press; ISBN: or latest version R. Dan Reid and Nada R. Sanders (2010) Operations Management: An Integrated Approach, International Student Version, Wiley; ISBN: Unit 1 Insight into Production and Operations Management 1.1 What is Operations Management? 1.2 The Role of Operations Management in Business 1.3 Decisions in Operations Management 1.4 Issues in Operations Management 1.5 What is Production and Operations Management? 3

4 1.1 What is Operations Management? The business function responsible for planning, coordinating, and controlling the resources needed to produce products and services for a company. Operations Management is in every organization. Manufacturing Products Providing Insurance Cover Providing Healthcare Etc... 4

5 Managing Operations in Manufacturing Products Receiving requirements Procuring raw materials Recruiting and retaining staff Planning resources Improving quality, cost and delivery Defining policies and procedures Making products Storing and distributing products Etc Managing Operations in Providing Insurance Cover Receiving requests for cover Assessing/reassess risk Answering customer enquiries Processing claims Making payments Recruiting and retaining staff Planning resources Improving quality, cost and delivery Customer care process Accredit repairers/arrange repairs Forecast demand Process applications Process 5

6 Managing Operations in Providing Healthcare Obtaining finance Recruiting and retaining staff Sourcing and procuring supplies Responding to emergencies Scheduling patient operations Capacity planning (bed/theatre management) Quality control/track and trace Dispatch ambulance Contact theatre team Prepare resource (theatre) Kitting for operations Resource planning (Ward) Plan diagnostics resources Liaise with other departments Operations Management Activities (1/3) Forecast Demand Market Product Adapt to comply with customer demand Understand what the customer wants Understand how much the customer wants Know product demand Sourcing and Procurement Order Stationery Check delivery with order Order materials Schedule suppliers Managing stock (getting it in the right place at the right time) 6

7 Operations Management Activities (2/3) Creation of Output Arrange for necessary equipment Schedule material/staff/equipment to produce goods and services Plan work order Produce goods Quality control Delivery Deliver finished products Consider logistics/delivery Dispatching the goods or service to the customer Arrange packaging/presentation Operations Management Activities (3/3) Manage People Employ people Train people Outsource Delegation Managing people Recruit and train staff Schedule labour 7

8 Operations Management is: A management function An organisation s core function Core Functional Areas of the Organisation Manages people, equip., tech., materials and info. to produce goods and/or services Operations Finance Marketing Manages customer demands; Generates sales for goods and services. Manages cash flow, current assets and capital investments 8

9 Organising to Produce Goods and Services Production activities are dependent mutually and connected tightly Sales Marketing Finance POM HRM QA Engineering MIS Accounting 1.2 The Role of Operations Management in Business Operations Management Transforms inputs to outputs Inputs are resources such as People, Material, and Money Outputs are goods and services 9

10 Operations Management s Transformation Process Customer Feedback Inputs Human Resources Facilities and Processes Technologies Materials The Transformation Process Outputs Goods Services Performance Information Operations Management s Transformation Role To add value Increase product value at each stage Value added is the net increase between output product value and input material value Provide an efficient transformation Efficiency means performing activities well for least possible cost 10

11 Manufacturers and Service Organizations Both use technology Both have quality, productivity, & response issues Both must forecast demand Both can have capacity, layout, and location issues Both have customers, suppliers, scheduling and staffing issues 1.3 Decisions in Operations Management Strategic Decisions set the direction for the entire company; they are broad in scope and long-term in nature. Tactical and Operational Decisions focus on specific day-to-day issues like resource needs, schedules, and quantities to produce. Strategic and Tactical decisions must align. 11

12 Strategic Decisions Responsible for, and decisions about: What to make (product development) How to make it (process and layout decisions) or should we buy it Where to make it (site location) How much is needed (high level capacity decisions) Tactical Decisions Address material and labour resourcing within strategy constraints, for example: How many workers are needed and when (labour planning) What level of stock is required and when should it be delivered (inventory and replenishment planning) How many shifts to work. Whether overtime or subcontractors are required (detailed capacity planning) 12

13 Operational Decisions Detailed lower-level (daily/weekly/monthly) planning, execution and control decisions, for example: What to process and when (scheduling), The order to process requirements (sequencing) How work is put on resources (loading) Who does the work (assignments) 1.4 Issues in Operations Management Environmental sustainability, recycling, reuse Customers demand better quality, greater speed, and lower costs Globalisation of supply and demand Achieving and sustaining high quality while controlling cost Integrating new technologies and control systems into existing processes Obtaining, training, and keeping qualified workers and managers Increased cross-functional decision making Integrating production and service activities at multiple sites in decentralized organizations Recognized need to better manage information using ERP and CRM systems 13

14 1.5 What is Production and Operations Management? Production The creation of goods and services by turning inputs into outputs, which are products and services Operations Management Management of the production process Production and operations management (POM) is the management of an organization s production system A production system takes inputs and converts them into outputs The conversion process is the predominant activity of a production system The primary concern of an operations manager is the activities of the conversion process 14

15 Operations Strategy Marketplace Corporate Mission Finance Strategy Operations Strategy Marketing Strategy Operations management Inputs: Materials Customers People Plants Parts Processes Planning & control systems Production System Outputs: Products Services Unit 2 Demand Forecasting 2.1 Forecasting Definition and Fundamental Rules 2.2 Types of Forecasting Methods 2.3 Quantitative Forecasting Methods 15

16 2.1 Forecasting Definition and Rules Forecasting is the prediction of future events on the basis of either: historical data Opinions trend of events, or known future variables Demand forecasting is estimating the future demand for products and services and the resources necessary to produce these outputs It is the first step in planning in any business Forecasting in Business Forecasts provide information that assist managers in guiding future activities toward organizational goals Forecasting is critical to management of all organizational functional areas : Marketing relies on forecasting to predict demand and future sales Finance forecasts stock prices, financial performance, capital investment needs Information systems provides ability to share databases and information Human resources forecasts future hiring requirements 16

17 General Characteristics of Forecasts Forecasts are seldom perfect The prediction does not take account of all factors; The environment is complex and subject to rapid change Forecasts are more accurate for groups or families of items Forecasts are more accurate for shorter time periods; Long term forecasting is problematic Every forecast should include an error estimate Elements of a Good Forecast The forecast should be timely The forecast should be accurate The forecast should be reliable The forecast should be expressed in meaningful units The forecasting technique should be simple to understand and use 17

18 Steps in the Forecasting Process 1. Determine the purpose of the forecast (what are the objectives of forecasting?) 2. Select the Items for which forecasts are needed (single product or group of products) 3. Determine the Time Horizon for the forecast (shortterm, medium-term or long-term) 4. Select the Forecasting Model (Qualitative technique or Quantitative technique) 5. Gather information to be used in forecasting 6. Generate the forecast 7. Monitor forecast accuracy over time Choosing the Forecasting Model The greater the ability to react, the less accurate the forecast has to be A tradeoff between the cost of doing the forecast and the opportunity cost of proceeding with misleading numbers Factors to consider: 1. Length of forecast horizon 2. The amount and type of available data 3. Degree of accuracy required 4. Presence of data patterns 5. Availability of qualified personnel 18

19 2.2 Types of Forecasting Methods Qualitative Methods Rely on subjective opinions from one or more experts. Quantitative Methods Rely on data and analytical techniques. Qualitative Forecasting Methods Usually based on judgments about causal factors that underlie the demand of particular products or services Do not require a demand history for the product or service, therefore are useful for new products/services Approaches vary in sophistication from scientifically conducted surveys to intuitive hunches about future events 19

20 Examples of Qualitative Forecasting Methods Grass Roots: deriving future demand by asking the person closest to the customer. Market Research: trying to identify customer habits; new product ideas. Panel Consensus: deriving future estimations from the synergy of a panel of experts in the area. Historical Analogy: identifying another similar market. Delphi Method: similar to the panel consensus but with concealed identities. 2.3 Quantitative Forecasting Methods Quantitative forecasting methods are mathematical models based on historical data They are based on the assumption that the forces that generated the past demand will generate the future demand, i.e., history will tend to repeat itself Analysis of the past demand pattern provides a good basis for forecasting future demand 20

21 Quantitative Forecasting Methods Categories Time Series Models: Assumes information needed to generate a forecast is contained in a time series of data Assumes the future will follow same patterns as the past Causal Models or Associative Models Explores cause-and-effect relationships Uses leading indicators to predict the future Majority of quantitative forecasting methods fall in the category of time series analysis Quantitative Forecasting Methods Simple Moving Average (Time Series Analysis) Weighted Moving Average (Time Series Analysis) Exponential Smoothing (exponentially weighted moving average) (Time Series Analysis) Exponential Smoothing with Trend (double smoothing) (Time Series Analysis) Linear Regression (Causal or Associative Method) 21

22 Sales Historical Demand Data Patterns 1. Trend is the gradual upward or downward movement of the data overtime. Trends are noted by an upward or downward sloping line (T) 2. Seasonality is a data pattern that repeats itself over the period of one year or less (days, weeks, months, or quarters) (S) 3. Cycle is a data pattern that repeats itself... may take years (C) 4. Random fluctuations are blips in the data caused by chance or random variation or unexplained causes (R) Data Patterns in a Time Series Seasonal variation x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x Year x x x x x x x x x Trend Level 22

23 Short Range Forecasts In cases in which the time series is fairly stable and has no significant trend, seasonal, or cyclical effects, one can use smoothing methods to average out the irregular components of the time series Three common smoothing methods are: Simple moving average Weighted moving average Exponential smoothing Simple Moving Average Used if demand is not growing nor declining rapidly Used often for smoothing, that is removing random fluctuations in the data Equation At-1 + At-2 + At At-n Ft = n where: F t = forecast for period t, n = number of periods to be averaged (AP) A t-1 = actual demand realized in the past period for up to n periods 23

24 Simple Moving Average Example 1 Historical demand for a product is as shown in the Table on the left. Using a simple four month moving average, calculate a for cast for October. The table on the right shows the solution. Month Actual Demand April 60 May 55 June 75 July 60 Aug 80 Sept 75 Oct? Month Actual Demand April 60 May 55 June 75 July 60 Forecast (AP = 4 Month) Aug Sept Oct Simple Moving Average Example 2 During the past ten weeks, sales of cases of Comfort brand headache medicine at Robert s Drugs Store have been as follows: Week Sales Week Sales Forecast the sales in period 11 using a three period moving average. 24

25 Simple Moving Average Example 2 Solution Solution performed in Microsoft Excel software Robert's Drug n=3 F t is the forecast for week t. Week (t ) A t F t #N/A #N/A F 4 (forecast for week 4)=116.7 F 11 (forecast for week 11)=118.3 Thus we would forecast the sales for Week 11 to be Weighted Moving Average This is a variation on the simple moving average where instead of the weights used to compute the average being equal, they are not equal This allows more recent demand data to have a greater effect on the moving average, therefore the forecast The weights must add to 1.0 and generally decrease in value with the age of the data 25

26 Weighted Moving Average Allows different weights to be assigned to past observations Older data usually less important Weights based on experience, trial-and-error Equation F = w A + w A + w A +...+w A t 1 t-1 2 t- 2 3 t-3 n t- n n t=1 w t = 1 w t = weight given to time period t occurrence (weights must add to one) Robert s Drug Sales Forecast using Weighted Moving Average Use a 3 period weighted moving average to forecast the sales for week 11 giving a weight of 0.6 to the most recent period, 0.3 to the second most recent period, and 0.1 to the third most recent period. F 11 = (0.6)*130 + (0.3)*110 + (0.1)* 115= Sales for the most recent period Sales for 2 nd most recent period Sales for 3 rd most recent period Thus we would forecast the sales for week 11 to be

27 Disadvantages of Moving Average Methods o Increasing n (number of periods averaged) makes forecast less sensitive to real changes in the data o They do not forecast trends well. They lag the actual values o Require sufficient historical data Exponential Smoothing Method Premise: The most recent observations might have the highest predictive value. Therefore, we should give more weight to the more recent time periods when forecasting Requires smoothing constant ( ) Which ranges from 0 to 1, and Subjectively chosen The method involves little record keeping of past data It is the most used of short range forecasting techniques because it is fairly accurate, models easily formulated, easily understood, little computation required, and easy to test 27

28 Exponential Smoothing Equation The equation used to compute the forecast is... F t = F t-1 + (A t-1 - F t-1 ), or F t = A t-1 + (1- ) F t-1 where... F t = forecast demand A t = actual demand realized = smoothing constant Note that every observation is included in equation, but their weights get smaller. Robert s Drug Sales Forecast using Exponential Smoothing Method Robert's Drugs F 11 = 0.1 * A F 10 = 0.1 * * = α=0.1 Week (t ) Sales t F t #N/A Forecast Robert s drug sales for period 11 using Exponential Smoothing with α= 0.1 Thus we would forecast sales for week 11 to be

29 Responsiveness with Different Values Actual demand alpha =.1 alpha =.5 alpha = Questions that You should be Asking For the Moving Average technique, how do I determine the best value of AP (n) to use for forecasting? For Exponential Smoothing, how do I determine the best value of α to use? If I realize that a smoothing technique should be employed, how do you know which smoothing technique is best? In order to answer the above questions, we need a criteria for judging the accuracy of a forecasting technique. Once we select a criterion, the method (or parameter) which provides the best value for our criterion is the best method (or parameter) to use for forecasting our scenario. 29

30 Forecast Accuracy Accuracy is the typical criterion for judging the performance of a forecasting approach Accuracy is how well the forecasted values match the actual values Monitoring Forecast Accuracy Accuracy of a forecasting approach needs to be monitored to assess the confidence you can have in its forecasts and changes in the market may require reevaluation of the approach Accuracy can be measured in several ways, two of which are: Mean Absolute Deviation (MAD) Mean Squared Error (MSE) 30

31 Mean Absolute Deviation (MAD) The mean of the absolute values of all forecast errors is calculated, and the forecasting method or parameter(s) which minimize this measure is selected. MAD = n i=1 Actual demand - Forecast demand n i MAD = n t=1 A - F n t t Mean Squared Error (MSE) MSE = (S yx ) 2 Small value for S yx means data points tightly grouped around the line and error range is small. The smaller the standard error the more accurate the forecast. MSE = 1.25(MAD) when the forecast errors are normally distributed 31

32 Selecting the Smoothing Technique for Robert s Drugs Sales Forecasting Determine the smoothing technique that is best for forecasting Robert s Drug sales: A two period moving average, a three period moving average, exponential smoothing (α=0.1), or exponential smoothing (α=0.2) Realistically we should have experimented with more values of n for the moving average, and α for exponential smoothing to determine the absolute best parameters to use for our technique We randomly chose to use the MSE criterion to judge the best technique MSE for Moving Average with AP = 2 Robert's Drug Sales n=2 Error Week (t ) A t F t (A t - F t ) (A t - F t ) #N/A MSE

33 MSE for Moving Average with AP = 3 Robert's Drug Sales n=3 Error Week (t ) A t F t (A t - F t ) (A t - F t ) #N/A #N/A MSE MSE for Exponential Smoothing with = 0.1 Sales α=0.1 Error Week (t ) A t F t (A t - F t ) (A t - F t ) #N/A MSE

34 MSE for Exponential Smoothing with = 0.2 Sales α=0.2 Error Week (t ) A t F t (A t - F t ) (A t - F t ) #N/A MSE Selecting the Smoothing Technique for Robert s Drugs Sales Forecasting Since the three period moving average technique (MA 3 ) provides the lowest MSE value, this is the best smoothing technique to use for forecasting Robert s Drug Sales 34

35 Exponential Smoothing with Trend Attempts to correct (somewhat) the lag in the exponential smoothing method Trend equation with a smoothing constant, (delta) Formulae FIT t = Forecast including trend FIT t = F t + T t F t = FIT t-1 + (A t-1 - FIT t-1 ) T t = T t-1 + (F t - FIT t-1 ) Trend-Adjusted Forecasting Three steps to compute a Trend-Adjusted Forecast: Step 1: Step 2: Step 3: Compute F t, the exponentially smoothed forecast for period t Compute the smoothened trend, T t Calculate the forecast including trend, FIT t = F t + T t 35

36 Trend-Adjusted Forecasting Exercise A large cement manufacturer uses exponential smoothing to forecast demand for a piece of pollutioncontrol equipment. It appears that an increasing trend is present. Month A t Month A t If the initial forecast for month 1 was 650 units and the trend over that period was 0 units, calculate FIT for the 9-month period. Use smoothing constants, = 0.1 and = 0.2. Linear Regression in Forecasting Linear regression is based on 1. Fitting a straight line to data 2. Explaining the change in one variable through changes in other variables. dependent variable = a + b (independent variable) By using linear regression, we are trying to explore which independent variables affect the dependent variable 36

37 Linear Regression Model Regression models are used to test if a relationship exists between variables; that is, to use one variable to predict another Equation is of the form: Y = a + bx+ error where: Y = dependent variable (response) X = independent variable (predictor) a = intercept (value of Y when X = 0) b = slope of the regression line error = random error Simple Linear Regression The predicted line is: Ŷ = a + bx Used to predict Y for some future value of X Sample data are used to estimate the true values for the intercept and slope (a and b values, respectively) Error ( = Y Ŷ), difference between the actual value of Y and the predicted value The Least Squares Method of Linear Regression minimises the sum of squared errors 37

38 Least Squares Regression Equations Ŷ = a + bx a X n 2 Y X 2 X X 2 XY b n n XY X 2 X X 2 Y Developing a Linear Regression Equation Step 1: Collect the historical data required for analysis. Step 2: Identify the X and Y values for each observation. Step 3: Put the data in tabular form and make necessary column calculations. Step 4: Compute the Y intercept (a) and the slope (b) using least squares regression equations. Step 5: Formulate the estimating equation. 38

39 Manufacturing Example Step 1: Collect the historical data required for analysis Step 2 and 3: Put the data in Tabular form. X = manufacturing direct labour hours in hundreds of hours (00 s) Y = manufacturing overhead in thousands of dollars ($000 s) 39

40 Step 5: Formulate the Estimating Equation Ŷ = a + bx Ŷ = X Where: Ŷ = Manufacturing Overhead ($000 s) X = Manufacturing Direct Labour Hours (00 s) Estimate manufacturing overhead given an estimate for manufacturing direct labour hours of 2,100: Ŷ = X Ŷ = (21) Ŷ = Ŷ = thousand dollars Rounded to the nearest dollar, the estimate would be $ 124,

41 Measuring the Fit of the Regression Model To understand how well the model predicts the response variable, we evaluate the following: Correlation Coefficient r strength of the relationship between Y and X variables Coefficient of Determination r 2 - proportion of explained variation Correlation Coefficient The correlation coefficient (r) measures the strength of the linear relationship. r n XY X Y n X X n Y Y Note: -1 < r <

42 Correlation Coefficient Interpretation Coefficient of Determination The coefficient of determination (r 2 ) is the proportion of the variability in Y that is explained by the regression equation. Note: 0 < r 2 < 1 42

43 Manufacturing Example Model Fit r = 0.97 r 2 = 0.94 This means that approximately 94% of the variation in manufacturing overhead (Y) can be explained by its relationship with manufacturing direct labour hours (X). Forecasts with Seasonality Seasonal indices can be used to make adjustments in the forecast for seasonality. A seasonal index indicates how a particular season compares with an average season. The seasonal index can be found by dividing the average value for a particular season by the average of all the data. 43

44 Calculating Seasonal Indices Month Demand Yr 1 Demand Yr 2 2-Yr Avge Monthly Avge Seasonal Index Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec ,128 (1,128/12) (2Yr-Ag/M-Ag) Decomposition Method with Trend and Seasonal Components in historical data Decomposition is the process of isolating linear trend and seasonal factors to develop more accurate forecasts. There are five steps to decomposition: 1. Compute the seasonal index for each season. 2. Deseasonalize the data by dividing each number by its seasonal index. 3. Compute a trend line with the deseasonalized data. 4. Use the trend line to forecast. 5. Multiply the forecasts by the seasonal index. 44

45 Decomposition Method Example Given three years of quarterly data, determine the seasonal Indices Demand Data: Qtr Year 1 Year 2 Year 3 Total Index Qtr 1 SI = Qtr 2 SI = Qtr 3 SI = Qtr 4 SI = Decomposition Method Example Deseasonalize the actual demand data by dividing by the appropriate seasonal factor: Year Quarter Period Demand DeSeas

46 Decomposition Method Example Then perform a linear regression, least squares approximation of the relationship between quarter (X) and deseasonalized sales (Y) Form of a Linear Equation: Y = a + b X From the equations: a = b = X Y XY XX

47 Decomposition Method Example Project the trend using the predictive equation for each quarter of year 4: Quarter 13: F 13 = (13) = 12.9 Quarter 14: F 14 = (14) = 13.1 Quarter 15: F 15 = (15) = 13.3 Quarter 16: F 16 = (16) = 13.5 Decomposition Method Example Adjust for seasonality by multiplying by the seasonal factors for the appropriate quarters: Qtr Proj. ReSeas Therefore, year four forecasts are: Qtr 1 = 7.7 Qtr 2 = 14.6 Qtr 3 = 11.4 Qtr 4 =

48 Demand The graph shows that the forecasts have not lagged actual demand, and has captured both trend and seasonality Demand Proj. ReSeas Quarter Unit 3 Inventory Management 3.1 Inventory and Independent Demand Inventory Management Models 3.2 Determining Inventory Order Quantities 3.3 Determining Inventory Order Points 3.4 Inventory ABC Classification 48

49 3.1 Inventory and Independent Demand Inventory Management Models Inventory is the stock of any item or resource used in an organisation Inventory in the system is the result of imperfection in Demand forecasting, production, and supplier delivery Inventory is the most expensive and the most important asset for an organisation Inventory Types Raw materials and purchased parts from outside suppliers Components: subassemblies that are awaiting final assembly Work in process: all materials or components on the production floor in various stages of production Finished goods: final products waiting for purchase or to be sent to customers Supplies: all items needed but that are not part of the finished product 49

50 Independent Demand versus Dependent Demand Independent Demand: Finished Goods/Parts A Dependent Demand: Raw Materials, Component parts, Sub-assemblies, etc. B(4) C(2) D(2) E(1) D(3) F(2) Product Tree Why Do We Want to hold Inventory (1/3) Finished Goods Inventory: Essential produce to stock positioning, of strategic importance Necessary in level aggregate capacity plans Products can be displayed to customers In-Process (Work-in-Process (WIP)): Necessary in process-focused production, uncouples the states of production, increases flexibility Producing and transporting larger batches of products creates more inventory but may reduce materials-handling and production costs 50

51 Why Do We Want to hold Inventory (2/3) Raw Materials Inventory: Suppliers produce and ship raw materials in batches Larger purchases result in more inventory, but quantity discounts and reduced freight and materials-handling costs may result Raw material sourcing has long and variable lead times Improve customer service: meet or exceed customer s expectations of product availability Why Do We Want to hold Inventory (3/3) Reduce certain costs such as: ordering costs (processing the purchase order, expediting, record keeping and receiving the order into the warehouse) stockout costs (lost sales, dissatisfied customers, disruptions to production) acquisition costs (quantity discount, lower transportation and handling costs) start-up quality costs (learning curve, less changeovers and less scrap) Contribute to the efficient and effective operation of the production system (decoupling) 51

52 Why We Do not Want to hold Inventory Certain costs increase such as: carrying costs (interest on debt, interest income foregone, warehouse rent, lighting, security, receiving, insurance, etc) cost of customer responsiveness (large WIP clog production systems) cost of coordinating production (large WIP lead to schedule coordination problems) cost of diluted return on investment (large inventories reduce ROI and adds to the finance costs by increasing interest rates on debt and reducing stock prices) reduced-capacity costs (Inventory is some form of waste!) large-lot quality cost (defects would lead to large batch losses) cost of production problems (large WIP camouflage underlying production problems!) Common Inventory Problems to Avoid Not stocking products customers expect to be in stock Too many stockouts of products that are stocked Excess inventory and dead stock of other products Product proliferation Lower margins resulting in fewer dollars available to invest in inventory 52

53 Use Inventory Policies to solve the common Inventory Problems Inventory Policies specify decision rules with respect to the point in time when a replenishment of the inventory should be initiated, as well as to the replenishment quantity that should be ordered from the supplying node in the supply network Independent Demand Inventory Management Models 1. Fixed Order Quantity System: this involves placing orders for the same quantity of the item each time that item reaches a preset minimum stocking level, or reorder point 2. Fixed Order Period System: this involves review of inventory at fixed time intervals, and orders are placed for enough items to bring its inventory levels back up to some predetermined level 53

54 Inventory Control Decisions Two fundamental decisions in controlling inventory: 1. How much to order 2. When to order Overall goal is to minimize total inventory cost Inventory Costs Cost of the Items (Acquisition costs) Cost of Ordering or Setup Cost of Carrying or Holding inventory Cost of safety stock Cost of stockouts 54

55 Inventory Carrying Costs Capital Costs: based on inventory investment Inventory Service Costs: relate to insurance and taxes paid Storage Space Costs: relate to warehousing of inventory Inventory Risk Costs: arising from obsolescence, damages, pilferage and relocation costs 3.2 Determining Inventory Order Quantities The procedure of determining inventory order quantities would depend on the inventory management system Fixed Order Quantity System rely on the behaviour of inventory costs to identify the quantities with minimum total inventory stocking costs This section uses the Fixed Order Quantity System 55

56 Inventory Costs Behaviour Costs associated with ordering too much (represented by carrying costs) Costs associated with ordering too little (represented by ordering costs) These costs are opposing costs, that is, as one increases the other decreases The sum of the two costs is the Total Stocking Cost (TSC) When plotted against order quantity, the TSC decreases to a minimum cost and then increases This cost behaviour is the basis for answering the first fundamental question: how much to order? Inventory Cost Behaviour Plot Annual Cost ($) Lower Higher Minimum Total Annual Stocking Costs Total Annual Stocking Costs Annual Carrying Costs Smaller EOQ Annual Ordering Costs Order Quantity Larger 56

57 Fixed-Order Quantity Models Basic EOQ EOQ for Production Lots EOQ with Quantity Discounts Fixed-Order Quantity Models Assumptions Demand for the product is constant and uniform throughout the period Lead time (time from ordering to receipt) is constant Price per unit of product is constant Inventory holding cost is based on average inventory. Ordering or setup costs are constant. All demands for the product will be satisfied (No back orders are allowed) 57

58 Variables used in EOQ Equations ac = Cost of purchasing or producing one unit of a material or product C = Cost of carrying one unit in inventory for one year D = Annual demand for a material d = Demand rate or usage rate EOQ = Optimum number of pieces per order p = Supply rate or production rate Q = Quantity of material ordered at each order point S = Average cost of completing an order for a material TMC = Total of annual acquisition cost and total annual stocking cost for a material TSC = Total annual stocking costs for a material Model I: Basic EOQ Typical assumptions made: annual demand (D), carrying cost (C) and ordering cost (S) can be estimated average inventory level is the fixed order quantity (Q) divided by 2 which implies: no safety stock orders are received all at once demand occurs at a uniform rate no inventory when an order arrives Stockout, customer responsiveness, and other costs are inconsequential acquisition cost is fixed, i.e., no quantity discounts 58

59 Steps in finding the Optimum Order Quantity Develop an expression for the ordering cost. Develop an expression for the carrying cost. Set the ordering cost equal to the carrying cost. Solve this equation for the optimal order quantity, EOQ. Steps in finding the Optimum Order Quantity Annual Ordering Cost: Annual demand x Ordering cost Number of units per order D Q S Annual Carrying or Holding Cost: Average Inventory Q 2 C x Carrying Cost Per Year 59

60 Steps in finding the Optimum Order Quantity D S Q = Q C 2 Q 2 = 2 D S C EOQ = 2 D S C Per Unit versus Percentage Carrying Cost Typically, carrying cost, C, is stated in per unit $ cost per year Sometimes, an annual Interest rate, I, is cited and C must be calculated I multiplied by ac (unit cost) or acquisition cost I(ac) then replaces C 60

61 Per Unit versus Percentage Carrying Cost Per Unit Carrying Cost: EOQ = 2DS C Percentage Carrying Cost: Denominator Change EOQ = 2DS I(ac) Calculating other Parameters Total Stocking Cost: TSC D S Q Q 2 C Expected Number of Orders: N Demand Order Quantity D EOQ Expected Time between Orders: Number of Working Days per Year T Number of Orders per Year Days N 61

62 Basic EOQ Exercise NCZ produces fertilizer to sell to wholesalers. One raw material calcium nitrate is purchased from a nearby supplier at $22.50 per ton. NCZ estimates it will need 5,750,000 tons of calcium nitrate next year. The annual carrying cost for this material is 40% of the acquisition cost, and the ordering cost is $595. a) What is the most economical order quantity? b) How many orders will be placed per year? c) How much time will elapse between orders? Model II: EOQ for Production Lots Used to determine the order size, production lot, if an item is produced at one stage of production, stored in inventory, and then sent to the next stage or the customer Differs from Model I because orders are assumed to be supplied or produced at a uniform rate (p) rather than the order being received all at once It is also assumed that the supply rate, p, is greater than the demand rate, d 62

63 Inventory Level Production and Demand Cycles Production Portion of Cycle (t) = Q/p Q Maximum Inventory Level Demand Portion of Cycle Demand Portion of Cycle Time Developing the Production Order Quantity Annual Ordering Cost: Annual demand x Ordering cost Number of units per order D Q S Annual Carrying or Holding Cost: Average Inventory Q (p - d) [ ]C 2 p x Carrying Cost Per Year 63

64 Setting the Equations equal to Solve for EOQ D Q S Q (p - d) [ ]C 2 p EOQ 2DS C p [ (p - d) ] Note the similarities with Model I equation EOQ for Production Lots Exercise A Power Company buys coal from a Coal mine to generate electricity in rural areas. The Coal mine can supply coal at the rate of 3,500 tons per day for $10.50 per ton. The Power Company uses the coal at a rate of 800 tons per day and operates 365 days per year. The annual carrying cost for coal is 20% of the acquisition cost, and the ordering cost is $5,000. a) What is the economical production lot size? b) What is the Power Company s maximum inventory level for coal? 64

65 Model III: EOQ with Quantity Discounts Under quantity discounts, a supplier offers a lower unit price if larger quantities are ordered at one time This is presented as a price or discount schedule, that is, a certain unit price over a certain order quantity range This means this model differs from Model I because the acquisition cost (ac) may vary with the quantity ordered, that is, it is not necessarily constant Under this condition, acquisition cost becomes an incremental cost and must be considered in the determination of the EOQ Model III: EOQ with Quantity Discounts To evaluate the most economical quantity to Order, use the Total annual material costs (TMC) = Total annual stocking costs (TSC) + Annual acquisition cost Total Annual Material Cost: TMC D Q S Q C (D)ac 2 65

66 EOQ with Quantity Discounts Steps To find the EOQ, the following procedure is used: 1. Compute the EOQ using the lowest acquisition cost. If the resulting EOQ is feasible (the quantity can be purchased at the acquisition cost used), this quantity is optimal and you are finished. If the resulting EOQ is not feasible, go to Step 2 2. Identify the next higher acquisition cost. EOQ with Quantity Discounts Steps 3. Compute the EOQ using the acquisition cost from Step 2. If the resulting EOQ is feasible, go to Step 4. Otherwise, go to Step Compute the TMC for the feasible EOQ (just found in Step 3) and its corresponding acquisition cost. 5. Compute the TMC for each of the lower acquisition costs using the minimum allowed order quantity for each cost. 6. The quantity with the lowest TMC is optimal. 66

67 EOQ with Quantity Discounts Exercise A Motor Vehicle parts supplier has a regional engine oil warehouse in Lusaka. One popular engine oil, Castrol GTX, has estimated demand of 25,000 next year. It costs the supplier $100 to place an order for this oil, and the annual carrying cost is 30% of the acquisition cost. Determine the optimal order quantity if the supplier quotes these prices for the oil: Q ac $ , Determining Inventory Order Points Basis for Setting the Order Point Demand During Lead Time (DDLT) Distributions Setting Order Points 67

68 Basis for Setting the Order Point In the fixed order quantity system, the ordering process is triggered when the inventory level drops to a critical point, the order point This starts the lead time for the item. Lead time is the time to complete all activities associated with placing, filling and receiving the order During the lead time, customers continue to draw down the inventory It is during this period that the inventory is vulnerable to stockout (run out of inventory) Customer service level is the probability that a stockout will not occur during the lead time Basis for Setting the Order Point Thus, the order point is set based on the demand during lead time (DDLT) and the desired customer service level The degree of uncertainty in the DDLT and the customer service level desired determines the amount of safety stock needed Order point (OP) = Expected demand during lead time (EDDLT) + Safety stock (SS) 68

69 Demand During Lead Time (DDLT) Distributions If there is variability in the DDLT, the DDLT is expressed as a distribution discrete continuous In a discrete DDLT distribution, values (demands) can only be integers A continuous DDLT distribution is appropriate when the demand is very high Setting Order Point for a Discrete DDLT Distribution 1. Assume a probability distribution of actual DDLTs is given or can be developed from a frequency distribution 2. Starting with the lowest DDLT, accumulate the probabilities. These are the service levels for the DDLTs 3. Select the DDLT that will provide the desired customer service level as the order point 69

70 Setting Order Point for a Discrete DDLT Distribution Example One of Emerging Technologies inventory items is being analyzed to determine an appropriate level of safety stock. The manager wants an 80% service level during lead time. The item s historical DDLT is: DDLT (cases) Occurrences Setting Order Point for a Discrete DDLT Distribution Example Construct a Cumulative DDLT Distribution Probability Probability of DDLT (cases) of DDLT DDLT or Less To provide 80% service level, OP = 5 cases 70

71 Setting Order Point for a Discrete DDLT Distribution Example Safety Stock (SS): OP = EDDLT + SS SS = OP - EDDLT EDDLT =.4(3) +.3(4) +.2(5) +.1(6) = 4.0 SS = 5 4 = 1 Setting OP for a Discrete DDLT Distribution with known Stockout Costs Exercise Lusaka Eye Hospital has determined that its reorder point for spectacle frames is 50 units. Its carrying cost per frame per year is $5, and stockout (or lost sale) cost is $40 per frame. The store has experienced the probability distribution for inventory demand during the reorder period as shown on the next slide. The optimum number of orders per year is six. How much safety stock should Lusaka Eye Hospital keep on hand? 71

72 Setting OP for a Discrete DDLT Distribution with known Stockout Costs Exercise Initial calculations: OP = 50 (d*lt) C h = $5 /unit per year C ss = $40/ unit (stockout cost) D/Q = 6 times per year Setting Order Point for a Continuous DDLT Distribution Assume that the lead time (LT) is constant Assume that the demand per day is normally distributed with the mean ( d ) and the standard deviation (σ d ) The DDLT distribution is developed by adding together the daily demand distributions across the lead time DDLT ( 1 ) ( 2)... ( L) 72

73 Setting Order Point for a Continuous DDLT Distribution The resulting DDLT distribution is a normal distribution with the following parameters: EDDLT = LT(d ) DDLT LT( d) 2 Setting Order Point for a Continuous DDLT Distribution The customer service level is converted into a Z value using the normal distribution table The safety stock is computed by multiplying the Z value by σ DDLT. The order point is set using OP = EDDLT + SS, or by substitution: OP = LT(d) + z LT(σ ) d 2 73

74 Setting Order Point for a Continuous DDLT Distribution Example A SME supplies lubricants including a popular motor oil SAE 30. When the stock of this oil drops to 20 gallons, a replenishment order is placed. The store manager is concerned that sales are being lost due to stockouts while waiting for an order. It has been determined that lead time demand is normally distributed with a mean of 15 gallons and a standard deviation of 6 gallons. The manager would like to know the probability of a stockout during lead time. Setting Order Point for a Continuous DDLT Distribution Example EDDLT = 15 gallons σ DDLT = 6 gallons OP = EDDLT + Z(σ DDLT ) 20 = 15 + Z(6) 5 = Z(6) Z = 5/6 Z =

75 Setting Order Point for a Continuous DDLT Distribution Example Standard Normal Distribution Area =.2967 Area =.2033 Area = z The probability of a stockout during lead time is Setting Order Point for a Continuous DDLT Distribution Exercise Daily demand for product EPD101 is normally distributed with a mean of 50 units and a standard deviation of 5. Shipping is usually certain with a lead time of 6 days. The cost of placing an order is $8 and annual carrying costs are 20% of unit price of $1.20. A 95% service level is desired for the customers who place orders during the reorder period. Backorders are not allowed. Once stocks are depleted, orders are filled as soon as stocks arrive. No stockout costs. Assume sales made over the entire year. What is the reorder point? What is the cost of carrying safety stocks?. 75

76 3.4 Inventory ABC Classification Start with the inventoried items ranked by dollar value in inventory in descending order Plot the cumulative dollar value in inventory versus the cumulative items in inventory Typical observations A small percentage of the items (Class A) make up a large percentage of the inventory value A large percentage of the items (Class C) make up a small percentage of the inventory value These classifications determine how much attention should be given to controlling the inventory of different items ABC Classification Items kept in inventory are not of equal importance in terms of: dollars invested profit potential sales or usage volume stock-out penalties % of $ Value % of Use A B C 76

77 Percent of Annual Dollar Usage ABC Classification Group A Items - Critical Group B Items - Important Group C Items - Not That Important Inventory Group Dollar Usage (%) Inventory Items (%) Are Complex Quantitative Control Techniques Used? A B C Yes In some cases No ABC Classification A Items B Items C Items Percent of Inventory Items 77

78 ABC Classification and Inventory Policy Greater expenditure on supplier development for A items than for B items or C items Tighter physical control on A items than on B items or on C items Greater expenditure on forecasting A items than on B items or on C items Unit 4 Aggregate Planning 4.1 Production Planning Hierarchy and Aggregate Planning 4.2 Role of Aggregate Planning in Production Management 4.3 The Aggregate Planning Problem 4.4 Aggregate Planning Strategies 78

79 4.1 Production Planning Hierarchy and Aggregate Planning Long-Range Capacity Planning Long-Range (years) Aggregate Planning Master Production Scheduling Production Planning and Control Systems Medium-Range (3-18 months) Short-Range (weeks) Very-Short-Range (hours - days) Pond Draining Systems Push Systems Pull Systems Focusing on Bottlenecks Production Planning Hierarchy Units of Measure Long-Range Capacity Planning Aggregate Planning Master Production Scheduling Production Planning and Control Systems Entire Product Line Product Family Specific Product Model Labour, Materials, Machines Pond Draining Systems Push Systems Pull Systems Focusing on Bottlenecks 79

80 Role of Aggregate Planning in Production Management Given Capacity is limited and has cost Lead times are greater than zero Aggregate planning is: The process by which a company determines levels of capacity, production, subcontracting, inventory, stock-outs, and pricing over a specified time horizon Where the goal is to. maximize profit What is Aggregate Planning? Aggregate Planning is the intermediate planning method used by a firm to seek the most optimal resource inputs in order to meet anticipated demand for product families. Intermediate in this case means anywhere from 3 to 18 months depending on the company and its industry, types of products, etc. 80

81 Aggregate Planning Scope Decisions are usually made at a product family (not Stock Keeping Unit (SKU)) level SKUs within product families tend to use same capacities, have similar costs Avoids too much detail- there might be 10 product families for 1500 SKUs The time frame is generally 3 to 18 months Too early to schedule by SKU Too late to make strategic, long term plans ( build another plant ) Answers question of How can a firm best use the facilities it has? with possibly Do we need to outsource or subcontract? Aggregate Planning Problem Given the demand forecast for each period in the planning horizon, determine the production level, inventory level, and the capacity level for each period that maximizes the firm s profit over the planning horizon Specify the planning horizon Specify the duration of each period (time bucket) typically 1 month Specify key information required to develop an aggregate plan 81

82 Medium-Term Capacity Adjustments Workforce level Hire or layoff full-time workers Hire or layoff part-time workers Hire or layoff contract workers Utilization of the work force Overtime Idle time (under time) Reduce hours worked Inventory level Finished goods inventory Backorders/lost sales Subcontract Information Needed for an Aggregate Plan Demand forecast in each period Production costs Machine costs labour costs, regular time ($/hr) and overtime ($/hr) subcontracting costs ($/hr or $/unit) cost of changing capacity: hiring or layoff ($/worker) and cost of adding or reducing machine capacity ($/machine) Labour/machine hours required per unit Material requirements per unit, material cost and availability Inventory holding cost ($/unit/period) Stock-out or backlog cost ($/unit/period) Yield rates, if applicable (% loss in production or inventory) Constraints: physical or policy limits on overtime, layoffs, capital available, warehousing, stock-outs and backlogs 82

83 Aggregate Planning Goals Specify the optimal combination of: production rate (units completed per unit of time) workforce level (number of workers) inventory on hand (inventory carried from previous period) Meet demand (Sales Forecast) Use capacity efficiently Satisfy inventory policy Minimize cost (Labour, Inventory, Subcontract, Plant and Equipment) Aggregate Plan Outputs Production quantity from regular time, overtime, and subcontracted time: used to determine number of workers and supplier purchase levels Inventory held: used to determine how much warehouse space and working capital is needed Backlog/stock-out quantity: used to determine what customer service levels will be Machine capacity increase/decrease: used to determine if new production equipment needs to be purchased or capacities need to be rededicated 83

84 Why Aggregate Planning is Necessary Fully load facilities and minimize overloading and underloading Make sure enough capacity available to satisfy expected demand Plan for the orderly and systematic change of production capacity to meet the peaks and valleys of expected customer demand Get the most output for the amount of resources available Aggregate Planning Strategies 1. Chase strategy: match production rate to production requirements by varying the workforce (no inventory buildup or shortage allowed) 2. Level strategy: keep a constant workforce who work at maximum capacity (inventory will vary from period to period); workforce level chosen such that the total requirement over the planning horizon can be exactly met 3. Stable workforce: keep a constant workforce who work at maximum capacity; outsource in order to match production and requirements (no inventory buildup or shortage allowed); workforce level chosen such that they can exactly satisfy the requirements in the period with the minimum requirement level 84

85 Aggregate Planning Inputs A forecast of aggregate demand covering the selected planning horizon (3-18 months) The alternative means available to adjust shortto medium-term capacity, to what extent each alternative could impact capacity and the related costs The current status of the system in terms of workforce level, inventory level and production rate Aggregate Planning Production Plans A production plan: aggregate decisions for each period in the planning horizon about workforce level; inventory level; Backorders/Lost sales; production rate; and Units subcontracted/outsourced Projected costs if the production plan was implemented 85

86 Aggregate Planning Methods Informal or Trial-and-Error Approach (Cut and Try Approach) Mathematically Optimal Approaches Linear Programming Linear Decision Rules Computer Search General Steps in Cut and Try Method 1. Convert demand forecasts into production requirements 2. Identify pertinent company policies 3. Develop alternative production plans for the company (pure or mixed strategies?) 4. Calculate the cost of each plan 5. Choose the best plan that fits (minimal costs) 86

87 Aggregate Planning Variables W t = Workforce size in period t H t = Number of workers hired at start of period t L t = Number of workers laid off at start of period t P t = Production in period t EI t = Inventory at the end of period t D t = Demand in period t C t = Number of litres subcontracted for period t O t = Number of overtime hours worked in period t Example: CA&J Company JAN FEB MAR APR MAY JUN Total Demand Forecast 1,800 1,500 1, ,100 1,600 8,000 Working Days Costs Inventory holding Backorders Hiring and training Layoff Labour time required Straight time cost (8 hours) Outsourcing $1.50/unit/month $5.00/unit/month $200.00/worker $250.00/worker 0.20 units/hour $4.00/hour $20.00/unit Inventory Beginning Inventory 400 units Labour Beginning Labour 40 workers 87

88 First step: Analyze the requirements JAN FEB MAR APR MAY JUN Beginning Inventory 400 Demand Forecast 1,800 1,500 1, ,100 1,600 Production requirement Ending Inventory First step: Analyze the requirements JAN FEB MAR APR MAY JUN Beginning Inventory Demand Forecast 1,800 1,500 1, ,100 1,600 Production requirement 1,400 1,500 1, ,100 1,600 Ending Inventory

89 Plan 1: Chase strategy (variable workforce) JAN FEB MAR APR MAY JUN Production requirement 1,400 1,500 1, ,100 1,600 Production hours required Days per month Worker hours per month Workers required Workers hired Hiring cost Workers laid off Layoff cost Labour cost Plan 1: Chase strategy JAN FEB MAR APR MAY JUN Production requirement 1,400 1,500 1, ,100 1,600 Production hours required 7,000 7,500 5,500 4,500 5,500 8,000 Days per month Worker hours per month Workers required Workers hired Hiring cost ,800 Workers laid off Layoff cost 0 0 4,000 1, Labour cost 28,000 30,000 22,000 18,000 22,000 32,000 89

90 Plan 1: Chase strategy Hiring cost 6,400 Layoff cost 5,500 Labour cost 152,000 Total Cost 163,900 Plan 2: Level strategy (Level Capacity) JAN FEB MAR APR MAY JUN Beginning inventory 400 Working days per month Production hours available Monthly production level Demand Forecast 1,800 1,500 1, ,100 1,600 Ending Inventory Shortage Cost Inventory cost Labour cost 90

91 Plan 2: Level strategy Number of workers required = Total hours required over planning horizon/(8*total days) = 38,000/(8*125) = 38. This is the no. of workers for each month JAN FEB MAR APR MAY JUN Beginning inventory Working days per month Production hours available ,384 6,688 6,080 Monthly production level 1,338 1,155 1,277 1,277 1,338 1,216 Demand Forecast 1,800 1,500 1, ,100 1,600 Ending Inventory Shortage Cost Inventory cost Labour cost Plan 2: Level strategy Layoff cost 500 Shortage cost Inventory cost 3, Labour cost 152,000 Total Cost 156,

92 Plan 3: Stable strategy with outsourcing JAN FEB MAR APR MAY JUN Production requirement 1,400 1,500 1, ,100 1,600 Working days per month Monthly production hours Monthly production level Monthly outsourcing level Monthly outsourcing cost Monthly labour cost Plan 3: Stable strategy with outsourcing Number of workers = enough workers to cover requirements in April = 900*5/(21*8) = 27 workers (this is the no. of workers for each month) JAN FEB MAR APR MAY JUN Production requirement 1,400 1,500 1, ,100 1,600 Working days per month Monthly production hours 4,752 4,104 4,536 4,536 4,752 4,320 Monthly production level Monthly outsourcing level Monthly outsourcing cost 9,000 13,580 3, ,000 14,720 Monthly labour cost 19, ,144 18,144 19,008 17,280 92

93 Plan 3: Stable strategy with outsourcing Layoff Cost 3,250 Outsourcing Cost 44,160 Labour Cost 108,000 Total Cost 155,410 Comparison Layoff cost 500 Hiring cost 6,400 Shortage cost 3,495 Layoff Cost 3,250 Layoff cost 5,500 Inventory cost Outsourcing Cost 44,160 Labour cost 152,000 Labour cost 152,000 Labour cost 108,000 Total Cost 163,900 Total Cost 156, Total Cost 155,410 Chase Level Stable 93

94 Factors important in the choice of the option The cost of each option Work environment harmony (managementunion relations) Ergonomics aspects during increased overtime durations (fatigue, morale, productivity) Impact on product quality due to overworking (excessive overtime) Flexibility of increasing or decreasing unplanned production levels Unit 5 Master Production Schedule (MPS) 5.1 Master Production Schedule (MPS) 5.2 Time Fences in MPS 5.3 Developing an MPS 5.4 Rough-Cut Capacity Planning 94

95 5.1 Master Production Schedule (MPS) A Master Production Schedule (MPS) is a realistic, detailed, manufacturing plan for which all possible demands upon the manufacturing facilities (such as available personnel, working hours, management policy and goals) have been considered and are visualized The MPS is a statement of what the company expects to produce and purchase expressed in selected items, specific quantities and dates Objectives of MPS Determine the quantity and timing of completion of end items over a short-range planning horizon Schedule end items (finished goods and parts shipped as end items) to be completed promptly and when promised to the customer Avoid overloading or underloading the production facility so that production capacity is efficiently utilized and low production costs result 95

96 Effective MPS Give management the information to control the manufacturing operation Tie overall business planning and forecasting to detail operations Enable marketing to make legitimate delivery commitments to warehouses and customers Greatly increase the efficiency and accuracy of a company's manufacturing as it drives detailed material and production requirements in Material Requirements Planning (MRP) phase 5.2 Time Fences in MPS The rules for scheduling 1-2 weeks No Change Frozen 2-4 weeks +/- 5% Change Firm 4-6 weeks +/- 10% Change Full 6+ weeks +/- 20% Change Open 96

97 The Rules of Scheduling Do not change orders in the frozen zone Do not exceed the agreed on percentage changes when modifying orders in the other zones Try to level load as much as possible Do not exceed the capacity of the system when promising orders If an order must be pulled into level load, pull it into the earliest possible week without missing the promise 5.3 Developing an MPS Using input information: Customer orders (end items quantity, due dates) Forecasts (end items quantity, due dates) Inventory status (balances, planned receipts) Production capacity (output rates, planned downtime) Schedulers place orders in the earliest available open slot of the MPS 97

98 Developing and MPS Schedulers must: estimate the total demand for products from all sources assign orders to production slots make delivery promises to customers, and make the detailed calculations for the MPS Developing an MPS Example Arizona Instruments produces bar code scanners for consumers and other manufacturers on a produce-to-stock basis. The production planner is developing an MPS for scanners for the next 6 weeks. The minimum lot size is 1,500 scanners, and the safety stock level is 400 scanners. There are currently 1,120 scanners in inventory. The estimates of demand for scanners in the next 6 weeks are shown on the next slide. 98

99 Developing an MPS Example Demand Estimates WEEK CUSTOMERS BRANCH WAREHOUSES MARKET RESEARCH PRODUCTION RESEARCH Developing an MPS Example Computations WEEK CUSTOMERS BRANCH WAREHOUSES MARKET RESEARCH PRODUCTION RESEARCH TOTAL DEMAND BEGINNING INVENTORY REQUIRED PRODUCTION ENDING INVENTORY

100 Developing an MPS Example MPS for Bar Code Scanners WEEK SCANNER PRODUCTION Rough-Cut Capacity Planning As orders are slotted in the MPS, the effects on the production work centers are checked Rough-Cut Capacity Planning (RCCP) identifies underloading or overloading of capacity Rough-Cut Capacity plans are used only to determine if sufficient capacity exists over broad time frames such as a month or a quarter RCCP is the validation of MPS with respect to capacity 100

101 Rough-Cut Capacity Planning Example Emerging Technologies makes a line of computer printers on a produce-to-stock basis for other computer manufacturers. Each printer requires an average of 24 labour-hours. The plant uses a backlog of orders to allow a level-capacity aggregate plan. This plan provides a weekly capacity of 5,000 labour-hours. Emerging Technologies rough-draft of an MPS for its printers is shown on the next slide. Does enough capacity exist to execute the MPS? If not, what changes do you recommend? Rough-Cut Capacity Planning Example Rough-Cut Capacity Analysis WEEK PRODUCTION TOTAL 1030 LOAD CAPACITY UNDER or (OVER) LOAD (1000)(1720)

102 Rough-Cut Capacity Planning Example Rough-Cut Capacity Analysis: The plant is underloaded in the first 3 weeks (primarily week 1) and it is overloaded in the last 2 weeks of the schedule. Some of the production scheduled for week 4 and 5 should be moved to week 1. Rough-Cut Capacity Planning Example 2 A firm produces two products, A and B, on a produce-tostock basis. The safety stock for A is 30 and for B it is 40. The fixed lot size for A is 50 and for B it is 60. The beginning inventory for A is 70 and for B it is 50. Prepare an MPS for these two products for the next 6 weeks using the demand estimates given on the next slide. Suppose that the final assembly for the two products is done on the same line, determine if the MPS developed is underloaded or overloaded given that the final assembly line has a weekly capacity of 100 hours available, while each Product A requires 0.9 hours and each Product B requires 1.6 hours of final assembly capacity. 102

103 Rough-Cut Capacity Planning Example 2 Product A 1 WEEK INTRA-COMPANY BRANCH WAREHOUSES R&D CUSTOMER DEMAND Product B 1 WEEK INTRA-COMPANY BRANCH WAREHOUSES R&D CUSTOMER DEMAND Rough-Cut Capacity Planning Example 2 Product A 1 WEEK TOTAL DEMAND BEGINNING INVENTORY REQUIRED PRODUCTION ENDING INVENTORY Product B 1 WEEK TOTAL DEMAND BEGINNING INVENTORY REQUIRED PRODUCTION ENDING INVENTORY

104 Rough-Cut Capacity Planning Example 2 MPS for Products A and B WEEK PRODUCT A PRODUCT B Rough-Cut Capacity Planning Steps Compute the actual final assembly hours required at the plant each week and the total 6 weeks to produce the MPS (this is the Load) Compare the load to the labour-hours capacity in each week and for the total 6 weeks (this is the rough-cut capacity planning) Assess if enough production capacity exist to produce the MPS, and recommend any changes to the MPS if necessary. 104

105 Rough-Cut Capacity Planning Example 2 Rough-Cut Capacity Analysis WEEK Product A assembly hours TOTAL Product B assembly hours TOTAL LOAD (Hours) CAPACITY (Hours) UNDER or (OVER) LOAD (41) (41) 55 (41) Rough-Cut Capacity Planning Example 2 Rough-Cut Capacity Analysis The final assembly line is underloaded in weeks 1, 2, and 5, and it is overloaded in weeks 3, 4, and 6 of the schedule. A better balance of weekly final assembly capacity is possible if some of the production lots are moved into earlier weeks of production schedule. Move lots of Product A from weeks 4 and 6 into weeks 3 and 5, and move the lot of Product B from week 3 into week

106 Rough-Cut Capacity Planning Example 2 Revised Rough-Cut Capacity Analysis WEEK Product A assembly hours TOTAL 180 Product B assembly hours TOTAL LOAD (Hours) CAPACITY (Hours) UNDER or (OVER) LOAD Demand Management Review customer orders and promise shipment of orders as close to request date as possible Update MPS at least weekly... work with Marketing to understand shifts in demand patterns Produce to order... focus on incoming customer orders Produce to stock... focus on maintaining finished goods levels Planning horizon must be as long as the longest lead time item 106

107 Unit 6 Material Requirement Planning (MRP) 6.1 Material Requirement Planning (MRP) 6.2 MRP System 6.3 MRP: Typical Procedures 6.4 Lot-Sizing in MRP 6.1 Material Requirement Planning(MRP) Computer-based system for determining the quantity and timing for the acquisition of dependent demand items needed to satisfy the MPS requirements Explodes Master Schedule (MPS) into required amounts of raw materials and subassemblies to support MPS Nets against current orders and inventories to develop production and purchased material ordering schedules 107

108 Relationship of MRP and other Plans Engineering design changes Bill of Materials (BOM) file Firm orders from known customers Aggregate Product Plan Master Production Schedule (MPS) Material Requirements Planning (MRP) Forecast of demand from random customers Inventory transactions Inventory records file Reports 6.2 MRP System Inputs Service-Parts Orders and Forecasts Inventory Status File Master Production Schedule Bill of Materials File 7/4/2016 MRP System Outputs Inventory Transaction Data Order Changes Planned Order Schedule Planning Reports Performance Reports Exception Reports 108

109 Inventory Status File Includes information on the status of each item by time period Gross requirements Scheduled receipts Amount on hand Lead times Lot sizes and more MRP Logic Terminology (1/6) Gross Requirements These requirements are typically forecast for independent demand items Assumes no on-hand inventory 109

110 MRP Logic Terminology (2/6) On-hand inventory The inventory physically present in the facility Allocated inventory The inventory physically present in the facility but allocated to a particular work order or purchase order MRP Logic Terminology (3/6) Net requirements A quantity of an item that must be purchased or manufactured in order to be able to fully deliver independent demand requirements in a timely fashion Presence of positive net requirements signals that an order must be planned to be received in a given period 110

111 MRP Logic Terminology (4/6) Planned order receipts Quantities that must be planned to be received in some future periods in order to meet the requirements Planned order released Quantities that must be planned to be released in some future periods in order to meet the requirements MRP Logic Terminology (5/6) Scheduled receipts Quantities that will be received in some future periods as their corresponding orders have been released in the past Planned order receipts become scheduled receipts at the time when they are released to the shop or to suppliers 111

112 MRP Logic Terminology (6/6) Gross to Net logic: Net Requirements = Gross Requirements + Allocated Inventory + Safety Stock - Inventory On Hand + Backorders 6.3 MRP: Typical Procedures 1. Develop a Bill of Materials (BOM). The BOM identifies The components Component descriptions Amount required to produce 1 unit of final product 2. Develop a Material Structure Tree The tree has several levels depending on the depth of subcomponent required Parents and components are identified 1. Items above any level are parents 2. Items below any level are components The tree shows how many units are needed at each level of production 112

113 3. Determine the Gross Material Requirements Once the materials structure tree is done, construct a gross material requirements plan. This is a time schedule that shows when an item must be ordered 1. when there is no inventory on hand, or 2. when the production of an item must be started in order to satisfy the demand for the finished product at a particular date. 4. Determine the Net Material Requirements Plan A net material requirements plan is constructed using the gross materials requirements plan and the inventory on-hand information This plan includes, for each item: Gross requirements, On-hand inventory, Net requirements, Planned-order receipts (Scheduled receipts), and Planned-order releases 113

114 Material Structure Tree Example Assume demand for product A is 50 units. Each unit of A requires 1. 2 units of B, which in turn requires 1. 2 units of D 2. 3 units of E 2. 3 units of C, which in turn requires 1. 1 unit of E 2. 2 units of F Material Structure Tree: An Example 114

115 This structure tree has 3 levels: 0, 1, and 2 There are 3 parents: A, B, C There are 5 components: B, C, D, E, F B and C are parents and components Numbers in parentheses next to the levels indicate the amounts needed for 1 unit of final product of A For example, B(2) indicates that it takes 2 units of B to make 1 unit of A Component Calculations to meet Demand of 50 units of Product A: Part B: 2 # of A = 2 50 = 100 Part C: 3 # of A = 3 50 = 150 Part D: 2 # of B = = 200 Part E: 3 # of B + 1 # of C = = 450 Part F: 2 # of C = =

116 A Material Requirement Plan (MRP) for Awesome Speakers is shown as an example, starting with the product tree. Fifty Awesome speakers are required. Product Structure for Awesome A 7/4/

117 Requirements for 50 Awesome A Component Calculations: Part B: 2 # of A = 2 50 = 100 Part C: 3 # of A = 3 50 = 150 Part D: 2 # of B + 2 # of F = = 800 Part E: 2 # of B + 2 # of C = = 500 Part F: 2 # of C = = 300 Part G: 1 # of F = = 300 Lead Times for Awesome Speaker Kits (As) COMPONENT LEAD TIME (Weeks) A 1 B 2 C 1 D 1 E 2 F 3 G 2 117

118 Using Lead Times a Time-Phased Product Structure can be constructed. Note: this is not necessary if you know the delivery date Start production of D 2 weeks 1 week G D 2 weeks Must have D and E completed here so production can begin on B 1 week 3 weeks 2 weeks D E 2 weeks to produce B A E 1 week 1 week C F Gross Materials Requirements Plan for 50 Speaker Kits A A B C D E F D G 7/4/2016 WEEK Required date Order release date 50 Required date Order release date 100 Required date Order release date 150 Required date Order release date 200 Required date Order release date Required date Order release date 300 Required date Order release date 600 Required date Order release date LT 1 wk 2 wks 1 wk 1 wk 2 wks 3 wks 1 wk 2 wks 118

119 Inventory for Awesome Speaker Kits (As) ITEM ON-HAND INVENTORY A 10 B 15 C 20 D 10 E 10 F 5 G 0 Net Requirements Plan Item A ITEM: A Lead Time: 1 week LOT SIZE: Lot For Lot 1 WEEK GROSS REQUIREMENTS 50 SCHEDULED RECEIPTS AVAILABLE (PROJ. ON-HAND = 10) NET REQUIREMENTS 40 PLANNED ORDER RELEASES

120 Net Requirements Plan Item B ITEM: B Lead Time: 2 weeks LOT SIZE: Lot For Lot 1 WEEK GROSS REQUIREMENTS 80 A SCHEDULED RECEIPTS 65 AVAILABLE (PROJ. ON-HAND = 15) NET REQUIREMENTS 65 PLANNED ORDER RELEASES 65 Net Requirements Plan Item C ITEM: C Lead Time: 1 week LOT SIZE: Lot For Lot 1 WEEK GROSS REQUIREMENTS 120 A SCHEDULED RECEIPTS 100 AVAILABLE (PROJ. ON-HAND = 20) NET REQUIREMENTS 100 PLANNED ORDER RELEASES

121 Net Requirements Plan Item E ITEM: E Lead Time: 2 weeks LOT SIZE: Lot For Lot 1 WEEK GROSS REQUIREMENTS 130 B 200 C SCHEDULED RECEIPTS AVAILABLE (PROJ. ON-HAND = 10) NET REQUIREMENTS PLANNED ORDER RELEASES Net Requirements Plan Item F ITEM: F Lead Time: 3 weeks LOT SIZE: Lot For Lot 1 WEEK GROSS REQUIREMENTS 200 C SCHEDULED RECEIPTS 195 AVAILABLE (PROJ. ON-HAND = 5) NET REQUIREMENTS 195 PLANNED ORDER RELEASES

122 Net Requirements Plan Item D ITEM: D Lead Time: 1 week LOT SIZE: Lot For Lot WEEK GROSS REQUIREMENTS SCHEDULED RECEIPTS 390 F B 130 AVAILABLE (PROJ. ON-HAND = 10) NET REQUIREMENTS PLANNED ORDER RELEASES Net Requirements Plan Item G ITEM: G Lead Time: 2 weeks LOT SIZE: Lot For Lot 1 WEEK GROSS REQUIREMENTS 195 F SCHEDULED RECEIPTS 195 AVAILABLE (PROJ. ON-HAND = 0) 0 0 NET REQUIREMENTS 195 PLANNED ORDER RELEASES

123 6.4 Lot-Sizing in MRP Lot-size is the quantity ordered/produced at one time Large lots are preferred because: Changeovers cost less and capacity greater Annual cost of purchase orders less Price breaks and transportation breaks can be utilized Small lots are preferred because: Lower inventory carrying cost Reduced risk of obsolescence Shorter cycle time to produce customer order Lot-Sizing Techniques Economic Order Quantity (EOQ) does not consider quantity discounts does not always provide the most economical approach with lumpy demand Lot-for-Lot (LFL) accommodates lumpy demand Period Order Quantity (POQ) The best method, resulting in least cost, depends on cost and demand patterns. 123

124 Lot-Sizing for Speaker Kits Speaker Kits Inc., wants to compute its ordering and carrying cost of inventory on lot-for-lot criteria. Speaker Kits has determined that, for the 12-inch speaker/booster assembly, setup cost is $100 and holding cost is $1 per period. The production schedule, as reflected in net requirements for assemblies, is shown on the next slide. Lead time is 1 week. What is the total cost? Lot-Sizing Techniques: Lot-for-Lot Gross Requirements Scheduled Receipts Projected on Hand Net Requirements Planned Order Releases

125 Lot-Sizing Techniques: Economic Order Quantity Gross Requirements Scheduled Receipts Projected on Hand Net Requirements Planned Order Releases Lot-Sizing Techniques: Part Period Balancing Gross Requirements Scheduled Receipts Projected on Hand Net Requirements Planned Order Releases

126 Example: Bill of Material Product A & Product B A C (3) Other Components D(2) Other Subcomponents B C (2) Other Components D(2) Other Subcomponents Demand and other information for Products A and B Month Product A B It takes two months to produce a unit of C and one month to produce a unit of D. At the beginning of month 1, there are 150 units of C and 600 units of D in stock from previously planned manufacturing or purchasing orders, while 50 units of C and 100 units of D are scheduled to be received at the beginning of month 2. Develop MRP using the Lot-for-Lot, Period Order Quantity (POQ) with period (P) = 3, Economic Order Quantity (EOQ) and Part-Period Balancing (PPB) lot sizing strategies. For the EOQ assume that the average demand for component C is 225 units per month, the set up cost of producing C during a month is $225 and the cost of holding one unit of C in inventory for one month is $

127 Equations to use in developing the MRP NR t (C)=[(GR t (C ) SR t (C ) OHI t-1 (C)] + OHI t (C)=[SR t (C) + OHI t-1 (C ) GR t (C)] + Where: NR t : Net Requirements in period t GR t : Gross Requirements in period t SR t : Scheduled Receipts in period t OHI t : On-Hand Inventory in period t Net Requirements Plan Item C Month GR OHI (150) SR NR PP???????????? 127

128 Lot-for-Lot with 2 Months Lead Time-Item C Month GR OHI (150) SR NR PP X X Lot-for-Lot with 1 Months Lead Time-Item D Month GR X X OHI (600) SR X X NR X X PP X X X 128

129 MRP Record Item C (POQ; P = 3) Month GR OHI (150) SR NR PP X X MRP Record Item C (EOQ) Month GR OHI (150) SR NR PP X X 129

130 Net Requirements Plan Item C (PPB) Month GR OHI (150) SR NR PP X X Choosing the Lot Sizing Strategy for Component C The following are the cost for each of the last three lot sizing approaches: POQ Method: Holding cost = 0.5( ) = $905 Setup cost = 225(3) = $675 Total cost = $1,580 EOQ Lot-sizing Method: Holding cost = 0.5( ) = $1,685 Setup cost = 225(4) = $900 Total cost = $2,585 Part-Period Balancing Method: Holding cost = 0.5( ) = $325 Setup cost = 225(4) = $900 Total cost = $1,225 Use the PPB which gives the lowest cost in this case. 130

131 Unit 7 Shop-Floor Planning and Control 7.1 Scheduling Process-Focused Manufacturing 7.2 Input-Output Control 7.3 Order-Sequencing Rules 7.4 Minimising Total production Time 7.1 Scheduling Process-Focused Manufacturing Process-focused factories are often called job shops. A job shop s work centers are organized around similar types of equipment or operations. Workers and machines are flexible and can be assigned to and reassigned to many different orders. Job shops are complex to schedule. 131

132 Job Shop vs Flow Shop Job Shops use a FUNCTIONAL LAYOUT In the functional layout, like processors are grouped into departments. As products visit the department they are placed on one of the processors of the group. Flow Shops use a PRODUCT LAYOUT In the product layout the required processors for a product are identified and arranged using a linked station-to-station alignment Job Shop 132

133 Flow Shop Pre-Production Planning Design the product in customer order Plan the operations the product must pass through... this is the routing plan Work moves between operations on a move ticket 133

134 Common Shop Floor Control Activities Assigning a priority to each order. This aids in setting the sequence of producing orders at work centers. Issuing dispatching lists to each work centre Tracking work-in-progress (WIP) and keeping the system updated Controlling input-output on all work centers Measuring efficiency, utilization, and productivity of workers and machines at each work centre. 7.2 Input-Output Control Input-output control identifies problems such as insufficient or excessive capacity, bottlenecks or any issues that prevents the order from being completed on time Input Work Center Output Planned input should never exceed planned output 134

135 Input-Output Control Report (Week -1) Week: Planned input: labor-hrs Actual input: labor-hrs Cumulative deviation Planned output: labor-hrs Actual output: labor-hrs Cumulative deviation Planned ending WIP: l-h Actual ending WIP: l-h 70 Input-Output Control Report (Week 1) Week: Planned input: labor-hrs Actual input: labor-hrs 50 Cumulative deviation -50 Planned output: labor-hrs Actual output: labor-hrs 110 Cumulative deviation -10 Planned ending WIP: l-h Actual ending WIP: l-h

136 Input-Output Control Report (Week 4) Week: Planned input: labor-hrs Actual input: labor-hrs Cumulative deviation Planned output: labor-hrs Actual output: labor-hrs Cumulative deviation Planned ending WIP: l-h Actual ending WIP: l-h Assigning Jobs to Work Centers: How Many Jobs/Day/Work Center Infinite loading Assigns jobs to work centers without regard to capacity Unless excessive capacity exists, long queues occur Finite loading Uses work center capacity to schedule orders Popular scheduling approach Integral part of Capacity Requirement Planning (CRP) 136

137 Assigning Jobs to Work Centers: Which Job Gets Built First? Forward scheduling Jobs are given earliest available time slot in operation excessive WIP usually results Backward scheduling Start with promise date and work backward through operations reviewing lead times to determine when a job has to pass through each operation Less WIP but must have accurate lead times Forward Scheduling Backward Scheduling B E B E Today Due Date Today Due Date 137

138 7.3 Order-Sequencing Rules We want to determine the sequence in which we will process a group of waiting orders at a work center Many different sequencing rules can be followed in setting the priorities among orders There are numerous criteria for evaluating the effectiveness of the sequencing rules Sequencing Rules (1/2) First-Come First-Served (FCFS) Next job to process is the one that arrived first among the waiting jobs Shortest Processing Time (SPT) Next job to process is the one with the shortest processing time among the waiting jobs Earliest Due Date (EDD) Next job to process is the one with the earliest due (promised finished) date among the waiting jobs 138

139 Sequencing Rules (2/2) Least Slack (LS) Next job to process is the one with the least [time to due date minus total remaining processing time] among the waiting jobs Critical Ratio (CR) Next job to process is the one with the least [time to due date divided by total remaining processing time] among the waiting jobs Least Changeover Cost (LCC) Sequence the waiting jobs such that total machine changeover cost is minimized Evaluating Sequencing Rules Average flow time - average amount of time jobs spend in shop Average number of jobs in system Average job lateness - average amount of time job s completion date exceeds its promised delivery date Changeover cost - total cost of making machine changeovers for group of jobs 139

140 Example: Evaluating Sequencing Rules Use the FCFS, SPT, and Critical Ratio rules to sequence the five jobs below. Evaluate the rules on the bases of average flow time, average number of jobs in the system, and average job lateness. Job Processing Time Time to Promised Completion A 6 hours 10 hours B C 9 8 D E 8 7 FCFS Rule Sequence: A > B > C > D > E Processing Promised Flow Job Time Completion Time Lateness A B C D E

141 FCFS Rule Performance: Average flow time: 141/5 = 28.2 hours Average number of jobs in the system: 141/49 = 2.88 jobs Average job lateness: 90/5 = 18.0 hours SPT Rule Sequence: A > E > C > B > D Processing Promised Flow Job Time Completion Time Lateness A E C B D

142 SPT Rule Performance: Average flow time: 127/5 = 25.4 hours Average number of jobs in the system: 127/49 = 2.59 jobs Average job lateness: 76/5 = 15.2 hours Critical Ratio Rule Sequence: E > C > D > B > A Processing Promised Flow Job Time Completion Time Lateness E (.875) C (.889) D (1.00) B (1.33) A (1.67)

143 Critical Ratio Rule Performance: Average flow time: 148/5 = 29.6 hours Average number of jobs in the system: 148/49 = 3.02 jobs Average job lateness: 93/5 = 18.6 hours Comparison of Rule Performance: Average Average Average Flow Number of Jobs Job Rule Time in System Lateness FCFS SPT CR SPT rule was superior for all 3 performance criteria. 143

144 Controlling Changeover Costs Changeover costs - costs of changing a processing step in a production system over from one job to another: Changing machine settings Getting job instructions Changing material Changing tools Usually, jobs should be processed in a sequence that minimizes changeover costs Controlling Changeover Costs: Job Sequencing Heuristic 1. First, select the lowest changeover cost among all changeovers (this establishes the first two jobs in the sequence) 2. The next job to be selected will have the lowest changeover cost among the remaining jobs that follow the previously selected job 144

145 Example 1: Minimizing Changeover Costs Hardtimes Heat Treating Service has 5 jobs waiting to be processed at work center #11. The job-to-job changeover costs are listed below. What should the job sequence be? Jobs That Follow Jobs That Precede A B C D E A B C D E Job sequence with minimum changeover cost: A follows D ($50 is the least c.o. cost) C follows A ($92 is the least following c.o. cost) B follows C ($69 is the least following c.o. cost) E follows B (E is the only remaining job) Job sequence is D A C B E Total changeover cost = $ = $

146 Example 2: Minimizing Changeover Costs A printing company does custom printing jobs for local firms and schools. The operations manager is currently developing a weekly printing schedule for the printing press. He has developed changeover costs for six waiting jobs. All jobs carry equal priority, so the deciding factor in selecting a job sequence is the total changeover cost for the six jobs. What is the preferred job sequence? Jobs with Changeover Costs Jobs That Follow Jobs That Precede A B C D E F A B C D E F

147 Job sequence with minimum changeover cost: There is a tie on the starting lowest changeover cost between (D-A) and (C-D). Therefore develop two sequences and choose one with least total cost. Sequence 1: D A F C B E Total changeover cost = = $85 Sequence 2: C D A F B E Total changeover cost = = $80 In this case sequence C-D-A-F-B-E is the preferred sequence. 7.4 Minimizing Total Production Time Sequencing n Jobs through Two Work Centers When several jobs must be sequenced through two work centers, we may want to select a sequence that must hold for both work centers Johnson s rule can be used to find the sequence that minimizes the total production time through both work centers 147

148 Johnson s Rule 1. Select the shortest processing time in either work center 2. If the shortest time is at the first work center, put the job in the first unassigned slot in the schedule. If the shortest time is at the second work center, put the job in the last unassigned slot in the schedule. 3. Eliminate the job assigned in step Repeat steps 1-3, filling the schedule from the front and back, until all jobs have been assigned a slot. Example: Minimizing Total Production Time It is early Saturday morning and The Finest Detail has five automobiles waiting for detailing service. Each vehicle goes through a thorough exterior wash/wax process and then an interior vacuum/shampoo/polish process. The entire detailing crew must stay until the last vehicle is completed. If the five vehicles are sequenced so that the total processing time is minimized, when can the crew go home. They will start the first vehicle at 7:30 a.m. 148

149 Time Estimates for the jobs at each work station Exterior Interior Job Time (hrs.) Time (hrs.) Cadillac Bentley Lexus Porsche Infiniti Johnson s Rule Least Work Schedule Time Job Center Slot 1.4 Infiniti Interior 5 th 1.6 Porsche Interior 4 th 1.9 Lexus Exterior 1 st 2.0 Cadillac Exterior 2 nd 2.1 Bentley Exterior 3 rd 149

150 Gantt Chart of Job Sequencing Exterior L C B P I Idle Interior Idle L C B P I It will take from 7:30 a.m. until 7:30 p.m. (not allowing for breaks) to complete the five vehicles. Unit 8 Total Quality Management 8.1 Quality Definition 8.2 Nature of Quality 8.3 Quality Advocates 8.4 Total Quality Management 150

151 8.1 Quality Definition The quality of a product or service is a customer s perception of the degree to which the product or service meets his or her expectations. Quality Definitions (1/2) Design quality How does the product or service appear in preproduction or pre-delivery phases (i.e. on a CAD terminal, on a blueprint, etc.) Conformance quality Degree to which the design specifications are met by the production or delivery system 151

152 Quality Definitions (2/2) Performance quality Degree to which the product or service meets or exceeds customer expectations in the marketplace (i.e. product or service performance characteristics) Societal quality Degree to which the product or service meets or exceeds societal expectations (i.e. product or service performance characteristics important to the general public) Quality: Meeting the Customer Requirements Fitness for Purpose Juran The totality of features and characteristics of a product or service that bear on its ability to satisfy stated or implied needs BS 4778:1987 (ISO 8402, 1986) Quality should be aimed at the needs of the customer, present and future Deming The total composite product and service characteristics of marketing, engineering, manufacture and maintenance through which the product and service in use will meet the expectation by the customer Feigenbaum Conformance to requirements - Crosby 152

153 Customer-Driven Quality Meeting or exceeding customer expectations Customers can be... External customers Internal customers 8.2 Nature of Quality Dimensions of Quality Determinants of Quality Costs of Quality 153

154 Dimensions of Product Quality 1. Performance A product s primary operating characteristics 2. Features Special characteristics that appeal to customers 3. Reliability The probability of a product s surviving over a specified period under stated conditions of use 4. Serviceability The speed, courtesy, cost and convenience of repairs and maintenance 5. Durability The amount of use one gets from a product before it physically deteriorates or until replacement is preferable 6. Aesthetics How a product looks, feels, sounds, tastes, or smells 7. Conformance The degree to which physical and performance characteristics of a product match pre-established standards 8. Safety To both users and the environment Dimensions of Service Quality 1. Reliability The ability to provide what was promised, dependably and accurately 2. Assurance The knowledge and courtesy of employees and their ability to convey trust and confidence 3. Tangibles The physical facilities and equipment, and appearance of personnel 4. Empathy The degree of caring and individual attention provided to customers 5. Responsiveness The willingness to help customers and provide prompt service 154

155 Determinants of Quality Quality of design products/service designed based on customers expectations and desires Quality capability of production processes processes must be capable of producing the products designed for the customers Quality of conformance capable processes can produce inferior product if not operated properly Quality of customer service a superior product does not mean success; must have quality service also Organization quality culture superior product and service requires organization-wide focus on quality Costs of Quality Cost of Quality (COQ) provides a basis for identifying improvement opportunities and success of quality improvement programs A quality cost is considered to be any cost that the company or organization would not have incurred if the quality of the product or service were perfect Cost of Quality (COQ) is the cost of avoiding poor quality, or incurred as a result of poor quality 155

156 Cost of Quality Categories Prevention Costs: investments made to keep nonconforming products from occurring and reaching the customer Appraisal Costs: associated with efforts to ensure conformance to requirements, generally through measurement and analysis of data to detect nonconformances Internal Failure Costs: incurred as a result of unsatisfactory quality found before product delivery to the customer External Failure Costs: occur after poor-quality products reach the customer Costs of Quality Examples Prevention costs: training, redesigns, inspection procedures, supplier quality surveys, etc. Appraisal costs: tests and inspections, process measurement and control, etc. Internal failure costs: scrap and rework, costs of correcting errors before they reach the customer, and downgrading costs. External failure costs: warranty repairs, replacements, complaints, legal expenses, lost business and goodwill. 156

157 Contrasting Traditional and Modern Approaches to Quality Traditional Passive (Inspect) Find it and fix it Emphasize on Acceptable Quality Level It is a job of Quality Control department Quantity is top priority Quality is expensive Modern Active (Prevent) Build Quality into the product Focus on Process Control and Continuous Improvement It is a company-wide concern Quality is top priority Higher Quality means lower cost 8.3 Quality Advocates Edwards W. Deming Joseph M. Juran Philip B. Crosby Armand V. Feigenbaum Kaoru Ishikawa 157

158 Edwards W. Deming Assisted Japan in improving productivity and quality after World War II In 1951 Japan established Deming Prize US was slow in recognizing his contributions Introduced Japanese companies to the Plan- Do-Check-Act (PDCA) cycle Developed 14 Points for managers PDCA Cycle 4. ACT Permanently implement improvements 1. PLAN Identify Improvement And develop plan 3. CHECK Evaluate plan to see if it works 2. DO Try plan on a test basis 158