basic fundamentals of supply chain engineering

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.

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.

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

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

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?

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

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

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

35. Considerations for Supply Chain Drivers

38. Dealing with Product Variety: Mass Customization Mass Customization Low High High Low Long Short Lead Time Cost Customization

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

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

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

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

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              –      –                     –            –      

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)                                            

68. Supply Chain Network Fundamentals William T. Walker, CFPIM, CIRM, CSCP Practitioner, Author, and Supply Chain Architect

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

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

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

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?

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

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

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

101. Suppliers Customers Trading Partner Employees We win! Shareholders Work the 5V Principles to maximize throughput. In Summary I win! I win! We win!

105. Sales and Operations Planning Strategies

110. Aggregate Planning 8.2 DEMAND Table 8.1 (5.1)

111. Aggregate Planning (Define Objective Function) Monthly

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

146. Role of Inventory in the Supply Chain ( LESSON 7) Cost Availability Efficiency Responsiveness

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

151. FORMULAS USED FOR CALCULATING SERVICE LEVELS

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

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

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

175. Demand Distribution      

177. Profitability Calculations

178. Profitability scenarios

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

190. Tailored Sourcing: Multiple Sourcing Sites

191. Dual Sourcing Strategies