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SUPPLY CHAIN ENGINEERING…MN 799 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
GUIDELINES ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
DEFINITION OF A SUPPLY CHAIN ,[object Object],[object Object],[object Object],[object Object],[object Object]
Traditional View: Logistics in the Economy ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Source: Cass Logistics Homework: What are 2007 statistics?
Traditional View: Logistics in the Manufacturing Firm ,[object Object],[object Object],[object Object],[object Object],Homework: What it the profile for Consumables; Pharamas and Computers Profit Logistics  Cost Marketing  Cost Manufacturing  Cost
Supply Chain Management: The Magnitude in the Traditional View ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
HAMBURGERS AND FRIES ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
What problems do you foresee in this Supply Chain? Please write some down  Burger and Fries Examine this process – What do you observe?
Understanding the Supply Chain   … a chain is only as good as its weakest link      Recall that saying? The saying applies to the principles of building a competitive infrastructure: Manufacturer Wholesaler Retailer Customer Supplier … there is a limit to the surplus or profit in a supply chain We are all part of a Supply Chain in everything we buy Strong, well-structured  supply chains are  critical  to sustained competitive advantage.
OBJECTIVES OF A SUPPLY CHAIN ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],UNDERSTAND EACH OBJECTIVE
DECISION PHASES IN A SUPPLY CHAIN ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Basic Supply Chain Architectures  ( Examples ) 1. Indirect Channel 2. Direct Channel 3. Virtual Channel Supplier Supplier Supplier Supplier Supplier Supplier Supplier Supplier Customer Customer Customer Customer Customer Factory Factory Factory Wholesale Wholesale Integrator Express Freight Retailer Retailer Retailer Virtual Store Fabricator Fabricator Credit Service C  1999.  William T. Walker, CFPIM, CIRM with the APICS Educational & Research Foundation.  All Rights Reserved.
SOLE SOURCE SINGLE SOURCE MULTI-SOURCE INDIRECT CHANNEL DIRECT CHANNEL VIRTUAL CHANNEL MAKE vs. BUY Supply Demand Supply Chain Architecture ,[object Object],[object Object],[object Object],[object Object],[object Object],LOCAL  REGIONAL  GLOBAL MARKET  MARKET  MARKET C  1999.  William T. Walker, CFPIM, CIRM with the APICS Educational & Research Foundation.  All Rights Reserved.
SUPPLY CHAIN FRAMEWORK AND INFRASTRUCTURE  ,[object Object],[object Object],[object Object],[object Object]
Cycle View of Supply Chains   DEFINES ROLES AND RESPONSIBILITIES OF MEMBERS OF SUPPLY CHAIN Customer Order Cycle Replenishment Cycle Manufacturing Cycle Procurement Cycle Customer Retailer Distributor Manufacturer Supplier to to to to
PROCESS VIEW OF A SUPPLY CHAIN ,[object Object],[object Object],[object Object],[object Object],[object Object]
PROCESS VIEW OF A SUPPLY CHAIN ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
PROCESS VIEW OF A SUPPLY CHAIN ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
A Customer’s View of the Supply Chain  Order the product... with configuration complexity on-line Pay for the product... in a foreign currency by credit card Service the product... anywhere in the world Take delivery... the next day at home, and get started without a hassle Ex.-Travel arrangements on line FRONT OFFICE C  1999.  William T. Walker, CFPIM, CIRM with the APICS Educational & Research Foundation.  All Rights Reserved.
Push/Pull View of Supply Chains   PULL – PROCESSES IN RESPONSE TO A CUSTOMER ORDER PUSH – PROCESSES IN ANTICIPATION OF A CUSTOMER ORDER   Procurement, Manufacturing and Replenishment cycles Customer Order Cycle Customer Order arrives PUSH PROCESSES PULL PROCESSES
UNDERSTANDING THE SUPPLY CHAIN ,[object Object],[object Object],[object Object],[object Object],[object Object]
SUPPLY CHAIN PERFORMANCE – STRATEGIC FIT AND SCOPE  ( Lesson 2) New Product Development Marketing and Sales Operations Distribution Service Finance, Accounting, Information Technology, Human Resources Business Strategy New Product Strategy Marketing Strategy Supply Chain Strategy Supply and  Manufacture FILM  – CHAIN REACTION EXAMPLES?
ACHIEVING STRATEGIC FIT ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Implied Demand  Uncertainty See Table 2.1 Regular Demand Uncertainty  due to customers   demand  and  Implied Demand Uncertainty  due to uncertainty in  Supply Chain
Levels of Implied Demand Uncertainty Low High Price Responsiveness Customer Need Implied Demand Uncertainty Attributes (Table 2-2) Low Implied Uncertainty  High Implied Uncertainty Product Margin  Low –  High  Aver. Forecast Error  10%  40-100%;  Aver. Stockout rate  1-2%  10-40%;  Aver. markdown  0%  10-25% Detergent Long lead time steel High Fashion Emergency steel
SUPPLY SOURCE UNCERTAINTY ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Step 2 - Understanding the Supply Chain:  Cost-Responsiveness Efficient Frontier (Table: 2.4) High Low  Low High Exercise:  Give examples of products that are:  Highly efficient, Somewhat efficient, Somewhat responsive and highly responsive Cost (efficient) Responsiveness Responsiveness – to  Quantity, Time, Variety, Innovation, Service level Fig 2.3
Step 3. Achieving Strategic Fit Low Cost High Cost Companies try to move  Zone of Strategic fit Implied uncertainty spectrum Responsive supply chain Efficient supply chain Certain demand Uncertain demand Responsiveness spectrum Zone of Strategic Fit
SCOPE ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Strategic Scope Suppliers Manufacturer Distributor Retailer Customer Competitive Strategy Product Dev. Strategy Supply Chain Strategy Marketing Strategy
Drivers of Supply Chain Performance  TRADE OFF FOR EACH DRIVER Competitive Strategy Supply Chain Strategy Efficiency Responsiveness Inventory Transportation Facilities Information Supply chain structure Drivers
INVENTORY ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
TRANSPORTATION ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
FACILITIES ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
INFORMATION ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Considerations for Supply Chain Drivers
MAJOR OBSTACLES TO ACHIEVING FIT ,[object Object],[object Object],[object Object],Increasing demand and supply uncertainty Local optimization and lack of global fit
OBSTACLES TO ACHIEVING STRATEGIC FIT ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Dealing with Product Variety: Mass Customization Mass Customization Low High High Low Long Short Lead Time Cost Customization
Fragmentation of Markets and Product Variety ,[object Object],[object Object],[object Object],[object Object]
HOMEWORK ,[object Object],[object Object],[object Object],[object Object],[object Object]
REVIEW QUESTIONS ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Forecasting (uncertainty) Order service (certainty) Demand management Demand-Management Activities RULE: Do not forecast what you can plan, calculate, or extract from supply chain feedback. Source: Adapted from Plossl, “Getting the Most from Forecasts,”  APICS 15th International Conference Proceedings , 1972 Lesson 3
DETERMINING DEMAND ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
FORECASTING ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
FORECASTING ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
FORECASTING ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
WORK OUT JUNE’s FORECASTS FOR ALL SKU’s
Simple Moving Averages (SMA) Simple Moving Average (SMA) Where  F = Forecast T = Current time period D = Demand n = Number of periods( max) Forecast Forecast Demand (3-period   4-period   start-up start-up             Exercise: Work out the SMA for two periods Question: What determines the number of periods used? Why? n D D D F 2       - - + + + =
Weighted Moving Averages Weighted Moving Average (WMA) Where:  F = Forecast T = Current time period D = Demand n = Number of periods (max) W = Weight, where greatest weight   applies to most recent period and sum of weights = 1 Forecast Forecast Demand    start-up start-up             Exercise: Work out forecast for two periods with weights of 0.4,0.6 What periods and weights will use for forecasting soap and fashion clothes Why?
Exponential Smoothing   Decision þ  Select or compute a smoothing constant (  ) þ  Relationship of exponential smoothing to simple moving average Where n = number of past periods to be captured Where F = forecast value T = current time period D = demand    = exponential factor <1 Formulas
Period Demand  Forecast  Forecast Forecast     (   = .1) (  = .5) (  = .9) 0 180   start-up  start-up start-up 1 160   180   180   180 2 220   178   170   162 3 200  182     195   214 4 260   184   198   201 5 240   192   229   254 6      196   234   241 Exponential Smoothing — Continued F T+1  = F T  + a (D T  – F T ) Work out forecasts with   =0.3 What   ’s will use for forecasting soap and fashion clothes Why?
Simple Trended Series — Example    Algebraic Trend Projection X   Y a.  Trend (“rise” over “run”) = (13 - 4)/3 = 3 = b  0  4 1   7 2 10 3 13 c.  Period 4: Y = a + bX = 4 + 3 (4 [for period 4]) = 16 b. Y-intercept (a) = “compute” the Y value for X = 0, thus Y-int = 4 1  2  3 13 10 7 4 Run Rise
REGRESSION ANALYSIS ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
TRENDED TIME SERIES FORECASTING ,[object Object],[object Object],[object Object],[object Object],[object Object]
Seasonal Series Indexing Seasonal Month Year 1 Year 2 Year 3 Total Index Jan 10 12 11 33 0.33 Feb 13 13 11 37 0.37 Mar 33 38 29 100 1.00 Apr 45 54 47 146 1.46 May 53 56 55 164 1.64 Jun 57 56 55 168 1.68 Jul 33 27 34 94 0.94 Aug 20 18 19 57 0.57 Sep 19 22 20 61 0.61 Oct 18 18 15 51 0.51 Nov 46 50 45 141 1.41 Dec 48 53 47 148 1.48 Total 395 417 388 1200 12.00 Yr 1  Yr2
Seasonal Series Indexing  Sample Data —  Continued ,[object Object],[object Object],[object Object],[object Object],  Monthly Total (MT) Formula: Seasonal Index (SI) = Average Month (AM)   33 SI JAN = = .33 100   94 SI JUL =   = .94 100 Where: 1200 AM  = = 100 12
   Given       Deseasonalized  Seasonal   Demand   Forecast   Index July   34   36   0.94 Aug   0.57    Rationale and Computations 1. Deseasonalize current (July) actual demand 2. Use exponential smoothing to project deseasonalized data one   period ahead (   = .2) 3. Reseasonalize forecast for desired month (August) = Deseasonalized forecast    seasonal factor = 36.03    0.57 = 20.53 or 21 36.03 (36) (0.8) (36.17) (0.2) )F (1 D F T T 1 T          Integrative Example: Calculating a Forecast  with Seasonal Indexes and Exponential Smoothing 34 0.94
Exercise ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Normal Distribution Using the Measures of Variability Source: Adapted from  CPIM Inventory Management Certification Review Course ( APICS, 1998).
Standard Deviation ( sigma) F= A = Actual Error (Sales –  Error Period Forecast Sales Forecast) Squared              –      –                     –            –      
Standard Deviation —  Continued Standard Deviation   About the use of n or  n - 1 in the above equations  n  Use with a large population (> 30 observations) n - 1  Use with a small population ( <  30 observations) Standard Deviation ( ) ( )    n F A     n F  2 i i 2 i i = = - = = = - - =  
Bias and MAD Cumulative sum of error = Bias = Mean Absolute Deviation (MAD) = ( )  n   F  i i = = -   F  n   i i = = -  F = A = Actual Error  Sales – Absolute  Period Forecast Sales Forecast) Error              –      –                     –            –      
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Measures of Forecast Error F ( )  - i i A ( ) n F A  i i  - n F  i  i  - ( )  n F    i  i - -  ( ) n F A  i i  - or
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Confidence Intervals ( ) ( ) ( ) s s s z x x or x x Deviation Standard Mean Distance z n F A OR  n F A Dev Std  i i 2 i i 2 i i + = - = - = - - - =  
z            ack Expressing z Values  (for +ve probabilities) Probabilit y D  +1 SD +2 SD +3 SD Cumulative normal distribution from left side of distribution (x + z)                                            
Application Problem — Service Level ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Homework ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Supply Chain Network Fundamentals William T. Walker, CFPIM, CIRM, CSCP Practitioner, Author, and Supply Chain Architect
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Session Outline
Learning Objectives ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
A  SUPPLY CHAIN   is  the global network  used to deliver products and services  from raw materials to end customers  through engineered flows of  information, material, and cash. Contributed to the  APICS Dictionary, 10th Edition  by William T. Walker
Network Terminology Physical Flow Info Flow Cash Flow &quot;Source&quot;  &quot;Make&quot;  &quot;Deliver&quot;  &quot;Return&quot;  Upstream  Midstream  Downstream  Reverse Stream Zone  Zone  Zone  Zone  Customer Value-Adding Value-Subtracting
Supplier Customer Trading Partner $ 3 M 1 M 2 M 3 $ 1 $ 2 Cash Material Material moves downstream to the customer. Cash moves upstream to the supplier. Supply Chain Network Operations
Suppliers Customers Trading Partner Shareholders Employees Value is the Perfect Order The Value Principle: Every stakeholder wins when throughput is maximized. Value is Employment Stability Value is Return In Investment Value is Continuity of Demand
The Network Rules ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
The Network Trust Factor ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Bill Of Materials For Example Items:  A3, B2, B5, C1, C2, C3, D1 Suppliers: S1, S2, S3, S4, S5 Item Master - Stock Keeping Unit (SKU) Number - Description - Unit Of Measure - Approved Supplier - Country Of Origin - Cost - Lead Time Product Structure - Parent To Child Relationship - Quantity Per Relationship S3 S5 S4 S2 S1 D1 A3 B5 B2 C1 C2 C3 BOM Level 0. BOM Level 1. BOM Level 2. BOM Level 3.
Supply Chain Network Map Upstream  Midstream  Downstream Driven by the Bill Of Materials  Driven by the Delivery Channel
[object Object],[object Object],[object Object],[object Object],[object Object],How To  Map A Network
The Velocity Principle: In network implementation  throughput is maximized  when order-to-delivery-to-cash velocity is maximized by minimizing process cycle time.  The 5V Principles of Supply Chain Management explain how a supply  chain network works by answering what, when, where, why, and how: Velocity –  how  are relationships connected to make the delivery?
The Network Flow Model From:  William T. Walker,  Supply Chain Architecture: A Blueprint for Networking the Flow of Material, Information, and Cash , CRC Press, ©2005. Supplier Customer Trading Partner Order-To-Delivery Order-To-Stock Invoice-To-Pay Invoice-To-Cash Material Material Cash Cash Info Info
Logistics Touches Every Subcycle ,[object Object],[object Object],[object Object],[object Object],Order-To-Delivery Order-To-Stock Invoice-To-Pay Invoice-To-Cash
Import/ Export Boundaries Country A exports and Country B imports in a forward supply chain. Country B exports and Country A imports in a reverse supply chain. Import duty and export licensing add complexity to network linkages decreasing velocity and increasing variability.  Country A  Country B  Buyer Return Seller Shipment Exports Imports Exports Imports
The Variability Principle:   In network implementation throughput is maximized  when order-to-delivery-to-cash variability is minimized by minimizing process variance. The 5V Principles of Supply Chain Management explain how a supply  chain network works by answering what, when, where, why, and how: Variability –  what  is likely to change from one delivery to the next?
Outward Signs of Variability ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
To Maximize Velocity ,[object Object],[object Object],[object Object],To Minimize Variability ,[object Object],[object Object],[object Object]
Push/Pull Boundary Pull Push Order Push/Pull  Boundary Forecast Demand Supply
Customer Lead Time Customer Demand Pull Push Order Build-To-Order (BTO) Push/Pull  Boundary Customer Demand Pull Push Build-To-Stock (BTS) Push/Pull  Boundary Order F/C F/C
[object Object],[object Object],[object Object],How To  Locate A Push/Pull Boundary
The Vocalize Principle: In network operations throughput is maximized by pulling supply to demand  by vocalizing actual demand at the network constraint. The 5V Principles of Supply Chain Management explain how a supply  chain network works by answering what, when, where, why, and how: Vocalize –  who  knows the full requirements of the order?
Common Causes of Stockouts L Quantity Time R SS L Q Quantity Time R SS L Q Quantity Time R SS Q Demand Uncertainty Supply Uncertainty Lead Time Variability (LT = Cycle Time + Transit Time)
The Planning Interface Pull To Demand Push From Forecast Sales & Operations Plan Master Schedule Downstream The Supply Chain Network Push  Zone  Pull  Zone Push/Pull Boundary I MRP Materials Requirements CRP Capacity Requirements I Upstream C C Capable Network Preload Inventory Throughput
Push Inventory And Capacity Ending Inventory = Starting Inventory  - Forecasted Demand + Production When actual demand exceeds forecasted demand, either capacity or inventory can constrain production causing lead time to expand. I Throughput Push Zone Forecast Safety Safety C
I Throughput Pull Zone Order C Pull Inventory And Capacity Max Max Ending Inventory = Starting Inventory  - Actual Demand + Production Throughput is limited to the smaller of limited inventory or limited capacity.
The Visualize Principle:     In network operations throughput is maximized by pushing supply to demand  by visualizing actual inventory supply across the network.   The 5V Principles of Supply Chain Management explain how a supply  chain network works by answering what, when, where, why, and how: Visualize –  where  is the inventory now and  when  will it be available?
Packaging And Labeling [ ] Transportation and warehousing costs are a function of cubic dimensions and weight. [ ] Items that have to be repalletized for transport or storage cost more. [ ] Cartons, plastic cushions, and labels may be missing from the product BOM. [ ] RFID/ bar code on all packaging. [ ] Select a wall thickness and box burst strength to protect the product. [ ] Keep Country Of Origin labeling consistent  from the product to the outside packaging. Cartons Master Carton Unit Load
Track and Trace Track Trace
Apply Technology To Visualize ,[object Object],[object Object],[object Object],[object Object],[object Object]
Measuring Network Inventory 1. Look for leakages between upstream issues and downstream receipts. 2. Look for inventory balance discrepancies at each trading partner. 3. Look for process yield issues within each trading partner. Upstream Issues = Downstream Receipts Ending Inventory = Starting Inventory + Receipts – Issues Complete Products Reflect BOM Part Proportions
To Vocalize ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],To Visualize
Suppliers Customers Trading Partner Employees We win! Shareholders Work the 5V Principles to maximize throughput. In Summary I win! I win! We win!
AGGREGRATE PLANNING (Chap8) Lesson 5 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
AGGREGRATE PLANNING STRATEGIES ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
SOP FORMAT ,[object Object],[object Object],[object Object],[object Object],PERIOD INVENTORY/ BACKLOG PRODUCTION SALES 6 5 4 3 2 1
Sales and Operations Planning Strategies
Production Rates and Levels Application 1 — Make-to-Stock  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],PRODUCTION = SALES + END INV – BEGIN INV
Production Rates and Levels Application 2 — Make-to-Order ,[object Object],[object Object],[object Object],[object Object],[object Object],PRODUCTION = SALES + BEGIN BL - END BL
OPTIMIZATION THRU LINEAR PROGRAMMING ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Aggregate Planning (Define Decision Variables) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Excel File
Aggregate Planning  8.2 DEMAND Table 8.1 (5.1)
Aggregate Planning (Define Objective Function) Monthly
Aggregate Planning (Define Constraints Linking Variables) ,[object Object]
Aggregate Planning (Constraints) ,[object Object]
Aggregate Planning (Constraints) ,[object Object]
Aggregate Planning (Constraints) ,[object Object]
SOLVING PROBLEM USING EXCEL ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
AGGREGATE PLANNING IN PRACTICE ,[object Object],[object Object],[object Object],[object Object],[object Object]
Process Flow Measures  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Homework ,[object Object]
Supply Chain Network Basics – Lesson 4 ,[object Object]
MANAGING SUPPLY AND DEMAND PREDICTABLE VARIABILITY  ( LESSON 6 ) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
MANAGING DEMAND (Predictable Variability) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
PREDICTABLE VARIABILITY IN PRACTICE ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
MANUFACTURING - MANAGING LEAD TIME ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
MANAGING INVENTORY ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Role of Inventory in the Supply Chain ,[object Object],[object Object],[object Object],[object Object],[object Object]
ROLE OF CYCLE INVENTORY (10.1) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Cycle Inventory related costs in Practice ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Fixed costs: Optimal Lot Size and Reorder Interval (EOQ)  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Example 10.1 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
EXPLOITING ECONOMIES OF SCALE ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Reducing Lot Size - Aggregating ,[object Object],[object Object],[object Object]
LOT SIZING WITH MULTIPLE PRODUCTS & CUSTOMERS ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Impact of product specific order cost Tailored aggregation  – Higher volume products ordered more frequently and lower volume products ordered less frequently (rather than ordered and delivered jointly) 10-6 Summary
Delivery Options ,[object Object],[object Object],[object Object]
Economies of Scale to exploit Quantity Discounts ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
WHY QUANTITY DISCOUNTS ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Quantity Discounts ,[object Object],[object Object],[object Object],[object Object],[object Object]
Strategies for  reducing fixed costs ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
ESTIMATING CYCLE INVENTORY COSTS ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Lessons From Aggregation ,[object Object],[object Object],[object Object],[object Object]
Lessons From Discounting Schemes ,[object Object],[object Object],[object Object],[object Object],[object Object]
Levers to Reduce Lot Sizes Without Hurting Costs ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Discussions on Site Visit ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Mid Term ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Role of Inventory in the Supply Chain ( LESSON 7) Cost Availability Efficiency Responsiveness
WHY HOLD SAFETY INVENTORY? (SAFETY STOCK) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
APPROPRIATE LEVEL OF SAFETY STOCK DEPENDS ON:   UNCERTAINTY OF DEMAND OR SUPPLY  REPLENISHMENT LEAD TIME  & DESIRED SERVICE LEVEL CSL – Cycle service level -CSL  is the fraction of replenishment cycles that end with all the customer demand being met. A replenishment cycle is the interval between two successive replenishment deliveries Time Inventory Cycle Inventory Q/2 Safety Stock Demand during  Lead time ROP Lot Size = Q SS = ROP - DL
Replenishment policies ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Continuous Review Policy: Safety Inventory and Cycle  Demand Uncertainty & Service Level ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Average Inventory = Q/2 + SS SS = ROP - RL
FORMULAS USED FOR CALCULATING SERVICE LEVELS
Example 11.1&2, 11.4 (Continuous Review Policy) = 8.xx New book ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Z Chart
Examples of Safety Stock Calculations ,[object Object]
Factors Affecting Fill Rate ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Actions: 1. Reduce supplier Lead Time L   2. Reduce underlying uncertainty of demand   R
Evaluating Safety Inventory Given Fill Rate Required safety stock grows rapidly with increase in the desired  Product availability The required SS grows rapidily with increase in desired Fill Rate The required SS increases with increase in Lead time and the  σ of demand
Impact of Supply Uncertainty ,[object Object],[object Object],[object Object],[object Object],[object Object],Standard Deviation of demand during lead time Mean demand  during lead time
Impact of Supply Uncertainty ( ( See Ex. 11.6 & Table 11.2) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Basic Quick Response Initiatives ,[object Object],[object Object],[object Object],[object Object]
Factors Affecting Value of Aggregation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
IMPACT OF AGGREGRATION ON SAFETY STOCK ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
IMPACT OF AGGREGRATION ON SAFETY STOCK ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Example 11.9: Value of Component Commonality Y Axis – SS Quantity; X Axis – No. of common components Without component commonality and postponment, product differentiation  Occurs early in the Supply Chain and inventories are disaggregate
ESTIMATING AND MANAGING SS IN PRACTICE ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Mass Customization I:  Customize Services  Around  Standardized  Products Deliver customized services as well as standardized products and services Market customized services with standardized products or services  Continue producing standardized products or services Continue developing standardized products or services Source: B. Joseph Pine DEVELOPMENT PRODUCTION MARKETING DELIVERY
Mass Customization II: Create  Customizable  Products and Services Deliver standard (but  customizable) products or services Market customizable products or services  Produce standard (but customizable) products or services Develop customizable products or services DEVELOPMENT PRODUCTION MARKETING DELIVERY
Mass Customization III: Provide  Quick Response  Throughout Value Chain  Reduce Delivery Cycle Times Reduce selection and order processing cycle  times  Reduce Production cycle time Reduce development cycle time DEVELOPMENT PRODUCTION MARKETING DELIVERY
Mass Customization IV: Provide  Point of Delivery Customization Deliver standardize portion Market customized products or services  Produce standardized portion centrally Develop products where point of delivery customization is feasible Point of delivery customization  ens Warehouse and Restaurants DEVELOPMENT PRODUCTION MARKETING DELIVERY
Mass Customization V:  Modularize  Components to  Customize End Products Deliver customized product Market customized products or services  Produce modularized components Develop modularized products  utos DEVELOPMENT PRODUCTION MARKETING DELIVERY
Types of Modularity for Mass Customization Component Sharing Modularity Cut-to-Fit Modularity Bus Modularity Mix Modularity Sectional Modularity
Example of Point of  Service Replenishment ,[object Object]
Cautions in Implementing Postponement and Modularity ,[object Object],[object Object],[object Object]
Summary of Learning Objectives Reduce Buffer Inventory Economies of Scale Supply / Demand Variability Seasonal Variability Cycle Inventory Safety Inventory Seasonal Inventory Match Supply & Demand ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
HOMEWORK ,[object Object],[object Object]
OPTIMUM LEVEL OF PRODUCT AVAILABILITY  Exercise: Swimsuit Production Lesson 8 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Demand Distribution      
Exercise ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Profitability Calculations
Profitability scenarios
OPTIMAL LEVEL OF PRODUCT AVAILABILITY ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Parkas at L.L. Bean ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Summary ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
How much to order? Parkas at L.L. Bean (Table 12.1) The probability that demand is greater than 1100 is 0.29 but the probability that demand is  greater than or equal to  1100 is 0.49. O.51 is the probability that the demand is 1000 or less. Thus, 1-0.51 = 0.49 is the probability that the demand is  greater than  1000 = probability that demand is  greater than or equal  to 1100
Parkas at L.L. Bean (Table  12.2) Expected Marginal Contribution of each 100 parkas Fig 9.1
Optimal Order Quantity Optimal Order Quantity = 13 0.917 Prob
Optimal level of service (Eqn. 12.1) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Order Quantity for a Single Order (ex 12.1) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
MANAGERIAL LEVERS TO IMPROVE PROFITABILITY ,[object Object],[object Object],[object Object],[object Object],[object Object]
Levers for Increasing Supply Chain Profitability ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Levers for Increasing Supply Chain Profitability ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Tailored Sourcing: Multiple Sourcing Sites
Dual Sourcing Strategies
SUPPLY CHAIN CONTRACTS ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
SETTING OPTIMAL LEVELS OF PRODUCT AVAILABILITY ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
CASE STUDY – OPTIMIZED DEMAND PULL ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
SOURCING and  PROCUREMENT  ( CH 14 ) Lesson 9   ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
EFFECTIVE SOURCING ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
IN HOUSE OR OUTSOURCE ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
RISKS OF USING A THIRD PARTY ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
SUPPLIER SCORING AND ASSESSMENT MUST BE BASED ON IMPACT ON  TOTAL COST (Tab14-3) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
SOURCING DECISIONS ,[object Object],[object Object]
SOURCING DECISIONS ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
SOURCING DECISIONS ,[object Object],[object Object],[object Object],[object Object],[object Object]
Make or Buy Decision ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Make-or-Buy Decision ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Cost Avoidance Analysis (Solution) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
SUPPLIER PARTNERSHIPS ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
HOMEWORK ,[object Object]
MANAGING TRANSPORTATION IN A SUPPLY CHAIN (Chap 13) – Lesson 10 ,[object Object],[object Object],[object Object],[object Object],[object Object]
Supply Chain Engineering
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Supply Chain Engineering

  • 1.
  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 8. What problems do you foresee in this Supply Chain? Please write some down Burger and Fries Examine this process – What do you observe?
  • 9. Understanding the Supply Chain … a chain is only as good as its weakest link   Recall that saying? The saying applies to the principles of building a competitive infrastructure: Manufacturer Wholesaler Retailer Customer Supplier … there is a limit to the surplus or profit in a supply chain We are all part of a Supply Chain in everything we buy Strong, well-structured supply chains are critical to sustained competitive advantage.
  • 10.
  • 11.
  • 12. Basic Supply Chain Architectures ( Examples ) 1. Indirect Channel 2. Direct Channel 3. Virtual Channel Supplier Supplier Supplier Supplier Supplier Supplier Supplier Supplier Customer Customer Customer Customer Customer Factory Factory Factory Wholesale Wholesale Integrator Express Freight Retailer Retailer Retailer Virtual Store Fabricator Fabricator Credit Service C 1999. William T. Walker, CFPIM, CIRM with the APICS Educational & Research Foundation. All Rights Reserved.
  • 13.
  • 14.
  • 15. Cycle View of Supply Chains DEFINES ROLES AND RESPONSIBILITIES OF MEMBERS OF SUPPLY CHAIN Customer Order Cycle Replenishment Cycle Manufacturing Cycle Procurement Cycle Customer Retailer Distributor Manufacturer Supplier to to to to
  • 16.
  • 17.
  • 18.
  • 19. A Customer’s View of the Supply Chain Order the product... with configuration complexity on-line Pay for the product... in a foreign currency by credit card Service the product... anywhere in the world Take delivery... the next day at home, and get started without a hassle Ex.-Travel arrangements on line FRONT OFFICE C 1999. William T. Walker, CFPIM, CIRM with the APICS Educational & Research Foundation. All Rights Reserved.
  • 20. Push/Pull View of Supply Chains PULL – PROCESSES IN RESPONSE TO A CUSTOMER ORDER PUSH – PROCESSES IN ANTICIPATION OF A CUSTOMER ORDER Procurement, Manufacturing and Replenishment cycles Customer Order Cycle Customer Order arrives PUSH PROCESSES PULL PROCESSES
  • 21.
  • 22. SUPPLY CHAIN PERFORMANCE – STRATEGIC FIT AND SCOPE ( Lesson 2) New Product Development Marketing and Sales Operations Distribution Service Finance, Accounting, Information Technology, Human Resources Business Strategy New Product Strategy Marketing Strategy Supply Chain Strategy Supply and Manufacture FILM – CHAIN REACTION EXAMPLES?
  • 23.
  • 24. Levels of Implied Demand Uncertainty Low High Price Responsiveness Customer Need Implied Demand Uncertainty Attributes (Table 2-2) Low Implied Uncertainty High Implied Uncertainty Product Margin Low – High Aver. Forecast Error 10% 40-100%; Aver. Stockout rate 1-2% 10-40%; Aver. markdown 0% 10-25% Detergent Long lead time steel High Fashion Emergency steel
  • 25.
  • 26. Step 2 - Understanding the Supply Chain: Cost-Responsiveness Efficient Frontier (Table: 2.4) High Low Low High Exercise: Give examples of products that are: Highly efficient, Somewhat efficient, Somewhat responsive and highly responsive Cost (efficient) Responsiveness Responsiveness – to Quantity, Time, Variety, Innovation, Service level Fig 2.3
  • 27. Step 3. Achieving Strategic Fit Low Cost High Cost Companies try to move Zone of Strategic fit Implied uncertainty spectrum Responsive supply chain Efficient supply chain Certain demand Uncertain demand Responsiveness spectrum Zone of Strategic Fit
  • 28.
  • 29. Strategic Scope Suppliers Manufacturer Distributor Retailer Customer Competitive Strategy Product Dev. Strategy Supply Chain Strategy Marketing Strategy
  • 30. Drivers of Supply Chain Performance TRADE OFF FOR EACH DRIVER Competitive Strategy Supply Chain Strategy Efficiency Responsiveness Inventory Transportation Facilities Information Supply chain structure Drivers
  • 31.
  • 32.
  • 33.
  • 34.
  • 35. Considerations for Supply Chain Drivers
  • 36.
  • 37.
  • 38. Dealing with Product Variety: Mass Customization Mass Customization Low High High Low Long Short Lead Time Cost Customization
  • 39.
  • 40.
  • 41.
  • 42. Forecasting (uncertainty) Order service (certainty) Demand management Demand-Management Activities RULE: Do not forecast what you can plan, calculate, or extract from supply chain feedback. Source: Adapted from Plossl, “Getting the Most from Forecasts,” APICS 15th International Conference Proceedings , 1972 Lesson 3
  • 43.
  • 44.
  • 45.
  • 46.
  • 47. WORK OUT JUNE’s FORECASTS FOR ALL SKU’s
  • 48. Simple Moving Averages (SMA) Simple Moving Average (SMA) Where F = Forecast T = Current time period D = Demand n = Number of periods( max) Forecast Forecast Demand (3-period   4-period   start-up start-up             Exercise: Work out the SMA for two periods Question: What determines the number of periods used? Why? n D D D F 2       - - + + + =
  • 49. Weighted Moving Averages Weighted Moving Average (WMA) Where: F = Forecast T = Current time period D = Demand n = Number of periods (max) W = Weight, where greatest weight applies to most recent period and sum of weights = 1 Forecast Forecast Demand    start-up start-up             Exercise: Work out forecast for two periods with weights of 0.4,0.6 What periods and weights will use for forecasting soap and fashion clothes Why?
  • 50. Exponential Smoothing   Decision þ Select or compute a smoothing constant (  ) þ Relationship of exponential smoothing to simple moving average Where n = number of past periods to be captured Where F = forecast value T = current time period D = demand  = exponential factor <1 Formulas
  • 51. Period Demand Forecast Forecast Forecast (  = .1) (  = .5) (  = .9) 0 180 start-up start-up start-up 1 160 180 180 180 2 220 178 170 162 3 200 182 195 214 4 260 184 198 201 5 240 192 229 254 6 196 234 241 Exponential Smoothing — Continued F T+1 = F T + a (D T – F T ) Work out forecasts with  =0.3 What  ’s will use for forecasting soap and fashion clothes Why?
  • 52. Simple Trended Series — Example  Algebraic Trend Projection X Y a. Trend (“rise” over “run”) = (13 - 4)/3 = 3 = b 0 4 1 7 2 10 3 13 c. Period 4: Y = a + bX = 4 + 3 (4 [for period 4]) = 16 b. Y-intercept (a) = “compute” the Y value for X = 0, thus Y-int = 4 1 2 3 13 10 7 4 Run Rise
  • 53.
  • 54.
  • 55. Seasonal Series Indexing Seasonal Month Year 1 Year 2 Year 3 Total Index Jan 10 12 11 33 0.33 Feb 13 13 11 37 0.37 Mar 33 38 29 100 1.00 Apr 45 54 47 146 1.46 May 53 56 55 164 1.64 Jun 57 56 55 168 1.68 Jul 33 27 34 94 0.94 Aug 20 18 19 57 0.57 Sep 19 22 20 61 0.61 Oct 18 18 15 51 0.51 Nov 46 50 45 141 1.41 Dec 48 53 47 148 1.48 Total 395 417 388 1200 12.00 Yr 1 Yr2
  • 56.
  • 57. Given Deseasonalized Seasonal Demand Forecast Index July 34 36 0.94 Aug 0.57  Rationale and Computations 1. Deseasonalize current (July) actual demand 2. Use exponential smoothing to project deseasonalized data one period ahead (  = .2) 3. Reseasonalize forecast for desired month (August) = Deseasonalized forecast  seasonal factor = 36.03  0.57 = 20.53 or 21 36.03 (36) (0.8) (36.17) (0.2) )F (1 D F T T 1 T          Integrative Example: Calculating a Forecast with Seasonal Indexes and Exponential Smoothing 34 0.94
  • 58.
  • 59. Normal Distribution Using the Measures of Variability Source: Adapted from CPIM Inventory Management Certification Review Course ( APICS, 1998).
  • 60. Standard Deviation ( sigma) F= A = Actual Error (Sales –  Error Period Forecast Sales Forecast) Squared              –      –                     –            –      
  • 61. Standard Deviation — Continued Standard Deviation   About the use of n or n - 1 in the above equations n Use with a large population (> 30 observations) n - 1 Use with a small population ( < 30 observations) Standard Deviation ( ) ( )    n F A     n F  2 i i 2 i i = = - = = = - - =  
  • 62. Bias and MAD Cumulative sum of error = Bias = Mean Absolute Deviation (MAD) = ( )  n   F  i i = = -   F  n   i i = = -  F = A = Actual Error  Sales – Absolute  Period Forecast Sales Forecast) Error              –      –                     –            –      
  • 63.
  • 64.
  • 65. z            ack Expressing z Values (for +ve probabilities) Probabilit y D +1 SD +2 SD +3 SD Cumulative normal distribution from left side of distribution (x + z)                                            
  • 66.
  • 67.
  • 68. Supply Chain Network Fundamentals William T. Walker, CFPIM, CIRM, CSCP Practitioner, Author, and Supply Chain Architect
  • 69.
  • 70.
  • 71. A SUPPLY CHAIN is the global network used to deliver products and services from raw materials to end customers through engineered flows of information, material, and cash. Contributed to the APICS Dictionary, 10th Edition by William T. Walker
  • 72. Network Terminology Physical Flow Info Flow Cash Flow &quot;Source&quot; &quot;Make&quot; &quot;Deliver&quot; &quot;Return&quot; Upstream Midstream Downstream Reverse Stream Zone Zone Zone Zone Customer Value-Adding Value-Subtracting
  • 73. Supplier Customer Trading Partner $ 3 M 1 M 2 M 3 $ 1 $ 2 Cash Material Material moves downstream to the customer. Cash moves upstream to the supplier. Supply Chain Network Operations
  • 74. Suppliers Customers Trading Partner Shareholders Employees Value is the Perfect Order The Value Principle: Every stakeholder wins when throughput is maximized. Value is Employment Stability Value is Return In Investment Value is Continuity of Demand
  • 75.
  • 76.
  • 77. Bill Of Materials For Example Items: A3, B2, B5, C1, C2, C3, D1 Suppliers: S1, S2, S3, S4, S5 Item Master - Stock Keeping Unit (SKU) Number - Description - Unit Of Measure - Approved Supplier - Country Of Origin - Cost - Lead Time Product Structure - Parent To Child Relationship - Quantity Per Relationship S3 S5 S4 S2 S1 D1 A3 B5 B2 C1 C2 C3 BOM Level 0. BOM Level 1. BOM Level 2. BOM Level 3.
  • 78. Supply Chain Network Map Upstream Midstream Downstream Driven by the Bill Of Materials Driven by the Delivery Channel
  • 79.
  • 80. The Velocity Principle: In network implementation throughput is maximized when order-to-delivery-to-cash velocity is maximized by minimizing process cycle time. The 5V Principles of Supply Chain Management explain how a supply chain network works by answering what, when, where, why, and how: Velocity – how are relationships connected to make the delivery?
  • 81. The Network Flow Model From: William T. Walker, Supply Chain Architecture: A Blueprint for Networking the Flow of Material, Information, and Cash , CRC Press, ©2005. Supplier Customer Trading Partner Order-To-Delivery Order-To-Stock Invoice-To-Pay Invoice-To-Cash Material Material Cash Cash Info Info
  • 82.
  • 83. Import/ Export Boundaries Country A exports and Country B imports in a forward supply chain. Country B exports and Country A imports in a reverse supply chain. Import duty and export licensing add complexity to network linkages decreasing velocity and increasing variability. Country A Country B Buyer Return Seller Shipment Exports Imports Exports Imports
  • 84. The Variability Principle: In network implementation throughput is maximized when order-to-delivery-to-cash variability is minimized by minimizing process variance. The 5V Principles of Supply Chain Management explain how a supply chain network works by answering what, when, where, why, and how: Variability – what is likely to change from one delivery to the next?
  • 85.
  • 86.
  • 87. Push/Pull Boundary Pull Push Order Push/Pull Boundary Forecast Demand Supply
  • 88. Customer Lead Time Customer Demand Pull Push Order Build-To-Order (BTO) Push/Pull Boundary Customer Demand Pull Push Build-To-Stock (BTS) Push/Pull Boundary Order F/C F/C
  • 89.
  • 90. The Vocalize Principle: In network operations throughput is maximized by pulling supply to demand by vocalizing actual demand at the network constraint. The 5V Principles of Supply Chain Management explain how a supply chain network works by answering what, when, where, why, and how: Vocalize – who knows the full requirements of the order?
  • 91. Common Causes of Stockouts L Quantity Time R SS L Q Quantity Time R SS L Q Quantity Time R SS Q Demand Uncertainty Supply Uncertainty Lead Time Variability (LT = Cycle Time + Transit Time)
  • 92. The Planning Interface Pull To Demand Push From Forecast Sales & Operations Plan Master Schedule Downstream The Supply Chain Network Push Zone Pull Zone Push/Pull Boundary I MRP Materials Requirements CRP Capacity Requirements I Upstream C C Capable Network Preload Inventory Throughput
  • 93. Push Inventory And Capacity Ending Inventory = Starting Inventory - Forecasted Demand + Production When actual demand exceeds forecasted demand, either capacity or inventory can constrain production causing lead time to expand. I Throughput Push Zone Forecast Safety Safety C
  • 94. I Throughput Pull Zone Order C Pull Inventory And Capacity Max Max Ending Inventory = Starting Inventory - Actual Demand + Production Throughput is limited to the smaller of limited inventory or limited capacity.
  • 95. The Visualize Principle: In network operations throughput is maximized by pushing supply to demand by visualizing actual inventory supply across the network. The 5V Principles of Supply Chain Management explain how a supply chain network works by answering what, when, where, why, and how: Visualize – where is the inventory now and when will it be available?
  • 96. Packaging And Labeling [ ] Transportation and warehousing costs are a function of cubic dimensions and weight. [ ] Items that have to be repalletized for transport or storage cost more. [ ] Cartons, plastic cushions, and labels may be missing from the product BOM. [ ] RFID/ bar code on all packaging. [ ] Select a wall thickness and box burst strength to protect the product. [ ] Keep Country Of Origin labeling consistent from the product to the outside packaging. Cartons Master Carton Unit Load
  • 97. Track and Trace Track Trace
  • 98.
  • 99. Measuring Network Inventory 1. Look for leakages between upstream issues and downstream receipts. 2. Look for inventory balance discrepancies at each trading partner. 3. Look for process yield issues within each trading partner. Upstream Issues = Downstream Receipts Ending Inventory = Starting Inventory + Receipts – Issues Complete Products Reflect BOM Part Proportions
  • 100.
  • 101. Suppliers Customers Trading Partner Employees We win! Shareholders Work the 5V Principles to maximize throughput. In Summary I win! I win! We win!
  • 102.
  • 103.
  • 104.
  • 105. Sales and Operations Planning Strategies
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  • 110. Aggregate Planning 8.2 DEMAND Table 8.1 (5.1)
  • 111. Aggregate Planning (Define Objective Function) Monthly
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  • 134. Impact of product specific order cost Tailored aggregation – Higher volume products ordered more frequently and lower volume products ordered less frequently (rather than ordered and delivered jointly) 10-6 Summary
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  • 145.
  • 146. Role of Inventory in the Supply Chain ( LESSON 7) Cost Availability Efficiency Responsiveness
  • 147.
  • 148. APPROPRIATE LEVEL OF SAFETY STOCK DEPENDS ON: UNCERTAINTY OF DEMAND OR SUPPLY REPLENISHMENT LEAD TIME & DESIRED SERVICE LEVEL CSL – Cycle service level -CSL is the fraction of replenishment cycles that end with all the customer demand being met. A replenishment cycle is the interval between two successive replenishment deliveries Time Inventory Cycle Inventory Q/2 Safety Stock Demand during Lead time ROP Lot Size = Q SS = ROP - DL
  • 149.
  • 150.
  • 151. FORMULAS USED FOR CALCULATING SERVICE LEVELS
  • 152.
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  • 154.
  • 155. Evaluating Safety Inventory Given Fill Rate Required safety stock grows rapidly with increase in the desired Product availability The required SS grows rapidily with increase in desired Fill Rate The required SS increases with increase in Lead time and the σ of demand
  • 156.
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  • 162. Example 11.9: Value of Component Commonality Y Axis – SS Quantity; X Axis – No. of common components Without component commonality and postponment, product differentiation Occurs early in the Supply Chain and inventories are disaggregate
  • 163.
  • 164. Mass Customization I: Customize Services Around Standardized Products Deliver customized services as well as standardized products and services Market customized services with standardized products or services Continue producing standardized products or services Continue developing standardized products or services Source: B. Joseph Pine DEVELOPMENT PRODUCTION MARKETING DELIVERY
  • 165. Mass Customization II: Create Customizable Products and Services Deliver standard (but customizable) products or services Market customizable products or services Produce standard (but customizable) products or services Develop customizable products or services DEVELOPMENT PRODUCTION MARKETING DELIVERY
  • 166. Mass Customization III: Provide Quick Response Throughout Value Chain Reduce Delivery Cycle Times Reduce selection and order processing cycle times Reduce Production cycle time Reduce development cycle time DEVELOPMENT PRODUCTION MARKETING DELIVERY
  • 167. Mass Customization IV: Provide Point of Delivery Customization Deliver standardize portion Market customized products or services Produce standardized portion centrally Develop products where point of delivery customization is feasible Point of delivery customization  ens Warehouse and Restaurants DEVELOPMENT PRODUCTION MARKETING DELIVERY
  • 168. Mass Customization V: Modularize Components to Customize End Products Deliver customized product Market customized products or services Produce modularized components Develop modularized products  utos DEVELOPMENT PRODUCTION MARKETING DELIVERY
  • 169. Types of Modularity for Mass Customization Component Sharing Modularity Cut-to-Fit Modularity Bus Modularity Mix Modularity Sectional Modularity
  • 170.
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  • 175. Demand Distribution      
  • 176.
  • 179.
  • 180.
  • 181.
  • 182. How much to order? Parkas at L.L. Bean (Table 12.1) The probability that demand is greater than 1100 is 0.29 but the probability that demand is greater than or equal to 1100 is 0.49. O.51 is the probability that the demand is 1000 or less. Thus, 1-0.51 = 0.49 is the probability that the demand is greater than 1000 = probability that demand is greater than or equal to 1100
  • 183. Parkas at L.L. Bean (Table 12.2) Expected Marginal Contribution of each 100 parkas Fig 9.1
  • 184. Optimal Order Quantity Optimal Order Quantity = 13 0.917 Prob
  • 185.
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  • 190. Tailored Sourcing: Multiple Sourcing Sites
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Editor's Notes

  1. Lesson 1