Production Management


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1 Production Management ChwenTzeng Su, PhD Professor, Department of Industrial Management National Yunlin University of Science & Technology Touliu, Yunlin, Taiwan 640, R.O.C. 1
2 Production/Manufacturing Production/manufacturing is the process of converting raw materials or semifinished products into finished products that have value in the market place. This process involves the contribution of labor, equipment, energy, and information. 2
3 The Production System Raw materials Energy Labor Equipment Information Production System Finished products Scrap Waste 3
4 Production Management Production management is focus on managing production operations and resources throughout the production system. 4
5 Example Performance Measures Cost (are products being created at minimum or acceptable cost?) Quality (what are the specifications of the products? What percentages of shipped products meet specification?) Variety (how many types of products are  or can be simultaneously produced?) Service (how long does it take to fulfill a customer order? how often are quoted lead times met?) Flexibility (how quickly can existing resources be reconfigured to produce new products?) 5
6 Process capabilities & business strategy Example product attributes: price, quality, variety, service, demand uncertainty Example process attributes: cost, quality, flexibility, lead time 6
7 ValueAdded/Production Control The difference between the cost of inputs and the value or price of outputs. Inputs Land Labor Capital Value added Transformation Conversion process Feedback Outputs Goods Services Feedback Control Feedback 7
8 Example Decisions What should we produce, how much, and when (forecasting)? How much can we produce (capacity planning)? How much do we have and how much do we need (inventory management)? When should we produce (production planning and scheduling)? 8
9 Cycle of Production System Forecast Demand Aggregate Production Plan Orders Resource Capacity Planning Inventory Record Bills of Materials Purchase Orders Master Production Schedule Material Requirements Plan Manufacturing Ordered Scheduling Roughcut Capacity Planning Capacity Requirements Planning 9
10 Forecasts A statement about the future Used to help managers Plan the system 10
11 Uses of Forecasts Accounting Finance Human Resources Marketing MIS Operations Product/service design Cost/profit estimates Cash flow and funding Hiring/recruiting/training Pricing, promotion, strategy IT/IS systems, services Schedules, MRP, workloads New products and services 11
12 Assumes causal system past ==> future Forecasts rarely perfect because of randomness Forecasts more accurate for groups vs. individuals Forecast accuracy decreases as time horizon increases I see that you will get an A this semester. 12
13 Steps in the Forecasting Process The forecast Step 6 Monitor the forecast Step 5 Prepare the forecast Step 4 Gather and analyze data Step 3 Select a forecasting technique Step 2 Establish a time horizon Step 1 Determine purpose of forecast 13
14 Types of Forecasts Judgmental  uses subjective inputs Time series  uses historical data assuming the future will be like the past Associative models  uses explanatory variables to predict the future 14
15 Forecasting Approaches Qualitative Methods Used when situation is vague & little data exist New products New technology Involves intuition, experience e.g., forecasting sales on Internet Quantitative Methods Used when situation is stable & historical data exist Existing products Current technology Involves mathematical techniques e.g., forecasting sales of color televisions 15
16 Judgmental Forecasts Executive opinions Sales force composite Consumer surveys Outside opinion Opinions of managers and staff Delphi method 16
17 Overview of Quantitative Approaches Naïve approach Moving averages Exponential smoothing Trend projection Timeseries Models Linear regression Causal models 17
18 Quantitative Forecasting Methods (NonNaive) Naive) Quantitative Forecasting Time Series Models Causal Models Moving Average Exponential Smoothing Trend Projection Linear Regression 18
19 Example of forecasting Forecasting using linear trend. Demonstration 1. Calculating correlation: 0, Week Number of library visitors Signifficant correlation Plotting a chart (XY scatter) Adding a linear trend line Options: display equation Calculating the forecast (by inserting number of the week x=6 into the equation) 6 5. Evaluation (using RSQ) 0,99 Very good fitting Number of library visitors Visitors y = 947,1x + 269,9 R 2 = 0, Week Forecasted number of visitors:
20 What is a Time Series? Set of evenly spaced numerical data Obtained by observing response variable at regular time periods Forecast based only on past values Assumes that factors influencing past, present, & future will continue Example Year: Sales:
21 Patterns of the timeseries data A forecasting method should comply with the data pattern. There are 4 basic data patterns: Horizontal (random, irregular variation) Trend (linear) Periodical (cyclical, seasonal) Complex (a combination of part or all listed above) 21
22 Forecast Variations Irregular variation Trend Cycles Seasonal variations
23 Trend Component Persistent, overall upward or downward pattern Due to population, technology etc. Several years duration Response Mo., Qtr., Yr T/Maker Co. 23
24 Cyclical Component Repeating up & down movements Due to interactions of factors influencing economy Usually 210 years duration Response Cycle B Mo., Qtr., Yr. 24
25 Seasonal Component Regular pattern of up & down fluctuations Due to weather, customs etc. Occurs within 1 year Response Summer T/Maker Co. Mo., Qtr. 25
26 Random Component Erratic, unsystematic, residual fluctuations Due to random variation or unforeseen events Union strike Tornado Short duration & nonrepeating T/Maker Co. 26
27 Components of an observation Observed demand (O) = Systematic component (S) + Random component (R) Level (current deseasonalized demand) Trend (growth or decline in demand) Seasonality (predictable seasonal fluctuation) 27
28 General Time Series Models Any observed value in a time series is the product (or sum) of time series components Multiplicative model Y i = T i S i C i R i Additive model (if quarterly or mo. data) Y i = T i + S i + C i + R i (if quarterly or mo. data) 28
29 Techniques for Averaging Moving average Weighted moving average Exponential smoothing 29
30 Main idea of the method The moving average uses the average of a given number of the most recent periods' value to forecast the value for the next period. Moving average smoothes down the fluctuations in the data 30
31 Smoothing Smoothing the data variation: a graphical presentation Data Smoothed data Moore smoothed data 31
32 A formula for the Moving Average If forecast for t period is denoted by F t, and the actual value of the timeseries was A t1 during period t1, A t2 during period t2, etc., then n period simple moving average is expressed as: F t =(A t1 +A t A tn )/n 32
33 Calculation of a moving average: an Example Month Number of clients Moving average (2) January February March April May (forecast)
34 Choosing the averaging period The averaging period (value of n) must be determined by the decisionmaker. It is important to try to select the best period to use for the moving average. As a general rule, the number of periods used should relate to the amount of random variability in the data. Specifically, the bigger the moving average period, the greater the random elements are smoothed. 34
35 Evaluation of MA Advantage: very simple method. Shortcomings: The new and the old data are treated in the same way (while, in fact, the old dat should be treated as less being signifficant). (Method of exponential smoothing does not have this shortcoming). 35
36 Modification of Moving Average method Weighted Moving Average: Simple moving average technique assigns equal weights to each period of historical data; the weighted moving average technique assigns different weights to historical data allowing the model to respond quickly to any shift in the series being studied. 36
37 Weighted Moving Average Method Used when trend is present Older data usually less important Weights based on intuition Often lay between 0 & 1, & sum to 1.0 Equation WMA?? (Weight for period n) (Demand in period n)? Weights 37
38 Disadvantages of Moving Average Method Increasing n makes forecast less sensitive to changes Do not forecast trend well Require much historical data T/Maker Co. 38
39 Exponential Smoothing F t = F t1 +? (A t1  F t1 ) Main idea of the method: smoothing down variations in the data. Forecast error for the previous period is taken into account. PremiseThe most recent observations might have the highest predictive value. Therefore, we should give more weight to the more recent time periods when forecasting. 39
40 Exponential Smoothing Method Form of weighted moving average Weights decline exponentially Most recent data weighted most Requires smoothing constant (? ) Ranges from 0 to 1 Subjectively chosen Involves little record keeping of past data 40
41 Exponential Smoothing Equations F t =? A t  1 +? (1? ) A t  2 +? (1? ) 2 A t  3 +? (1? ) 3 A t (1? ) t1 A 0 F t A t? = Forecast value = Actual value = Smoothing constant F t = F t1 +? (A t1  F t1 ) Use for computing forecast 41
42 Exponential Smoothing Example You re organizing a Kwanza meeting. You want to forecast attendance for 1998 using exponential smoothing (? =.10). The 1993 forecast was Corel Corp. 42
43 Exponential Smoothing Solution F t = F t1 + a (A t1  F t1 ) Time Actual Forecast, F t (a =.10) (Given) NA
44 Exponential Smoothing Solution Time Actual F t = F t1 + a (A t1  F t1 ) Forecast, F t ( a =.10) (Given) ( NA 44
45 Exponential Smoothing Solution Time Actual F t = F t1 + a (A t1  F t1 ) Forecast, Ft (a =.10) (Given) ( NA 45
46 Exponential Smoothing Solution F t = F t1 + a (A t1  F t1 ) Time Actual Forecast, F t ( a =.10) (Given) ( ) NA 46
47 Exponential Smoothing Solution Time Actual F t = F t1 + a (A t1  F t1 ) Forecast, F t (= =.10) (Given) ( ) = NA 47
48 Exponential Smoothing Solution Time Actual F t = F t1 + a (A t1  F t1 ) Forecast, F t (a =.10) (Given) ( ) = ( ) ) = NA 48
49 Exponential Smoothing Solution Time Actual F t = F t1 + a (A t1  F t1 ) Forecast, F t (a =.10) (Given) ( ) = ( ) = NA ( ) ) =
50 Exponential Smoothing Solution F t = F t1 + a (A t1  F t1 ) Time Actual Forecast, F t (a =.10) (Given) ( ) = ( ) = ( ) = ( ) ) = NA 50
51 Exponential Smoothing Solution Time Actual F t = F t1 + a (A t1  F t1 ) Forecast, F t (a =.10) (Given) ( ) = ( ) = ( ) = ( ) = NA ( ) ) =
52 Exponential Smoothing Graph Sales Actual Forecast Year 52
53 Forecast Effects of Smoothing Constant? F t = a A t a (1a) A t a (1 a) 2 A t Weight a is... Prior Period 2 Periods Ago Periods Ago a a (1 a ) a (1 a ) 2 53
54 Forecast Effects of Smoothing Constant? F t = a A t a (1a) A t a (1 a) 2 A t Weight a is... Prior Period 2 Periods Ago % Periods Ago a a (1 a ) a (1 a ) 2 54
55 Forecast Effects of Smoothing Constant? F t = a A t a (1a) A t a (1 a) 2 A t Weight a is... Prior Period 2 Periods Ago 3 Periods Ago a a (1 a ) a (1 a ) % 9%
56 Forecast Effects of Smoothing Constant? F t = a A t a (1a) A t a (1 a) 2 A t Weight a is... Prior Period 2 Periods Ago 3 Periods Ago a a (1 a ) a (1 a ) % 9% 8.1%
57 Forecast Effects of Smoothing Constant? F t = a A t a (1a) A t a (1 a) 2 A t Weight a is... Prior Period 2 Periods Ago 3 Periods Ago a a (1 a ) a (1 a ) % 9% 8.1% % 57
58 Forecast Effects of Smoothing Constant? F t = a A t a (1a) A t a (1 a) 2 A t Weight a is... Prior Period 2 Periods Ago 3 Periods Ago a a (1 a ) a (1 a ) % 9% 8.1% % 9% 58
59 Forecast Effects of Smoothing Constant? F t = a A t a (1a) A t a (1 a) 2 A t Weight a is... Prior Period 2 Periods Ago 3 Periods Ago a a (1 a ) a (1 a ) % 9% 8.1% % 9% 0.9% 59
60 Forecast Effects of Smoothing Constant? F t = a A t a (1a) A t a (1 a) 2 A t Weight a is... Prior Period 2 Periods Ago 3 Periods Ago a a (1 a ) a (1 a ) % 9% 8.1% % 9% 0.9% 60
61 Example of Exponential Smoothing Period Actual Alpha = 0.1 Error Alpha = 0.4 Error
62 Picking a Smoothing Constant Demand Period Actual?
63 Choosing? Seek to minimize the Mean Absolute Deviation (MAD) If: Forecast error = demand  forecast Then: MAD?? forecast n errors 63
64 Main idea of the trend analysis forecasting method Main idea of the method: a forecast is calculated by inserting a time value into the regression equation. The regression equation is determined from the timeserieas data using the least squares method 64
65 Prerequisites: 1. Data pattern: Trend Trend (close to the linear growth) 65
66 The trend line is the bestfit line: an example Y Yi = a + b Xi + Error Error Observed value ^i Regression line Y = a + b X i X 66
67 Linear Regression Model Shows linear relationship between dependent & explanatory variables Example: Sales & advertising (not time) Yintercept ^ Y i = a + Slope b X i Dependent (response) variable Independent (explanatory) variable 67
68 Linear Regression Equations Equation: Ŷ? a? i bx i Slope: b? n? i? 1 n? i? 1 x x i 2 i y i?? n x y n? x? 2 YIntercept: a? y? bx 68
69 Statistical measures of goodness of fit In trend analysis the following measures will be used: The Correlation Coefficient The Determination Coefficient 69
70 Prerequisites: 2. CorrelationC There should be a sufficient correlation between the time parameter and the values of the timeseries data 70
71 The Correlation Coefficient The correlation coefficient, R, measure the strength and direction of linear relationships between two variables. It has a value between 1 and +1 A correlation near zero indicates little linear relationship, and a correlation near one indicates a strong linear relationship between the two variables 71
72 Coefficient of Correlation and Regression Model Y Y r = 1 r = 1 Y^ i = a + b X i X r =.89 r = 0 Y Y ^ Y i = a + b X i X Y^ i = a + b X i X Y^ i = a + b X i X 72
73 73 Sample Coefficient of Correlation Sample Coefficient of Correlation?????????????????????????????????????????????? n i n i i i n i n i i i n i n i n i i i i i y y n x x n y x x y n r
74 Nonlinear trends Excel provides easy calculation of the following trends Logarythmic Polynomial Power Exponential 74
75 Logarithmic trend y = 4,6613Ln(x) + 1,0724 R 2 = 0,
76 Trend (power) y = 0,4826x 1,5097 R 2 = 0,
77 Trend (exponential) y = 0,0509e 1,0055x R 2 = 0,
78 Trend (polynomial) y = 0,1142x 3 + 1,6316x 25,9775x + 7,7564 R 2 = 0,
79 Choosing the trend that fitts best 1) Roughly: Visually, comparing the data pattern to the one of the 5 trends (linear, logarythmic, polynomial, power, exponential) 2) In a detailed way: By means of the determination coefficient 79
80 Guidelines for Selecting Forecasting Model You want to achieve: No pattern or direction in forecast error Error = (Y i  Y i ) = (Actual  Forecast) ^ Seen in plots of errors over time Smallest forecast error Mean square error (MSE) Mean absolute deviation (MAD) 80
81 Pattern of Forecast Error Trend Not Fully Accounted for Desired Pattern Error Error 0 0 Time (Years) Time (Years) 81
82 Forecast Error Equations Mean Square Error (MSE) MSE n? 2 ( yi? yˆ i ) i?? (forecast errors) 1?? n n Mean Absolute Deviation (MAD) MAD n?? i?? y i n? ŷ i?? forecast n errors 82
83 Tracking Signal Plot TS Time 83
84 Selecting Forecasting Model Example You re a marketing analyst for Hasbro Toys. You ve forecast sales with a linear model & exponential smoothing. Which model do you use? Actual Linear Model Exponential Year Sales Forecast Smoothing Forecast (.9)
85 Linear Model Evaluation Year Y i ^ Y i Error Error 2 Error Total
86 Linear Model Evaluation Year Y i ^ Y i Error Error 2 Error Total MSE = å Error 2 / n = 1.10 / 5 =.220 MAD = å Error / n = 2.0 / 5 =
87 Exponential Smoothing Model Evaluation ^ Year Y i Y i Error Error 2 Error Total MSE = å Error 2 / n = 0.05 / 5 = 0.01 MAD = å Error / n = 0.3 / 5 =
88 Exponential Smoothing 88
89 Linear Trend Equation 89
90 Trend Adjusted Exponential Smoothing 90
91 Simple Linear Regression 91
92 Inventory Management Type of Inventory Raw materials & purchased parts Partially completed goods called work in progress Finishedgoods inventories (manufacturing firms) or merchandise (retail stores) 92
93 Types of Inventories (Cont d) Replacement parts, tools, & supplies Goodsintransit to warehouses or customers 93
94 Functions of Inventory To meet anticipated demand To smooth production requirements To take advantage of order cycles To help hedge against price increases or to take advantage of quantity discounts To permit operations 94
95 Inventory hides problems in a process. Water Level = Inventory Rocks = Problems in the system Boat = Company Operations
96 Inventory Management Questions What should be the order quantity (Q)? When should an order be placed, called a reorder point (ROP)? How much safety stock (SS) should be maintained?
97 Inventory Cost Holding costs  associated with holding or carrying inventory over time Ordering costs  associated with costs of placing order and receiving goods Setup costs  cost to prepare a machine or process for manufacturing an order 97
98 Inventory Models Fixed order quantity models Economic order quantity Production order quantity Quantity discount Help answer the inventory planning questions! Probabilistic models Fixed order period models T/Maker Co. 98
99 Inventory Levels For EOQ Model Units on Hand Q 0 Q D Time
100 Why Holding Costs Increase More units must be stored if more ordered Purchase Order Description Qty. Microwave 1 Order quantity Purchase Order Description Qty. Microwave 1000 Order quantity 100
101 Why Order Costs Decrease Cost is spread over more units Example: You need 1000 microwave ovens 1 Order (Postage $ 0.32) 1000 Orders (Postage $320) Purchase Order Description Qty. Microwave 1000 Order quantity Purchase Purchase Order Order Description Purchase Description Purchase Order Order Qty. Qty. Description Microwave MicrowaveQty. Qty. 1 1 Microwave 1 Description Microwave 1 101
102 EOQ Model How Much to Order? Annual Cost Total Cost Curve Holding Cost Curve Order (Setup) Cost Curve Optimal Order Quantity (Q*) Order Quantity 102
103 Deriving an EOQ Develop an expression for setup or ordering costs Develop an expression for holding cost Set setup cost equal to holding cost Solve the resulting equation for the best order quantity 103
104 Total Cost Annual Total cost = carrying + cost Annual ordering cost Q 2 H D TC = + Q S 104
105 Annual Order Cost $ U? Q C O Q 105
106 Annual Holding Cost $ Q? 2 C H Q 106
107 Graph of Annual Inventory Costs 107
108 Deriving the EOQ Using calculus, we take the derivative of the total cost function and set the derivative (slope) equal to zero and solve for Q. Q = OPT 2DS H = 2(Annual Demand )(Order or Setup Cost ) Annual Holding Cost 108
109 The Inventory Cycle Q Usage rate Profile of Inventory Level Over Time Quantity on hand Reorder point Receive order Place order Receive order Place order Receive order Time Lead time 109
110 EOQ Example Given: 25,000 annual demand $3 per unit per year holding cost $100 ordering costs EOQ = 2(25,000)(100) 3?
111 EOQ Model Equations Optimal Order Quantity Expected Number of Orders = Q* = = N = 2 D S H D Expected Time Between Orders D d = Working Days / Year ROP = d L Q * = T = Working Days N D = Demand per year S = Setup (order) cost per order H = Holding (carrying) cost d = Demand per day L = Lead time in days / Year 111
112 Production Order Quantity Model Answers how much to order and when to order Allows partial receipt of material Other EOQ assumptions apply Suited for production environment Material produced, used immediately Provides production lot size Lower holding cost than EOQ model 112
113 POQ Model Inventory Levels Inventory Level Production portion of cycle Demand portion of cycle with no supply Supply Begins Supply Ends Time 113
114 POQ Model Equations Optimal Order Quantity = Q = p * 2* D* S? d? H *1? p?? Max. Inventory Level Setup Cost Holding Cost D = * Q = S = Q * 0.5 * H * Q? 1 d??? p?? 1 d??? p? D = Demand per year S = Setup cost H = Holding cost d = Demand per day p = Production per day 114
115 When to Reorder with EOQ Ordering Reorder Point  When the quantity on hand of an item drops to this amount, the item is reordered Safety Stock  Stock that is held in excess of expected demand due to variable demand rate and/or lead time. Service Level  Probability that demand will not exceed supply during lead time. 115
116 Safety Stock Quantity Maximum probable demand during lead time Expected demand during lead time ROP LT Safety stock Time 116
117 Demand During Lead Time Example? L? 3?? 15.?? 15.?? 15.?? = u=3 u=3 u=3 u=3 Four Days Lead Time? d L?12 ROP s s Demand During Lead time
118 Reorder Point Service level Probability of no stockout ROP Expected demand Safety stock 0 z Risk of a stockout Quantity zscale 118
119 Safety Stock (SS) Demand During Lead Time (LT) has Normal Distribution with  Mean( d L )?? ( LT)  Std. Dev.(? )?? SS with r% service level Reorder Point L SS? zr? LT ROP? SS? d L LT
120 FixedPeriod Model Answers how much to order Orders placed at fixed intervals Inventory brought up to target amount Amount ordered varies No continuous inventory count Possibility of stockoutbetween intervals Useful when vendors visit routinely Example: P&G rep. calls every 2 weeks 120
121 FixedPeriod Model: When to Order? Inventory Level Target maximum Period Time 121
122 FixedPeriod Model: When to Order? Inventory Level Target maximum Period Period Time 122
123 FixedPeriod Model: When to Order? Inventory Level Target maximum Period Period Time 123
124 FixedPeriod Model: When to Order? Inventory Level Target maximum Period Period Period Time 124
125 FixedPeriod Model: When to Order? Inventory Level Target maximum Period Period Period Time 125
126 FixedPeriod Model: When to Order? Inventory Level Target maximum Period Period Period Time 126
127 Material Requirements Planning (MRP) Manufacturing computer information system Determines quantity & timing of dependent demand items Gross Requirements Scheduled Receipts 5 30 Available Net Requirements 7 Planned Order Receipts 7 Planned Order Releases Corel Corp. 127
128 Collins Industries Largest manufacturer of ambulances in the world International competitor 12 major ambulance designs 18,000 different inventory items 6,000 manufactured parts 12,000 purchased parts MRP: IBM s MAPICS 128
129 Collins Industries Collins requires: Material plan must meet both the requirements of the master schedule and the capabilities of the production facility Plan must be executed as designed Effective timephased deliveries, consignments, and constant review of purchase methods Maintenance of record integrity 129
130 Purposes, Objectives and Philosophy of MRP Theme of MRP getting the right materials to the right place at the right time. Objectives of MRP Improve customer service, minimize inventory investment, and maximize production operating efficiency Philosophy of MRP Materials should be expedited if needed to keep the MPS on target and deexpedited when we are behind schedule and don t need the materials. 130
131 MRP and The Production Planning Process Forecast & Firm Orders Aggregate Production Planning Resource Availability Material Requirements Planning Master Production Scheduling Capacity Requirements Planning No, modify CRP, MRP, or MPS Realistic? Yes Shop Floor Schedules 131
132 Inputs to the Standard MRP System Bill of Materials File Actual Customer Orders Aggregate Product Plan Master Production Schedule MRP Program Forecasted Demand Inventory Records File 132
133 Structure of the MRP System BOM (BillofMaterial) Master Production Schedule MRP by period report Lead Times (Item Master File) Inventory Data MRP Programs MRP by date report Planned orders report Purchase requirements Purchasing data Exception reports 133
134 Master Production Schedule Shows items to be produced End item, customer order, module Derived from aggregate plan Example Item/Week Oct 3 Oct 10 Oct 17 Oct 24 Drills Saws
135 Billof ofmaterial List of components & quantities needed to make product Provides product structure (tree) Parents: Items above given level Children: Items below given level Shows lowlevel coding Lowest level in structure item occurs Top level is 0; next level is 1 etc. 135
136 Billof ofmaterial Product Structure Tree Bicycle(1) P/N 1000 Handle Bars (1) P/N 1001 Frame Assy (1) P/N 1002 Wheels (2) P/N 1003 Frame (1) P/N
137 TimePhased Product Structure Start production of D 2 weeks 1 week G D 2 weeks 1 week D 2 weeks to produce 3 weeks E 2 weeks Must have D and E completed here so production can begin on B E 1 week F B A 1 week C
138 MRP Example Shutter Mfg. Company Master Production Schedule Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Week 8 Shutters Bill of Materials (Product Structure Tree) Lead Time = 2 weeks Frames (2) Shutter Lead Time = 1 week Wood Sections(4) Lead Time = 1 week 138
139 MRP Plan Item Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Week 8 Shutters (Lead Time = 1) Gross Requirements Scheduled Receipts Onhand Net Requirements Planned Order Receipt Planned Order Release Frames (Lead Time = 2) Gross Requirements Scheduled Receipts Onhand Net Requirements Planned Order Receipt Planned Order Release Wood Sections(Lead Time = 1) Gross Requirements Scheduled Receipts 70 Onhand Net Requirements Planned Order Receipt Planned Order Release
140 MRP Benefits Increased customer satisfaction due to meeting delivery schedules Faster response to market changes Improved labor & equipment utilization Better inventory planning & scheduling Reduced inventory levels without reduced customer service 140
141 MRP Requirements Computer system Mainly discrete products Accurate billofmaterial Accurate inventory status 99% inventory accuracy Stable lead times T/Maker Co. 141
142 MRP Planning Develop a tentative master production schedule Use MRP to simulate material requirements Convert material requirements to resource requirements Revise tentative master production schedule Is shop capacity adequate? Yes Firm up a portion of the MPS No Can capacity be changed to meet requirements Change capacity No Yes 142
143 Toatal Processing Time/Week Total Processing Time Processing Time/ piece Lead Time Amount to Produce Order Number Week
144 /Week Capacity Capacity Loading Chart 144
145 MRP II Expanded MRP with and emphasis placed on integration Financial planning Marketing Engineering Purchasing Manufacturing 145
146 MRP Core Inside ERP Engineering CAD/ Configurators Bills of Material Demand Management SFA & CRM Routings Global Strategy Business Plan Sales & Operations Plan MPS Detailed Material & Capacity Planning Plant & Supplier Communication Schedule Execution Finance & Accounting Treasury & Activity Costing RoughCut Capacity Plan Inventory Work Centers 146
147 What is Scheduling? Scheduling deals with the allocation of scarce resources to tasks over time. It is a decisionmaking process with the goal of optimizing one or more objectives. Consists of planning and prioritizing activities that need to be performed in an orderly sequence of operation. Scheduling leads to increased efficiency and capacity utilization, reducing time required to complete jobs and consequently increasing the profitability of an organization. Resource scheduling, such as machines, labor, and material. 147
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