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Defense Supply Chain
 

Defense Supply Chain

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    Defense Supply Chain Defense Supply Chain Presentation Transcript

    • Defense Supply Chain A Logistics Lifecycle Management for TACOM’s Extended Enterprise A Short Workshop on Developing and Implementing Supply Chain 5th Annual U.S. Army Vetronics Institute Winter Workshop Series U.S. Army, TACOM, Warren, Michigan January 9-12, 2006 Presenter: Charu Chandra, Ph.D. Associate Professor Industrial and Manufacturing Systems Engineering Department The University of Michigan-Dearborn Engineering Complex 2230 4901 Evergreen Road, Dearborn, MI 48128-1491 Phone: 313-593-5258; Fax: 313-593-3692; E-mail: charu@umich.edu URL: http://www-personal.engin.umd.umich.edu/~charu/ January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Main Topics Module I: Supply Chain Management - Concepts and Applications Module II: Supply Chain Informatics - Theory and Concepts Module III: Military Supply Chains - Issues and Perspectives Wrap-up 2 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Module I: Supply Chain Management Concepts and Applications
    • Presentation Outline Supply Chain: Background and Perspectives Supply Chain Applications – Logistics Network Design – Inventory Management Supply Contracts Managing the Bullwhip Effect e-Business Models – Design for Logistics – Mass Customization 4 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Supply Chain Background and Perspectives
    • Supply chain Definition: A network of independent business organizations with common goals formed to optimize their resources to meet customers’ needs through sharing of information, expertise (technology), and resources for mutual benefits. 6 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • A supply chain Customers, Field demand Sources: Regional Warehouses: centers plants Warehouses: stocking sinks vendors stocking points ports points Supply Inventory & warehousing costs Production/ Transportation costs purchase Transportation costs Inventory & costs Warehousing costs Time 7 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Supply chain network: A general representation Trans-shipment node(s) Supplier 1 Customer 1 Distribution center 1 Plant 1 Supplier 2 Customer 2 Demand Node(s) Distribution Supply Node(s) center 2 Supplier 3 Customer 3 Plant 2 (source) (sink) Dj (-) j ti Distribution Si (+) ) c, ij center 3 ,u ij Supplier 4 Customer 4 (f ij Independent business Supply stage Production stage Distribution stage Consumption stage Entity (with unique OEM END-PRODUCT DISTRIBUTOR / END-CONSUMER objectives and independent MANUFACTURER WAREHOUSER / RETAILER resources) Notations: cij, = cost of movement of goods from node i to node j tij = time elapsed in movement of goods from node i to node j fij = flow of goods (in units) from node i to node j uij = capacity of arc connecting node i to node j 8 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Supply chain management Definition: Supply Chain Management is primarily concerned with the efficient integration of suppliers, factories, warehouses and stores so that merchandise is produced and distributed in the right quantities, to the right locations and at the right time, so as to minimize total system cost subject to satisfying service requirements. 9 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Supply chain management Supply Echelon Demand Forecasts Supplier 1 Customer 1 Distribution center 1 Vertical Integration Plant 1 Supplier 2 Customer 2 Distribution center 2 Supplier 3 Customer 3 Plant 2 Distribution center 3 Supplier 4 Customer 4 Inventory Replenishment Supply stage Production stage Distribution stage Consumption stage OEM END-PRODUCT DISTRIBUTOR / END-CONSUMER MANUFACTURER WAREHOUSER / Supply Networks Demand Networks RETAILER Horizontal Integration 10 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Conflicting objectives in the supply chain Minimum Total System Cost Supplier 1 Customer 1 Distribution center 1 Plant 1 Supplier 2 Customer 2 Distribution center 2 Supplier 3 Customer 3 Plant 2 Distribution center 3 Supplier 4 Customer 4 Function Supply stage Production stage Distribution stage Consumption stage Purchasing Manufacturing Warehousing Customers •Stable Volume Reqmts. •Long Production Run •Low Inventory •Short Order Lead Time •Flexible DeliveryTime •High Quality •Reduced Transportation •High in Stock •Little Variations in Mix •High Productivity Costs •Large Product Variety •Larger Order Quantities •Low Production Cost •Quick Replenishment •Low Prices 11 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Supply chain costs Management Costs Supplier 1 Customer 1 Distribution center 1 Plant 1 Supplier 2 Customer 2 Distribution center 2 Supplier 3 Customer 3 Plant 2 Distribution center 3 Supplier 4 Customer 4 Supply stage Production stage Distribution stage Consumption stage •Production / Assembly •Production Costs •Warehousing Costs •Marketing Costs Costs •Purchase Costs •Finished Goods Cost •Raw Materials Purchase •Set-up Costs Inventory Holding Costs Costs •Start-up Costs (Fixed •Ordering Costs Classification •Transportation Costs Costs) •Transportation Costs •Raw Materials Inventory •Transportation Costs Holding Costs •Work-in-Process Inventory Holding Costs 12 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Supply chain: the magnitude In 1998, American companies spent $898 billion in supply-related activities (or 10.6% of Gross Domestic Product). – Transportation 58% – Inventory 38% – Management 4% Third party logistics services grew in 1998 by 15% to nearly $40 billion. 13 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Supply chain: the magnitude (continued) It is estimated that the grocery industry could save $30 billion (10% of operating cost) by using effective logistics strategies. – A typical box of cereal spends 104 days getting from factory to supermarket. A typical new car spends 15 days traveling from the factory to the dealership. 14 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Supply chain: the magnitude (continued) Compaq computer estimates it lost $500 million to $1 billion in sales in 1995 because its laptops and desktops were not available when and where customers were ready to buy them. Boeing Aircraft, one of America’s leading capital goods producers, was forced to announce write-downs of $2.6 billion in October 1997. The reason? “Raw material shortages, internal and supplier parts shortages…”. (Wall Street Journal, Oct. 23, 1997) 15 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Supply chain: the potential Procter & Gamble estimates that it saved retail customers $65 million through logistics gains over the past 18 months. “According to P&G, the essence of its approach lies in manufacturers and suppliers working closely together …. jointly creating business plans to eliminate the source of wasteful practices across the entire supply chain”. (Journal of Business Strategy, Oct./Nov. 1997) 16 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Supply chain: the potential (continued) Dell Computer has outperformed the competition in terms of shareholder value growth over the eight years period, 1988-1996, by over 3,000% using - Direct business model - Build-to-order strategy 17 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Supply chain: the potential (continued) In 10 years, Wal-Mart transformed itself by changing its logistics system. It has the highest sales per square foot, inventory turnover and operating profit of any discount retailer. 18 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Supply chain: the complexity National Semiconductors: • Production: – Produces chips in six different locations: four in the US, one in Britain and one in Israel. – Chips are shipped to seven assembly locations in Southeast Asia. • Distribution – The final product is shipped to hundreds of facilities all over the world. – 20,000 different routes. – 12 different airlines are involved. – 95% of the products are delivered within 45 days. – 5% are delivered within 90 days. 19 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Supply chain challenges Achieving Global Optimization – Conflicting Objectives – Complex network of facilities – System Variations over time Managing Uncertainty – Matching Supply and Demand – Demand is not the only source of uncertainty 20 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Sequential Optimization vs. Global Optimization Sequential Optimization Procurement Manufacturing Distribution Demand Planning Planning Planning Planning Global Optimization Supply Contracts/Collaboration/Information Systems and DSS Procurement Manufacturing Distribution Demand Planning Planning Planning Planning Source: Duncan McFarlane 21 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • What’s New in Logistics? Global competition Shorter product life cycle New, low-cost distribution channels More powerful well-informed customers Internet and E-Business strategies 22 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • New Concepts Push-Pull strategies Direct-to-Consumer Strategic alliances Manufacturing postponement Mass Customization Dynamic Pricing E-Procurement 23 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Supply Chain Management Key Issues Distribution Network Outsourcing and Configuration Procurement Strategies Inventory Control Information Supply Contracts Technology and Distribution Strategies Decision Support Systems Supply Chain Integration and Customer Value Strategic Partnering 24 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Supply Chain Management Problem-Solving Approaches Issues Problem-Solving Approaches Distribution Network Configuration Network Flow Optimization Inventory Control Forecasting and Inventory Management Supply Contracts Global Optimization Distribution Strategies Warehousing and Transportation Costs Management Supply Chain Integration and Collaborative Planning, Forecasting and Replenishment Strategic Partnering (CPFR) Outsourcing and Procurement Managing risk, payoff tradeoffs with Outsourcing vs. Strategies Buying Information Technology and Decision Implementing Enterprise Resource Planning (ERP) Support Systems Decision Support Systems Customer Value Statistical Process Control, Total Quality Management, Service Level Maximization 25 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Supply Chain Applications
    • Logistics Network Design
    • The Logistics Network The Logistics Network consists of: Facilities:Vendors, Manufacturing Centers, Warehouse/ Distribution Centers, and Customers. Materials: Raw materials and finished products that flow between these facilities. 28 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Decision Classifications Strategic Planning: Decisions that involve major capital investments and have a long term effect 1. Determination of the number, location and size of new plants, distribution centers and warehouses 2. Acquisition of new production equipment and the design of working centers within each plant 3. Design of transportation facilities, communications equipment, data processing means, etc. 29 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Decision Classifications Tactical Planning: Effective allocation of manufacturing and distribution resources over a period of several months 1. Work-force size 2. Inventory policies 3. Definition of the distribution channels 4. Selection of transportation and trans-shipment alternatives 30 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Decision Classifications Operational Control: Includes day-to-day operational decisions 1. The assignment of customer orders to individual machines 2. Dispatching, expediting and processing orders 3. Vehicle scheduling 31 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Network Design: Key Issues Pick the optimal number, location, and size of warehouses and/or plants Determine optimal sourcing strategy – Which plant/vendor should produce which product Determine best distribution channels – Which warehouses should service which customers 32 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Network Design: Key Issues The objective is to balance service level against Production/ purchasing costs Inventory carrying costs Facility costs (handling and fixed costs) Transportation costs That is, we would like to find a minimal-annual-cost configuration of the distribution network that satisfies product demands at specified customer service levels. 33 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Aggregating Customers Customers located in close proximity are aggregated using a grid network or clustering techniques. All customers within a single cell or a single cluster are replaced by a single customer located at the centroid of the cell or cluster (referred to as a customer zone). 34 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Comparing Output Total Cost:$5,796,000 Total Cost:$5,793,000 Total Customers: 18,000 Total Customers: 800 Cost Difference < 0.05% 35 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Product Grouping Companies may have hundreds to thousands of individual items in their production line 1. Variations in product models and style 2. Same products are packaged in many sizes Collecting all data and analyzing it is impractical for so many product groups 36 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Within Each Source Group, Aggregate Products by Similar Characteristics 70.0 60.0 50.0 Weight (lbs per case) 40.0 30.0 Rectangles 20.0 illustrate how to cluster SKU’s. 10.0 0.0 0.000 0.010 0.020 0.030 0.040 0.050 0.060 0.070 0.080 0.090 0.100 Volume (pallets per case) 37 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Sample Aggregation Test: Product Aggregation Total Cost:$104,564,000 Total Cost:$104,599,000 Total Products: 46 Total Products: 4 Cost Difference: 0.03% 38 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • A Typical Network Design Model Several products are produced at several plants. Each plant has a known production capacity. There is a known demand for each product at each customer zone. The demand is satisfied by shipping the products via regional distribution centers. There may be an upper bound on total throughput at each distribution center. 39 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • A Typical Location Model There may be an upper bound on the distance between a distribution center and a market area served by it A set of potential location sites for the new facilities was identified Costs: – Set-up costs – Transportation cost is proportional to the distance – Storage and handling costs – Production/supply costs 40 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Inventory Management
    • Inventory Where do we hold inventory? – Suppliers and manufacturers – Warehouses and distribution centers – Retailers Types of Inventory – Raw materials – Work-in-process (WIP) – Finished goods Why do we hold inventory? – Economies of scale – Uncertainty in supply and demand 42 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Goals: Reduce Cost, Improve Service By effectively managing inventory: – Xerox eliminated $700 million inventory from its supply chain – Wal-Mart became the largest retail company utilizing efficient inventory management 43 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Goals: Reduce Cost, Improve Service By not managing inventory successfully – In 1994, “IBM continues to struggle with shortages in their ThinkPad line” (WSJ, Oct 7, 1994) – In 1993, “Liz Claiborne said its unexpected earning decline is the consequence of higher than anticipated excess inventory” (WSJ, July 15, 1993) – In 1993, “Dell Computers predicts a loss; Stock plunges. Dell acknowledged that the company was sharply off in its forecast of demand, resulting in inventory write downs” (WSJ, August 1993) 44 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Understanding Inventory The inventory policy is affected by: – Demand Characteristics – Lead Time – Number of Products – Objectives Service level Minimize costs – Cost Structure 45 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Cost Structure Order Costs – Fixed – Variable Holding Costs – Insurance – Maintenance and Handling – Taxes – Opportunity Costs – Obsolescence 46 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Types of inventory Supplier 1 Customer 1 Distribution center 1 Plant 1 Supplier 2 Customer 2 Fixed (Ordering) Distribution center 2 Supplier 3 Customer 3 Plant 2 Distribution center 3 Supplier 4 Customer 4 Supply stage Production stage Distribution stage Consumption stage Criteria Inventory Costs •Demand Pattern •Raw Materials / •Raw Materials / •Finished Goods •Shelved Goods •Fixed (Ordering) •Lead Time Assembly Assembly •Variable •No. of Products •Work-in-Process •Holding •Objectives •Finished Goods Insurance Service Level Maintenance and Holding Minimum Costs Taxes Opportunity Obsolescence 47 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Inventory / Production policies Postponement Supplier 1 Customer 1 Distribution center 1 Plant 1 Supplier 2 Customer 2 Fixed (Ordering) Distribution center 2 Supplier 3 Customer 3 Plant 2 Distribution center 3 Supplier 4 Customer 4 Supply stage Production stage Distribution stage Consumption stage •Push •Make-to-Stock •Consolidation •Pull •Make-to-Order •Cross Docking •Push - Pull •Third-party Logistics 48 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Factors that Drive Reduction in Inventory Top management emphasis on inventory reduction (19%) Number of SKUs in the warehouse (10%) Improved forecasting (7%) Use of sophisticated inventory management software (6%) Coordination among supply chain members (6%) Others 49 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Inventory Management: Supply Contracts
    • Supply Contracts Fixed Production Cost Variable Production Cost Wholesale Price Selling Price Salvage Value Manufacturer Manufacturer DC Retail DC Stores 51 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Supply Contracts Fixed Production Cost Variable Production Cost Wholesale Price Selling Price Salvage Value Manufacturer Manufacturer DC Retail DC Stores 52 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Supply Contracts Fixed Production Cost Variable Production Cost Wholesale Price Selling Price Salvage Value Manufacturer Manufacturer DC Retail DC Stores 53 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Supply Contracts: Key Insights Effective supply contracts allow supply chain partners to replace sequential optimization by global optimization Buy Back and Revenue Sharing contracts achieve this objective through risk sharing 54 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Supply Contracts (Risk Pooling) Supplier 1 Customer 1 Distribution center 1 Plant 1 Supplier 2 Customer 2 Fixed (Ordering) Distribution center 2 Supplier 3 Customer 3 Plant 2 Distribution center 3 Supplier 4 Customer 4 t y e bili Supply stage Production stage g rin Distribution stage b at Consumption stage e xi Sh a Re Fl e les tit y enu ck Sa u an ev -Ba R y Q Bu 55 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Inventory Management: Managing the Bullwhip Effect
    • The Dynamics of the Supply Chain Order Size Customer Customer Demand Demand Retailer Orders Retailer Orders Distributor Orders Distributor Orders Production Plan Production Plan Time Source: Tom Mc Guffry, Electronic Commerce and Value Chain Management, 1998 57 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • The Bullwhip Effect and its Impact on the Supply Chain Consider the order pattern of a single color television model sold by a large electronics manufacturer to one of its accounts, a national retailer. Order Stream Huang at el. (1996), Working paper, Philips Lab 58 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • The Bullwhip Effect and its Impact on the Supply Chain Point-of-sales Data- Original POS Data After Removing Promotions 59 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • The Bullwhip Effect and its Impact on the Supply Chain POS Data After Removing Promotion & Trend 60 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Higher Variability in Orders Placed by Computer Retailer to Manufacturer Than Actual Sales Lee, H, P. Padmanabhan and S. Wang (1997), Sloan Management Review 61 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Increasing Variability of Orders Up the Supply Chain Lee, H, P. Padmanabhan and S. Wang (1997), Sloan Management Review 62 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • The Bullwhip Effect: Managerial Insights Exists, in part, due to the retailer’s need to estimate the mean and variance of demand. The increase in variability is an increasing function of the lead time. The more complicated the demand models and the forecasting techniques, the greater the increase. Centralized demand information can reduce the bullwhip effect, but will not eliminate it. 63 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Coping with the Bullwhip Effect in Leading Companies Reduce Variability and Uncertainty - Point-of-Sales (POS) - Sharing Information - Year-round low pricing Reduce Lead Times - Electronic-Data-Interchange (EDI) - Cross Docking Alliance Arrangements – Vendor managed inventory – On-site vendor representatives 64 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Distribution Strategies Warehousing Direct Shipping – No Distribution Centers needed – Lead times reduced – “smaller trucks” – no risk pooling effects Cross-Docking 65 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Supply Chain Integration: Dealing with Conflicting Goals Lot Size vs. Inventory Inventory vs. Transportation Lead Time vs. Transportation Product Variety vs. Inventory Cost vs. Customer Service 66 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • What are the Causes…. Promotional sales Volume and Transportation discounts Inflated orders Demand Forecast Long cycle times Lack of Visibility to demand information 67 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Consequences…. Increased safety stock Reduced service level Inefficient allocation of resources Increased transportation costs 68 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Inventory Management: e-Business Models
    • The Future is Not What it Used to Be A new e-Business Model – Reduce cost – Increase Profit – Increase service level – Increase flexibility 70 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Reality is Different….. Amazon (Book) Peapod (Grocery) Dell (Computers) Cisco (Network Management) 71 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • The e-Business Model e-Business is a collection of business models and processes motivated by Internet technology, and focusing on improving the extended enterprise performance – e-commerce is part of e-Business – Internet technology is the driver of the business change – The focus is on the extended enterprise: Intra-organizational Business to Consumer (B2C) Business to Business (B2B) 72 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • A new Supply Chain Paradigm A shift from a Push System... – Production decisions are based on forecast …to a Push-Pull System – Parts inventory is replenished based on forecasts – Assembly is based on accurate customer demand 73 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • From Make-to-Stock Model…. Suppliers Assembly Configuration 74 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Push-Pull Supply Chains The Supply Chain Time Line Customers Suppliers PUSH STRATEGY PULL STRATEGY Low Uncertainty High Uncertainty Push-Pull Boundary 75 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • ….to Assemble-to-Order Model Suppliers Assembly Configuration 76 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Business models in the Book Industry From Push Systems... – Barnes and Noble ...To Pull Systems – Amazon.com, 1996-1999 And, finally to Push-Pull Systems – Amazon.com, 1999-present 7 warehouses, 3M sq. ft., 77 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • e-Business in the Retail Industry Brick-&-Mortar companies establish Virtual retail stores – Wal-Mart, K-Mart, Barnes and Noble Use a hybrid approach in stocking – High volume/fast moving products for local storage – Low volume/slow moving products for browsing and purchase on line Channel Conflict Issues 78 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • e-Fulfillment Requires a New Logistics Infrastructure Traditional Supply Chain e-Supply Chain Supply Chain Strategy Push Push-Pull Shipment Type Bulk Parcel Inventory Flow Unidirectional Bi-directional Reverse Logistics Simple Highly Complex Destination Small Number of Stores Highly Dispersed Customers Lead Times Depends Short 79 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Matching Supply Chain Strategies with Products Demand uncertainty Pull H I II Computer IV III Delivery cost Unit price Push L L H Economies of Scale Pull Push 80 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Locating the Push-Pull Boundary 81 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Organizational Skills Needed Raw Material Customers Push Pull Low Uncertainty High Uncertainty Long Lead Times Short Cycle Times Cost Minimization Service Level Resource Allocation Responsiveness 82 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • e-Business Opportunities: Reduce Facility Costs – Eliminate retail/distributor sites Reduce Inventory Costs – Apply the risk-pooling concept Centralized stocking Postponement of product differentiation Use Dynamic Pricing Strategies to Improve Supply Chain Performance 83 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • e-Business Opportunities: Supply Chain Visibility – Reduction in the Bullwhip Effect Reduction in Inventory Improved service level Better utilization of Resources – Improve supply chain performance Provide key performance measures Identify and alert when violations occur Allow planning based on global supply chain data 84 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Design for Logistics
    • Design for Logistics Concept Design for Logistics addresses three key components to manage trade-offs between inventory and service levels: – Economic packaging and transportation. – Concurrent and parallel processing. – Standardization. 86 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Economic packaging and transportation Products that can be packed compactly – are cheaper to transport, – Use up less storage space, – Facilitate cross-docking operations, – Impact handling costs because of lesser handling needed. 87 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Concurrent and Parallel Processing Modifying the manufacturing processes from sequential and dependent structures to concurrent and parallel processing. – Implement decoupling of manufacturing processes so as to make them more flexible. Benefits: reduced manufacturing lead time, lower inventory costs through improved forecasting, and reduced safety stock. 88 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Standardization Standardization can lower inventory costs and increase forecast accuracy. Standardization involves introducing concepts of: – Product Modularity Product assembled in modules allowing flexibility. – Process Modularity Allows implementing discrete manufacturing operations so that inventory can be stored in partially manufactured form between operations. 89 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Approaches to Standardization Part Standardization Process Standardization Product Standardization Procurement Standardization 90 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Part Standardization Commonality among parts. Common parts are introduced among products. Common parts reduce required part inventories due to risk pooling and reduce part costs. 91 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Process Standardization Involves standardizing as much of the process as possible for different products, and then customizing products as late as possible. Manufacturing process starts by making a generic or family product that is later differentiated into a specific end-product. – Also termed as postponement or delayed product differentiation strategy. Most of the time requires redesigning the process, such as re-sequencing. 92 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Product Standardization A large variety of products may be offered, but only a few kept in inventory. Resort to downward substitution when a product not kept in stock is ordered. – Product is substituted with product offering a superset of features. 93 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Procurement Standardization Standardizing processing equipment and approaches, even when the product itself is not standardized. – Example: Integrated Circuits. 94 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Selecting a Standardization Strategy Operational Strategies for Standardization Part Process Modular Standardization Standardization Product Product Procurement Non-Modular Standardization Standardization Non-Modular Modular Process IMSE 565, Winter 2003 Instructor: C. Chandra, University of Michigan-Dearborn 95 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Supplier Integration into New Product Development Involve suppliers in the design process. Potential benefits: – Reduced Purchased Materials costs – Increase in Purchased Materials quality – Decline in development time and cost – Decline in manufacturing cost – Increase in final product technology levels 96 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Spectrum of Supplier Integration None – Supplier is not involved in design. Materials and subassemblies are supplied according to customer specification and design. White Box – Informal consultations between supplier and buyer when designing products and specifications. Grey Box – Formal supplier integration into the design process. Formal supplier / buyer teams work on joint development. Black Box – Supplier independently designs the product according to requirements given by the buyer. 97 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Strategic Planning Process for Supplier Integration Proposed by the study at Michigan State University (1997): – Determine internal core competencies – Determine current and future new product developments – Identify external development and manufacturing needs 98 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Keys to Effective Supplier Integration Select suppliers and build relationships with them. Align objectives with selected suppliers. 99 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Mass Customization
    • Mass Customization Concept Mass customization involves the delivery of a wide variety of customized goods or services quickly and efficiently at low cost. 101 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Attributes for Implementing Mass Customization Strategy Rapid response to customer demands through quick linkages of production modules and processes. Linkages should be costless, that is, add very little cost to processes. Linkages should be seamless so customer service does not suffer. Networks or collections should be formed with little overhead. 102 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Techniques to Manage Inventories due to Product Proliferation Build-to-Order Model utilizing product postponement and push-pull strategies Keep large inventories at major distribution centers Offer fixed set of options that cover most customer requirements 103 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Module II: Supply Chain Informatics Theory and Concepts
    • Presentation outline System and System Design Supply Chain Informatics Reconfigurable systems Motivation and general guiding principles of research Problem solving framework Algorithmic modeling of reconfigurable supply chain – Information support system – Decision modeling system – Decision support systems prototype Examples of representative research problems 105 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • System and System Design
    • System: A Definition A system may be defined as an assemblage of sub-systems (components, modules, etc.), and agents and mechanisms (people, technology, and resources) designed to perform a set of tasks to satisfy specified functional requirements and constraints. 107 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • General Systems Theory Background Ludwig von Bertalanffy formulated a new discipline, General System Theory (GST), and defined its subject matter as “formulation and derivation of those principles which are valid for systems in general whatever the nature of the component elements and the relations or forces between them”. GST enunciated the principle of unification of science, and its essence was interdisciplinarity. It produced a new type of scientific knowledge: interdisciplinary knowledge. According to Bertalanffy, there is some element of isomorphism (state of similarity), which allows extension of one scientific discipline to other sciences. 108 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Concept of System in GST Unity, Parts, and Relationship Unity (‘consistent whole’, ‘complex Environment Unity whole’, ‘wholeness’, ‘synergy’, etc.). Parts (‘elements’, ‘constituents’, ‘components’, etc.). Relationship Relationship (‘interrelationship’, ‘interactions’, ‘structure’, and Part ‘organization’). Source: Dubrovsky (2004). 109 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Role of System in an Organization System gives organization a formal structure, a purpose, a goal (s) [objectives], and above all a basis for integration. Such a structure is beneficial for an organization in managing its complexity, integration of its functions, and aligning its product- process-resource structure. System provides the framework that an organization needs for designing and implementing models, methodologies, tools and techniques for aligning its business (es) and improving productivity. 110 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • How do System and Organization complement each other? System has a structure (or organization). Organization is a class of system and thus inherits its (system’s) structure. System needs an organization (and its structure) for a formal representation of an enterprise. On the other hand, organization needs a system (and its framework) for formalization. 111 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Issues Related to System Design How should a complex system be designed? – Top-down vs. bottom-up How should the complex relationships between various components of a system be coordinated and managed? – Modular with process flow interface How can the stability and controllability of a system be guaranteed? – Satisfying the Design Axioms 112 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Domain of System Design CAs FRs DPs PVs Customer Functional Physical Process Domain Domain Domain Domain Source: Suh, 1998 113 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Design Axioms Axiom 1: The Independence Axiom Maintain the independence of the Functional Requirements (FRs). Axiom 2: The Information Axiom Minimize the information content of the design. 114 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Inferences from Design Axioms Uncoupled Design: When each of the FRs can be satisfied independently by means of one DP. Decoupled Design: When the independence of FRs can be guaranteed, iff the DPs are changed in the proper sequence. Coupled Design: When the design violates the Independence Axiom (or Axiom 1). When several functional requirements must be satisfied, designers must develop designs that are either uncoupled or decoupled. Among all the designs that satisfy the Independence Axiom (or Axiom 1), the design that has the least information content is the best design. 115 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Influence of GST on System Design The biggest influence that GST has had on System Design is in its formalization. For example, system is designed to recognize its whole-part relationship instantiated in its environment (both internal and external). The concept of isomorphism has facilitated system design by recognizing similarity (or commonness) across entities, relationships, and environmental variables. Similarity implicitly recognizes relationships, thereby improving a system’s representation and eventually impacting its performance (quality, reliability etc.). Another useful feature of GST in system design is separating information needs (and associated knowledge) at the domain independent (or generic) level from that of domain dependent (or specific / problem) level. Such an approach ensures that the system captures both breadth and depth of knowledge. Since the latter is embedded in the former, the captured knowledge has a larger context, thereby ensuring interactions and thus larger relevance. It also ensures that the knowledge does not become redundant. 116 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Influence of GST on System Design Some key principles Unity: All system (and its components) is whole (or unity) depending on the context where they are represented. Commonality: All systems in the universe of systems share common universal characteristics. Isomorphism: Similarity (and therefore commonality) among system components and associated relationships. Reuse: Commonality leads to reuse and eventually standardization, conformity and reliability. Abstraction: Enables managing complexity by abstracting features of system’s components. It also allows representation of relationships such as, whole-part, and generalization-specialization. Polymorphism: Creates classes of systems and reusing them for specialized functions. Encapsulation: Enables encapsulating knowledge and information-hiding on objects (and classes) to create uniqueness of objects (and classes). Independence: Domain independent vs. domain dependent knowledge creation. Inheritance: Enables avoiding information redundancy and information-hiding by clustering information representation where they rightfully belong. 117 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Supply Chain Informatics
    • Concepts Supply Chain Informatics is the basis for applying Information Science to supply chain problems. The primary thrust of this area is on investigating design and modeling issues in information management of logistics in production networks. Specifically, it applies the concept of Information Economics to managing technology, aided by knowledge from multi-disciplinary topics in seeking solutions for supply chain problems. 119 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Motivation Application Explore Theory research in cross- cutting Systems Engineering areas Systems Science Apply this knowledge to investigating Management Science emerging Decision Science Industrial Engineering public policy Operations Research areas / issues Tools & Techniques 120 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Support Integrated Product Life Cycle System Cyber-Infrastructure (Internet, eBusiness) Product-Life-Cycle PDM Systems (CAD/CAM/CAE, ERP Systems Expert Systems) Supply Chain Process-Life-Cycle Suppliers Customers (Plan, Source, Make, Deliver, Return) 121 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Enable Co-Design of Product Systems Virtual Systems Design (Product Delivery Configuration) Logical Systems Design (Designing Inbound/Outbound Logistics) Physical Systems Design (Designing Product-Process Interface) Integrating consumer-supplier interface requirements concurrently at design time 122 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Reconfigurable Systems Manufacturing Systems that can be: – Designed, modeled, and Reconfigurable supply chain configured according to specific applications flexibly and with agility, Supplier 1 Distribution Customer 1 and Plant 1 center 1 – Upgraded and reconfigured Supplier 2 Customer 2 rather than replaced. Distribution center 2 With a reconfigurable Supplier 3 Customer 3 Plant 2 system, new products and Supplier 4 Distribution center 3 Customer 4 processes can supposedly be introduced with Supply stage Production stage Distribution stage Consumption stage considerably less expense and ramp-up time. 123 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Reconfigurable supply chain Triggers Issues Introduction of new product(s), or Assessing impacts of one or more of upgrade for existing product(s). following factors / activities in order to Introduction of new, or make (economic) decisions to improvement in existing implement reconfigurable systems: process(es). –Flows due to materials, inventory, information, and cash. Allocation of new, or re-allocation –Throughput due to movement of of existing resource(s). product. Selection of new supplier(s), or de- –Capacity utilization. selection of existing ones. –Costs at various stages of product Changes in demand patterns for development life cycle. product(s) manufactured. –Lead time in product development. Changes in lead times for product –Batch and lot sizing. and / or process during its life –Process redesign. cycle in the supply chain. –Product development strategies. Changes in commitments within –Procurement and / or allocation of and between supply chain resources. members. –Strategic, tactical, and operational policies on the supply chain. 124 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • General guiding principles Supply chain is a System; Application of system hence General System design theory principles to Theory principles can be develop an axiomatic applied for its study system design for supply through an inter-disciplinary chain focus – Designing configurable – Managing complexity system architectures – Isomorphic frameworks through integrating FRs/DPs/PVs, Cs and – Formal theoretical flows, while maintaining reference models design axioms – System research and design 125 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • General guiding principles Supply chain is an organization System integration comprises system with a set of integration of information managerial issues at: resources and collaboration – Technical level based on common problems – Organizational level – Horizontal collaboration vs. – Institutional level hierarchical management – Shared understanding of Supply chain knowledge common problems and tasks representation is carried out – Distributed environment for through process modeling of linking diverse information its workflows systems – Modeling supply chain workflows Systematically capturing organization and problem – Capturing and organizing knowledge for workflow knowledge management – Ontology for supply chain – Delivering process / problem knowledge modeling knowledge to decision – Semantic Web Services for modeling tools knowledge share and reuse 126 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Problem solving Principles Strategies Scalability of system(s) Developing: Meta modeling of 1. Domain independent system(s) solution(s) [templates] at the macro level Coordination within 2. Capability models for and between application specific system(s) domain dependent Information sharing problems at micro level. within and between 3. Coordination models to system(s) integrate models developed in (1) and (2) 127 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Taxonomy of supply chain reconfiguration models SC Reconfiguration Model Types Domain Independent • SC System Taxonomy Model S-C STRUCTURAL MODEL Modeling Context • SC Organization Structure Model S-C ARCHITECTURE Domain Independent Methodological Constructs • SC Process Model REPRESENTATION MODEL • SC Ontology Model • SC Database Model S-C WASTE MANAGEMENT Domain Independent • SC Multi Agent Model MODELS Problem-Solving Context • SC Agreement Model S-C PROBLEM SPECIFIC Domain Dependent • SC Forecast Management Model MODELS Problem-Solving Context • SC Inventory Management Model • SC Capacity Planning Model 128 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Supply chain modeling system Supply chain modeling system Information Decision support modeling system system Process Information Knowledge Forecasting Simulation Modeling & Agent & Inventory Optimization Modeling modeling Modeling Management Information support system provides supply chain information support. Decision modeling system is used to investigate and solve supply chain management problems. 129 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Modeling overview Information Support System (ISS)
    • Motivation Make decision support system effective Develop systematic approaches for information modeling Use the best breed of available information technologies and resources Integrate information system with decision modeling system Support supply chain management activities Design information systems to meet supply chain management requirements Integrate processes and activities across the supply chain Integrate information resources across the supply chain 131 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Information support system Scope 132 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Information support system Thrust areas Information modeling System taxonomy – standardization of domain structure and content Problem taxonomy – systematic representation of supply chain managerial issues Ontology – Organization and problem knowledge conceptualization with formal models Information system architecture Knowledge intensive information system design Ontology utilization by information system components in both temporal dimensions (development and run-time) Knowledge portal and Ontology server design 133 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Information modeling framework 134 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • “System” behind system taxonomy Input: information, Resources Materials Agents Mechanisms Output: Designed System, Paradigm, Product, Service Mechanisms: Control, Action, Performance, Behavior, Program, Management, Strategy, Structure, and Input Output Feedback. Processes Processes: Information flow, Energy flow, Material flow, Transformation, Synthesis, Event. Objectives: Goals, Means. Agents: Owner, Role, Actor, and Customer. Objectives Environment Environment: Relevant systems, Dependencies, Constraints, Boundaries. Supply chain system taxonomy development objectives • System taxonomy provides standardization of terms and definition, thus ensuring shared vocabulary across the supply chain system domain. • System taxonomy also provides unified structure for a formal representation, ensuring that data and knowledge can be represented in a format consumable by supply chain system members’ software applications. 135 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Problem taxonomy Classification of supply chain problems Classification of problem solving methodologies for supply chain management Identification of problem requirements Problem model projection from system taxonomy 136 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Ontology modeling Ontology Ontology is a domain or problem knowledge formulated in the form of concepts and relationships with a set of axioms, used in problem reasoning algorithms, and implemented in a common language understandable by software development tools. Ontology conceptualization Components (1) Data, (2) Axioms (constraints, rules) and (3) Algorithms (problem solving methods) Stages (1) Business process modeling, (2) problem domain requirements identification, (3) analysis, (4) design, (5) implementation and evaluation 137 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • ISS Reference Model Proposition 1. System consists of things (entities) related to each other. Proposition 2. Supply Chain problems can be introduced as a combination of two formalisms, viz., problem object model and problem formal model . Proposition 3. To better serve the needs of problem solving tools and provide reusability of problem models, the information representing their content is captured at different levels of abstractions. Particularly, problem formal model is proposed to have two representation levels: generic and specific. Proposition 4. Relationships in Supply Chain problem domain can be classified into two types: vertical and horizontal. The former is for building domain structure. The latter is for linking outputs of some problems with inputs of others. 138 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • ISS Reference Model: notations I Notations related to system taxonomy WWi – Set of possible states of observation channels wwi S – System T – Thing symbolizing the elements of a system Notations related to specific problem representation R – Relationships among things of a system defined on T Ob – Object model bi – Backdrop Notations related to problem taxonomy Bi – Set of backdrop states bi Tw – Set of thing pertinent to a specific state of the SC SP – Specific problem model system vi – Variable that can be assigned to attribute ati for Rw – Set of relationships pertinent to a specific state of specific problems the SC system among things of a system state defined on Tw Vi – Set of possible values that variable vi may have wi – Observation channel for backdrop b PR– Problem representation i Wi – Set of possible states of channels w RV – Vertical representation i oi – Observation channel for attributes ati RH– Horizontal representation Õ – Relationship between object system and problem system Notations related to general problem representation W – Class instances of S for SC domain (general GP – Generic problem model representation of Wi ) ati – Attribute Ati – Set of instances of at attribute i Notations common for specific and general problem vvi – Variable that can be assigned to attribute ati for representations generic problems Ê – Relationship between specific and generic systems VVi – Set of possible values that variable ati may have ei – Relationship between Vi ,VVi wwi – Observation channels for vvi k j – Relationship Charu Chandra, University of Michigan - Dearborn between W j ,WW j 139 January 11, 2006
    • ISS Reference Model: notations II Notations for Ontology M ––– Data model for SC domain I – Ontological commitments. Functions interpreting characteristics into variables V – Set of variables Bc , Bw, B – Observation channels for defining variables, constraints, and algorithms respectively h – Set of Interpretation functions J Mw – Data model for SC problem C – Constraints on data O – Ontology model A – Set of axioms H – Algorithm or heuristics G – Set of equations 140 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • ISS Reference Model: System Taxonomy Thing System S = (T , R) Relationships T ⊆ ( I × O × E × A × F × M × P) I = {i1 , i2 ,..., in } I = {i : i _ has _ properties, I1 I 2 ,...} Input Output R( I , O) ⊆ {(i, o) : (i, o) ∈ I × O} Thing X Thing Y R( X , Y ) = {( x, y ) : y ∈ {Y }^ x = { X }^ ∀x y} 141 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • ISS Reference Model: Problem Taxonomy T = {Tw | w ∈ W } R = { Rw | w ∈ W } S w = (Tw , R w ) S = {S w | w ∈ W } Object model Relationship (GP,SP) ( Tw = Ob, GP, SP, Ê,Õ ) Relationship (Ob,SP) General problem Specific problem Ob = ({(ati , Ati ) | i ∈ N n },{(b j , B j ) | j ∈ N m }) GP = ({(vvi , VVi ) | i ∈ N n },{( ww j ,WW j ) | j ∈ N m }) SP = ({(vi ,Vi ) | i ∈ N n },{( w j , W j ) | j ∈ N m }) 142 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • ISS Reference Model: Problem Taxonomy Ê = ({(VVi , Vi , ei ) | i ∈ N n },{(WW j , W j , e j ) | j ∈ N m }) Õ = ({( Ati , Vi , oi ) | i ∈ N n },{( B j , W j , w j ) | j ∈ N m }) Vertical relationships Horizontal relationships Rw = ( RVw ,RH w ) RVw1,3 (Tw1 , Tw2 ) = {( x, y ) : y ∈ {Tw1}^ x = {Tw2 }^ ∀x y | w1, w2 ∈ W } RH w1,2 (Tw1 , Tw 2 ) = {( x, y ) : ( x, y ) ∈ {Tw1 × Tw 2 } | w1, w2 ∈ W } 143 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • ISS Reference Model: Ontology Data model Ontology O = ( M , C, H) Constraints Problem solving method C = (C → V ∪ BC ) H = ( H → M ∪ BH ) Observation channels I = (V → Tw ∪ Bw ) Ontological commitment M w = ( S w , I) 144 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • System taxonomy building – Upper level SC Sy s te m T a x on omy Pr oc e s s e s +Processes Age n ts +Flows O u tput SC A1 +Transformation SC _ Pr o duc ts F un c tio n s +Members +Synthesis +Output +Goals1 +Agents +SC Services +Means1 +GSCA +Objectives +Role +Management agents Inp u t +Operational agent Info r mation R es o ur c es +Input Me c ha n is m En v ir on me nt +Materials M an age me n t +Constraints +Cost R ela tio n s h ip_ Ma na gemen t +Financial +Lead Time Str a teg ies +Envorinment1 +Material Attributes Str u c tu r e +Organizational behavior +Requirement +Mechanisms +Market +Decisions 145 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • System taxonomy building– Mechanisms Mec hanis ms Management Dec is ions Pr oduc tioAndMater ials Sales AndMar k eting -Information:int -Trnasportation:int +Management -Location:int +Accounting -Inventory:int +HumanResource -Supply:int -Production:int Str ategies +Strategies +Policy +Decision making +Relationship Coordination Relations hip_ Management +Supply +SC Members Relationships +Optimization +Open Market Negogiation +Business Str uc tur e +Cooperation +Inventory +Product +Coordination +Manufacturing lots +SC Structure +Colleboration +PRoduction +Project +Relationship Type +Distribution +Components +Partnership 146 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Problem Ontology engineering Supply Chain Model Workflow Conceptual Model Ontology Model Process decomposition Process models Ontologies according to SCOR model Process type Process views Ontology Calculus Process category IDEF Process models SCML Process element UML Process models 147 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Supply chain workflow modeling Supply Chain Operations Reference (SCOR) modeling technique integrates concepts of business processes, benchmarking, and best practices into a cross- functional framework. Workflow or process modeling aims to represent processes specified in SCOR third level as a collection of tasks executed by various resources within a SC. Workflow modeling can be captured by using explicit models: IDEF methods and UML language. 148 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Problem classification Supply Chain SCOR Processes Plan Source Make Deliver Return Identify, Prioritize, & Select Final Select Final Schedule Route Shipment aggregate Production Suppliers and Suppliers and Production and Select Carrier Requirements negotiate Negotiate Activities Tasks and Activities Schedule Forecasting Plan Production Production with IDEF Determine Schedule Load Schedule Finishing Purchase General Capacity Capacity Requirements for Production plan Outsourcing 149 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Problem-solving methodology classification 150 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Problem model SteelSupplyChain Output Mechanism Agent 1 ProductionUnit -Stage:int Product 0..* -PUID:int Structure Resource -PUName:int -ProductType:int 1 0..* -ProductStatus:int -NumberofHours:int 1 1 -ProductSize:int 0..* ProductProduction ProductCost -ResourceName:int -ProductID:int -Shift:int 0..* 0..* Cost -ProductName:int 1 -ProductionTime:int -InventoryHoldingCost:i -TotalCapacity:int Transportation -ProductQuantity:int ProductStructure -SetupTime:int -ProcessingCost:int -FixedCost:int -Resource:int -ProductSetupCost:int -TransportationDate:in -Quantity:int -OtherCost:int -BreakDownDurationP1:int -ProductCost:int -TransportationTime:in -MaterialID:int -TransportationCost -BreakDownDurationP2:int -Period:int -TransportationType:in -ProductID:int 1 -BreakDownDurationType:int -Destination:int Demand 1..* -DeffectivenessP1:int -DeffectivenessP2:int ResourceAttributes -QuantityAccumulative:in -DeffectivenessType:int -DemandNet:int -BreakDownFrequencyP1:int -Capacity:int -Costomer:int -BreakDownFrequencyP2:int -ScheduledRate:int -Period:int -BreakDownFrequencyType:i -Yield:int -ProductID:int 151 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Conceptual framework: Ontology engineering Two ontology development Scenario narration specifications are proposed Informal knowledge representation For knowledge engineers: Axioms classification Situation and predicate calculus For Software engineers: XML Formal axioms with ontology calculus language specification Axioms Implementation with a computer language For each process item (process, task, or activity), ontology or a set of ontologies is designed consisting of three components: (1) model, (2) axioms defining constraints and rules held on data model, and (3) algorithms, which are step-by-step conditional descriptions of process flows. 152 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Ontologies • Ontology calculus is utilized for capturing and representing the dynamics of supply chain processes. Exist (demand , p r oduct ) Less( MaxInventory, CurrInventory ) Poss (do(( L * AVG + z * STD) = s ) > Il ) ≡ MakeOrder ( s − Il ) • A supply chain markup language (SCML) for presenting knowledge about SC is being proposed. 153 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Supply chain markup language schema 154 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Supply chain markup language schema 155 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Ontology server 156 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Ontology-driven information system components Management component is implemented through software agents. Interface component is implemented with Semantic Web and Semantic Web services. Ontology component is the library and the ontology server to support their capture, assembly, storage and dissemination. Gathering components is the same as in traditional IS, but with taxonomic links to common ontologies. Gathering Distributes DMBS, Vocabulary Repositories, and annotation et setera. Management Ontology Agents Ontology server Processing logic Semantics and inference engine and structure Web Services Interface Semantic Web 157 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Inventory model visualization 158 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Modeling overview Decision Modeling System
    • Decision modeling system Goal Objectives – The ultimate goal is to To develop a framework for establish both robust and integration of different supply chain configuration flexible supply chain models configuration by exploring – To elaborate a the problem from different comprehensive supply points of view chain configuration methodology – To improve understanding of issues in SC configuration through analysis of models – Validation through application 160 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Design principles Emphasis of common features Synergies between models Data integrity Modeling efficiency Reusability Openness 161 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Supply chain configuration problem Backbone for other Supply chain supply chain configuration problem management decisions in the context of an Existing models have enterprise-wide limited scope adequacy information systems – Uncertainty – Information systems engineering dimension – Dynamic factors Data availability – Interactions between Modeling effort decision making levels – Interactions between supply chain members 162 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Decision Modeling System Prescriptive supply Forecasting chain modeling YES Models use outputs Supply chain modeling data base Strategic level optimization from other models as their input data Operational planning Modeling Simulation parameters and constraints are Adjust parameters and iteratively updated constraints? Modeling data base is used as unified Final results source of information 163 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Supply chain configuration process 1. Identify objectives and main constraints; assess impact of expected configuration decisions 2. Gather required data 3. Establish a decision making plan (i.e., models used, situations to be evaluated, acceptance criteria) 4. Pre-selection. Reduce a number of alternatives 5. Selection. Establish the configuration 6. Sensitivity analysis. Return to step 5, if necessary 7. Acceptance of results 8. Implement the configuration decisions 9. Evaluate the configuration decisions implemented 164 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Multiple views Information system based on the common data model Generic Stochastic optimization programming Hybrid model model model Simulation model 165 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Results
    • Data model Structures data required for supply chain configuration Provides uniform source of data for different types of models – Reduction of model building efforts – Reduction of errors – Integrity of results 167 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Configuration models Generic MIP model Simulation model associated with the MIP model Stochastic programming model Hybrid model for modeling impact of dynamic factors 168 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Reconfigurable supply chain modeling software prototype Architecture Integration of prominent commercially available tools SAP R/3, ARIS, Data Process management modeling Together J, XML Spy, Data transfer Internet browser, Knowledge knowledge programs Application controls Commands analysis design Data Non-automatic link Microsoft Excel, ProModel (or LINGO, Optimization Decision modeling ARENA), shell Simulation 169 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Reconfigurable supply chain modeling software prototype 170 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Decision modeling system software template Modeling steps controls Example of modeling results Handling of modeling results Input data 171 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Some applications analyzed by this researcher Stamping supply chain Investments in flexible manufacturing facilities Affordable vehicle program Healthcare supply chain 172 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Some recent research topics explored by this researcher 1. A coordinated supply chain dynamic production planning model (integrated modeling, operational planning). 2. Reconfiguration of multi-stage production systems to support product customization using generic simulation models (simulation modeling and analysis). 3. Modeling floating supply chains (reconfigurable supply chains, supply chain modeling). 4. Application of multi-steps forecast to restrain the bullwhip effect (bullwhip effect in forecasting, inventory management). 5. Knowledge based lot-sizing (operational planning). 6. Relationships among lot-size planning parameters and environmental settings under stochastic demand (operational planning, forecasting management). 173 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Some recent research topics explored in this research 7. Supply chain coordination and information availability (supply chain coordination, information support). 8. Integrated approach to supply chain configuration (integrated modeling, supply chain configuration). 9. Elaborating process models for supply chain reconfiguration (process modeling). 10. Supply chain reconfiguration: Designing information support with system taxonomy principles (taxonomy modeling). 11. Supply chain reconfiguration: Domain and problem-solving ontology construction (ontology modeling). 12. Methodology and architectural framework of multiagent system for supply chain network management (agent modeling). 174 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Supply chain design checklist Requirements Definition Develop a Design Framework – Gather and Synthesize Domain Independent – Clearly outline System -- formulation, Knowledge deduction, interpretation and validation issues Industry / Company Product – Make Only General Inferences About the Design a System Architecture Problem – Propose a Problem-Solving Hypothesis Identify a few potential problem areas – Incorporate Design Components -- Structure, Control, – Select related problem(s) Optimization – For the Selected Problem(s), Identify – Perform Value Analysis for Process, Order and Scope Information Life-Cycles Objective – Identify Relationships between System Components Performance Metrics through -- A General Method of Inquiry & Problem- Process Flow Diagramming Solving Establishing Decision-Making Hierarchies Schedule Defining Controls – Write a Formal Document – Create an Integrated Framework with Flows and Decision Modes represented hierarchically – Review the Document with Project Team Controls defined within and between system and Industry / Company Sponsors components – Obtain Buy-in and Formal Approval of Life-cycles represented within the product and process Project Team and Industry / Company structures Sponsors 175 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Supply chain design checklist (continued) Analyze the System in a Specific Analyze the System in a Specific Domain Domain (Pre-Modeling Analysis) (Post-Modeling Analysis) – Develop a System Analysis Methodology – Perform Comparative Analysis “As-Is” System Analysis Check system fidelity w.r.t. “As-Is” – Identify system elements system environment Identify areas for improvement and approaches consistent with the problem Check system fidelity w.r.t. “To- Be” system environment Develop analysis criteria Establish analysis mode – Validate Domain Specificity w.r.t. – Conduct Pre-Modeling Analysis Performance Metrics Make specific inferences about the Business Scenarios problem Develop Model to Represent System Report Findings of Analysis Analysis – Document and report results w.r.t – Model specific to problem domain Requirements Document Represent design components -- – Offer problem specific and industry structure, control, optimization using / company generic conclusions problem inferences 176 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Module III: Military Supply Chains Issues and Perspectives
    • Presentation Outline Military Supply Chain: Background Military Supply Chain: General Problem Military Supply Chain: A Generic Configuration Commercial vs. Military Supply Chains Trends and Paradigms in Military Supply Chains Issues and Complexities in Military Supply Chains Military Supply Chains Taxonomy Potential Military Supply Chains Configuration? 178 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Background Military supply chains are designed primarily to support military operations – Military operations characterize events with national / international significance During peace time military consumes resources for preparedness for war time operations During peace time military supply chain is similar to a business supply chain – Both emphasize on minimizing – cost and lead time – Both target improving efficiency in operations – Both strive to adopt best practices 179 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Background (continued) Decision-making for military supply chains – Strategic decisions for war time are taken during peace time, considering level of threat and force capabilities Supply levels at various echelons Logistics goals and policies – Operational and tactical decisions are taken during war time, considering theatre environment, and specific scenarios Supplies to commit for theatre deployment Combat unit’s logistics 180 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Background (continued) Motivation for designing and optimizing military supply chains is planning, implementing, and controlling – Supplies – Resource mobilization – Procuring and Moving ordnance – Maintenance activities – Medical resources 181 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Background (continued) The aim of the Defense Logistics Agency is to provide an integrated defense logistics infrastructure by: – Streamlining the military’s supply chain system – Harnessing information technology – Cutting costs by adopting practices from the corporate world 182 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • General Problem Design a military supply chain that effectively responds to battlefield needs during a military operation in a specific theatre scenario by optimizing allocation of resources under constraints of force size and capability, theater environment, enemy size and capability, nature of threat, and doctrine 183 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • A Generic Configuration Corps Division Brigade Battalion Company Platoon Squad Theatre 184 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Commercial vs. Military Supply Chains Military supply chain by and large mimics a consumer goods supply chain – Manufacturers (to make products) – Warehouses (to store products) – Retail stores (general supply units) – Local stores (direct supply units) 185 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Commercial vs. Military Supply Chains Criterion Commercial Military Operation Long-term, routine Short-term, rare Uncertainty Demand, cost, lead time Allocation of resources Structure Generally stable (static) Unstable (dynamic) Cost Primary minimization Secondary Flow Sparse Massive Inventory type Intensive – simple Extensive - complex Modeling Approach Micro Macro Service Measures Relaxed – internal (profit Strict – customer-centric, maximization, cost mission critical (war readiness), minimization, lead time high reliability (almost 100%) minimization) 186 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Trends and Paradigms Velocity Management Trade mass for velocity Just-in-case vs. Just-in-time Lean operations Flexible operations Quick response (maximize response) 187 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Issues and Complexities Massive size and scope Defined by the term “Mission Critical” Extensive inventories for wide range of products comprising large number of SKU’s across varied classes of supply items Vast, complex, and unique distribution system Unique metrics, very different from commercial supply chains 188 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Military Supply Chains Taxonomy Three types of supply chains: – Fast, low volume chain [moves food, medicine, clothing, etc.] – Slow, large items transport and maintenance chain [moves weapons system] – Deployment chain [moves large number of troops and materials] Supply chain characteristics: – Forward pipeline – Reverse pipeline – Lateral pipeline 189 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • How to contact me? Charu Chandra, Ph.D. Associate Professor Industrial and Manufacturing Systems Engineering Department University of Michigan – Dearborn EC2230, 4901 Evergreen Road Dearborn, Michigan 48128-1491, USA Tel: 313-593-5258; Fax: 313-593-3692 E-mail: charu@umich.edu URL: http://www.engin.umd.umich.edu/~charu/ 190 January 11, 2006 Charu Chandra, University of Michigan - Dearborn
    • Defense Supply Chain A Logistics Lifecycle Management for TACOM’s Extended Enterprise A Short Workshop on Developing and Implementing Supply Chain 5th Annual U.S. Army Vetronics Institute Winter Workshop Series U.S. Army, TACOM, Warren, Michigan January 9-12, 2006 Presenter: Charu Chandra, Ph.D. Associate Professor Industrial and Manufacturing Systems Engineering Department The University of Michigan-Dearborn Engineering Complex 2230 4901 Evergreen Road, Dearborn, MI 48128-1491 Phone: 313-593-5258; Fax: 313-593-3692; E-mail: charu@umich.edu URL: http://www-personal.engin.umd.umich.edu/~charu/ January 11, 2006 Charu Chandra, The University of Michigan-Dearborn, MI
    • Industry Example For reference purposes only. Please do not distribute
    • Food Product Supply Chain Bench Markers -- Lead Time, Inventory and Information Sharing Share Harvest Plans Share Production Plans Share Logistics Plans Share Marketing Programs Plan Harvesting INp based Plan Production INo based Manage INm Logistics based on LTp on LTo on LTm INi Bid on LTp Bid on LTo INo Bid on LTm Manage INs based on LTs LTi INp LTo INm Bid on LTs INs LTp LTm LTs Inbound Outbound Operations Marketing Service Logistics Logistics Negotiate & Negotiate & commit LTp Negotiate & Negotiate & commit LTm commit LTs commit LTo Provide INs updates Provide INp updates Provide INm updates Provide INo updates Share Program Progress Share Plan Progress Share Plan Progress Share Plan Progress Raw Finished Distributed Marketed Serviced Materials Goods Goods Product Product Food Ingredients Processed Food Packaged Food Labeled Food Grocery Food S-C Members Farmer’s Coop ADM Kraft Pillsbury Krueger Sara Lee Smiths General Mills January 11, 2006 Charu Chandra, The University of Michigan-Dearborn, MI 3
    • Steel Product Supply Chain Bench Markers -- Batches, Process & Setup Times, Bottleneck Operations and Information Sharing Share Mining Plans Share Production Plans Plan Production Lots, Process and Plan Ore Mining INp based on LTp Setup Times for INo based on LTo INi Bid on LTp INp INo Bid on Batches and LTo LTi LTp LTo Inbound Logistics Operations Outbound Logistics Negotiate & commit LTp Negotiate & commit on Batches and LTo Provide INp updates Provide INo updates Share Plan Progress Share Plan Progress Raw Finished Steel Materials Goods Product Iron Ore Ingot / Fabricated / Packaged Steel Finished Steel Product S-C Members Iron Ore Mines Mini Mills Distributor January 11, 2006 Charu Chandra, The University of Michigan-Dearborn, MI 4
    • A Textile Industry Supply Chain Profile
    • Objective of the Supply Chain • Apply the philosophy of Synchronous manufacturing, which promotes harmony in the entire production processes to achieve goals of the supply chain. • The attempt is to coordinate all resources in the supply chain, so that they work in harmony or are “synchronized”. January 11, 2006 Charu Chandra, The University of Michigan-Dearborn, MI 6
    • Goals Improve Performance Financial Operational • Net Profit • Throughput • Return on Investment • Inventory Levels • Cash Flow • Expenses January 11, 2006 Charu Chandra, The University of Michigan-Dearborn, MI 7
    • Principle Balance product flow throughout the system Process Time (A) Process Time (B) • Rather than balancing capacities, the flow of product through the system should be balanced January 11, 2006 Charu Chandra, The University of Michigan-Dearborn, MI 8
    • Understanding the Problem - Key #1 Learn about the Product Fabric & Garment Other Materials Nylon Filament Yarn (DuPont) Insulation (3M Company) Nylon Supplex® Shell Grommets & Washers (Glenn Raven Mills, Inc) (Fastener Supply) Lortex, Inc. Labels (Artistic Identification Systems) Polartec® Body Lining (Malden Mills) Knit Cuffs (Green Mountain Knitting) Parka (Cascade West Sportswear, Inc) Zippers (YKK) Catalog Item Vendors Supplied (L.L. Bean) Pellon Pocket Lining January 11, 2006 Charu Chandra, The University of Michigan-Dearborn, MI Sleeve Lining 9 Waist Draw Cord
    • Learn about the Process Process Steps for Men’s Nylon Supplex® Parka January 11, 2006 Charu Chandra, The University of Michigan-Dearborn, MI 10
    • Level 0 Decompose Processes 1 3 4 5 7 2 6 8 Storage at Yarn Cotton Fiber Yarn Packaged Yarn Greige Fabric Storage at Yarn Facility Processing Yarn Warehouse Mill Greige Fabric Mill 10 11 16 9 12 13 14 15 Process Yarn Greige Supples Transportation Storage at Greige Fabric Dyeing/ Supplex Fabric Fabric of Greige Finish/Dye Mill Processing Finishing Rolls Fabric 17 20 18 19 21 22 Customer Storage at Apparel Packaging Retail Storage at Cut/Sew Facility Manufacture and Shipping Distribution Retail Stores Key: denotes transportation activity denotes storage denotes a process January 11, 2006 denotes an Chandra, The University of Michigan-Dearborn, MI Charu end-product 11
    • Understand Business & Information Flow Customer P.O. Marketing Activity Account Create Forecasts Sales Forecast from Customers Sales Forecast & Customer P.O. Inventory Status Production Planning Weekly Updated RM Rolling Forecast Activity & RM PO sent to Texas Capacity Plan Prepare Historical Production Production Plan Production Process Production Design Plan Evaluate Production Process RM Availability Forecast & RM Shipping Notice from Texas A-B-C Priority Configuration Production Schedule Financial Data Production Marketing Activity Process Production Planning Determine Configuration Activity Reallocation Needs RM Shipping Invoice Production Scheduling across Accounts Via Product Wheel if Necessary Plant Operations Capacity Allocation Final Capacity Requirements Allocation & Scheduling Execute Production Scheduled Scheduled "Break-Ins" "Break-Ins" Organization & Management Fill & Warp Shipped Determine the Need for to GT and FFD Respectively "Break-In" Scheduling Glen Touch January 11, 2006 Charu Chandra, The University of Michigan-Dearborn, MI 12
    • Understand the Activity Flow Activity Sequence Produce Polymer via CP Technology TO (6h) DP.3 Input DP.1 DP.4 DP.5 DP.6 DP.7 DP.8 DP.9 DP.10 TO DP.2 DP.11 Unload Overhaul Denier Reactor & Shelve Production Evaporator Flasher Finisher Store CP Machines Change Vessel HMD & AA Set-up 10 days 3-8 h 5-6 h DP.12 DP.13 DP.14 TO DP.16 TO DP.18 TO DP.20 TO DP.15 DP.17 Inspect DP.19 Pack into DP.21 Extrude Draw Wind Strip Quads Note: Documentation 72 h indicates DP.12 - DP..22 requires 6-8 h. DP.25 DP.27 DP.30 DP.22 DP.23 TO DP.26 DP.28 DP.31 DP.29 Output Wrap DP.24 Load Ship Flat Shelve Cure Yarn Store Yarn Unshelve Ship Fill & Yarn Flat Yarn Yarn 1 day 5 days & Load Warp Yarn 6-8 h 1 day Key: End Product Move TO Inspection Set-Up Transportation Material Process/ Storage Delay Handling Processing Inspection January 11, 2006 Charu Chandra, The University of Michigan-Dearborn, MI 13
    • Understand the Process Flow Input Receiving Polymer Production Pre-Yarn Production DP.1 - DP.2 DP.3 - DP.10 DP.11 - DP.16 Inspect & Package Ship & Cure/Store Shipping DP.17 - DP.23 DP.24 - DP.29 DP.30 - DP.31 Output January 11, 2006 Charu Chandra, The University of Michigan-Dearborn, MI 14
    • Understanding the Problem - Key #2 Understand the Manufacturing Diversity Flexibility High Very Jumbled Flow Retailer - Non Manufacturing (Job-Shop) Process Flow Less Jumbled Flow APPAREL (Batching) TEXTILE Machine-paced Line Flow FIBER FIBER FIBER Continuous Output Rigid Flow Low (Assembly Line) Volume Custom Low Volume of High Volume of Very High Volume Products Many Products Several Major Commodity Products Low Product-Mix High January 11, 2006 Charu Chandra, The University of Michigan-Dearborn, MI 15
    • Understanding the Problem - Key #3 Find out about the Logistics Note: Supply Chain Length is ~ 9,500 highway miles Cascade West Jacket L.L. Bean ® Supplex ® Polartec ® Malden Mills Yarn DuPont Glen Raven Mills Supply Chain Retail Distribution Supply Chain Members Major Population Centers January 11, 2006 Charu Chandra, The University of Michigan-Dearborn, MI 16
    • Analyzing the Problem - Step #1 Breakdown of Productive System Manufacturing Time Line for the Parka Supply-Chain Total Time of Operation for Parka manufacturing under existing system (295 days) Total Ineffective Time in Total Work Content in Parka manufacturing Parka manufacturing Work Content in Parka Detailed manufacturing Work Content in Contribution of due to Parka manufacturing time due to lack Analysis defective - due to inefficient of productivity, Required product design, methods, processes, Contribution of time unskilled work to bring out and product or material flow, setup, due to inefficient force, etc. in Minimum Work Content plant layout, etc. these details raw material logistics in Parka Parka in Parka manufacturing specifications manufacturing manufacturing 1st Order Waste 2nd Order Waste 3rd Order Waste 4th Order Waste I II III IV Goal of Methods Engineering Opportunities for problem solving by Methods Engineering (56 days) (239 days) January 11, 2006 Charu Chandra, The University of Michigan-Dearborn, MI 17
    • Analyzing the Problem - Step #2 Identifying Bottleneck Locations 100 90 Legend 80 1.x Yarn Facility 70 2.x Texturizing 60 Wait 3.x Griege Fabric Mill 50 Time, 4.x Finish/Dye Mill 40 5.x Cut/Sew Facility Days 30 6.x Retail 20 10 0 Receiving Warehouse Warehouse Warehouse Warehouse Inventory Inventory Ship to Cut Cut Fabric Ship to Reserve Spread & Retail Storage Ready Storage Storage Storage Store in 5.8 & Sew 6.2 6.3 3.1 5.2 4.6 5.1 5.7 3.8 2.5
    • Analyzing the Problem - Step #3 Identifying Bottleneck Activities Activity Breakdown for Supply Chain 70 Delay 60 Setup 50 Inspect Number of Activities Package 40 Storage Move/Transport 30 Material Process 20 10 0 LL Bean DuPont Malden Raven Cascade Glen West Supply Chain Member January 11, 2006 Charu Chandra, The University of Michigan-Dearborn, MI 19
    • Problem Solving Technique Convert Bottleneck Activity to Nonbottleneck Activity Drum, Buffer, Rope Approach to Synchronization Bottleneck (Drum) A B C D E F Market Inventory Communication buffer (rope) (time buffer) January 11, 2006 Charu Chandra, The University of Michigan-Dearborn, MI 20
    • Organizing Processes Facilitates Synchronization Provideri Provideri+1 Pipeline Company Process Flow Model Raw Finished Receiving Materials Manufacturing Shipping Customeri Goods Activity Flow Model WIP Activity 1 WIP Activity 2 Task Flow Model WIP WIP Task 1 Task 2 Material Flows Raw Inventories Transformations January 11, 2006 Materials Charu Chandra, The University of Michigan-Dearborn, MI 21
    • Information Sharing is Key to Synchronizing Advertising, Catalog Stock Marketing Forecast Capacity Level Report Report Surveys Report Customer Order, Sales History Customer Payment Aggregated Orders Customer Sales Production Planning Sales History Order Status, Procurement Production Shipping Shipping Sales Invoice Schedule Schedule Schedule Invoice Vendors Procurement Receiving Production Shipping Purchase Order, Payment Shipping Shipping Shipping Shipping Notice Notice Notice Invoice January 11, 2006 Charu Chandra, The University of Michigan-Dearborn, MI 22
    • System Performance Simulation Dr Bottleneck um Resource 1 Resource 2 Machine 4 a Resource # T U S T U S T U S use # use # use # T U S b demand A D down A D down A D down u u u change A down change change D Count Buffer # V 1 2 Rope (Constrained Feedback) January 11, 2006 Charu Chandra, The University of Michigan-Dearborn, MI 23
    • Benefits Major Savings in Production Cycle Time • Setup time • Process time • Queue time • Wait time • Idle time January 11, 2006 Charu Chandra, The University of Michigan-Dearborn, MI 24
    • Benefits All Functions in the Manufacturing Enterprise • Marketing – discourages holding large amounts of finished goods inventory • Purchasing – discourages placing large purchase orders that on the surface appear to take advantage of quantity discounts • Manufacturing – discourages large work in process and producing earlier than needed January 11, 2006 Charu Chandra, The University of Michigan-Dearborn, MI 25
    • Classification of Supply-Chain Synchronized Models Push System Bottleneck (Control) Fini- Raw shed Matl. A B C D E F Goods Customer Synchronous Flow System Bottleneck (Control) Fini- Raw shed Matl. A B C D E F Goods Customer Inventory buffer (time buffer) Rope (Information) Pull System Raw Fini- Matl. 1 2 ... n shed Goods Customer Pull Pull Material Flow Information Flow January 11, 2006 Charu Chandra, The University of Michigan-Dearborn, MI 26
    • Comparison of Objectives in Synchronized Models Metrics Metrics Metrics Utilization Utilization Utilization Production Lead Time Production Lead Time Production Lead Time Time Time Time (Supply) Push (Push-Pull) Synchronous (Demand) Pull Objectives: Objectives: Objectives: • Control throughput • Control throughput • Control WIP Inventory • Measure WIP Inventory • Control WIP Inventory • Measure throughput Source: Dan L. Shunk. Integrated Process Design and Development, 1992.The University of Michigan-Dearborn, MI Illinois. January 11, 2006 Charu Chandra, Business One Irwin, Homewood, 27
    • Comparing Synchronous Manufacturing to MRP (or the Push philosophy) • MRP uses backward scheduling • Synchronous manufacturing uses forward scheduling January 11, 2006 Charu Chandra, The University of Michigan-Dearborn, MI 28
    • Comparing Synchronous Manufacturing to JIT (or the Pull philosophy) • JIT is limited to repetitive manufacturing • JIT requires a stable production level • JIT does not allow very much flexibility in the products produced January 11, 2006 Charu Chandra, The University of Michigan-Dearborn, MI 29
    • Comparing Synchronous Manufacturing to JIT (or the Pull philosophy) • JIT requires work in process when used with Kanban so that there is "something to pull" • Vendors need to be located nearby because the system depends on smaller, more frequent deliveries January 11, 2006 Charu Chandra, The University of Michigan-Dearborn, MI 30
    • Defense Supply Chain A Logistics Lifecycle Management for TACOM’s Extended Enterprise A Short Workshop on Developing and Implementing Supply Chain 5th Annual U.S. Army Vetronics Institute Winter Workshop Series U.S. Army, TACOM, Warren, Michigan January 9-12, 2006 Presenter: Charu Chandra, Ph.D. Associate Professor Industrial and Manufacturing Systems Engineering Department The University of Michigan-Dearborn Engineering Complex 2230 4901 Evergreen Road, Dearborn, MI 48128-1491 Phone: 313-593-5258; Fax: 313-593-3692; E-mail: charu@umich.edu URL: http://www-personal.engin.umd.umich.edu/~charu/ January 11, 2006 Charu Chandra, University of Michigan - Dearborn 1
    • Supply Chain Logistics Configuration and Supportive Information Technology Examples
    • Supply Chain Configuration Unit P1 Resource R1 Resource R2 Unit P2 Resource R3 Resource R4 Process Process Process Process Process Process Process Process Pr1 Pr2 Pr3 Pr4 Pr1 Pr3 Pr4 Pr5 Process Process Process Process Process Process Pr2 Pr3 Pr5 Pr2 Pr3 Pr5 Time t = t1 Time t = t2 Process Process Process Pr2 Pr3 Pr5 Product January 11, 2006 Charu Chandra, University of Michigan - Dearborn 3
    • Supply Chain Network Configuration Examples Parallel Sequential Distributed January 11, 2006 Charu Chandra, University of Michigan - Dearborn 4
    • Product structure Car Manufacturing Components – Body Under Carriage – Interior – Wheels – Under Carriage – Front and Rear Axles – Power Train – Front and Rear Shock Absorbers Body Interior • 14 Preformed Tubes – Dashboard • 5 Exterior Sheets – Front Seats – Rear Seat Power Train – Engine – Transmission January 11, 2006 Charu Chandra, University of Michigan - Dearborn – Cardan Shaft 5
    • Car Manufacturing Components 14 Preformed Tubes 5 Exterior Sheets Body Dashboard 2 Front seats Rear Seat Interior Car 2 Front Axles Under 2 Rear Axles Carriage 4 Wheels 2 Front Shock Absorbers Power Train 2 Rear Shock Absorbers Engine Transmission January 11, 2006 Charu Chandra, University of Michigan - Dearborn Cardan Shaft 6
    • Parallel Distributed Production Model of a Car Tier 3 Tier 2 Tier 1 Tier 0 Dashboard Interior Assembly Seat Manufacturer Engine Power Train Assembly Transmission Body Assembly Shaft / Axles Under Carriage Exterior Assembly Manufacturer Wheel Manufacturer Exterior Sheet Forming Tubes Manufacturer Shock Absorber Manufacturer Manufacturer January 11, 2006 Charu Chandra, University of Michigan - Dearborn 7
    • Example Automotive Supply Chain Raw Material Tier 2 Tier 1 Manufacturer Dealers Consumers Suppliers Suppliers Suppliers Dashboard Manufacturer Interior Assembler Final Car Transmission Assembler Manufacturer Power Train Assembler Engine Manufacturer Demand (Information) Flow January 11, 2006 Charu Chandra, University of Michigan - Dearborn 8 Product (Materials) Flow
    • Supply chain configurations Sequential Distributed Production Shaft / Axles Manufacturer Model - manufacturing mainly is Dashboard Under Carriage done in-house and sequentially Manufacturer Wheel Assembly Manufacturer Seat Engine Power Train Manufacturer Shock Abs. Manufacturer Manufacturer Assembly Engine Transmission Form. Tubes Dashboard Body / Interior Manufacturer Manufacturer Manufacturer Manufacturer Assembly Transmission Body Seat Exterior Manufacturer Assembly Manufacturer Assembly Shaft / Axles Exterior Exterior Sheet Manufacturer Assembly Manufacturer Wheel Exterior Sheet Manufacturer Manufacturer Dashboard Shock Abs. Manufacturer Manufacturer Seat Parallel Distributed Production Form. Tubes Manufacturer Interior Model - manufacturing is mainly Manufacturer Assembly Engine done in-house and is parallel Manufacturer One-Stage Distributed Production Model - Body Transmission Power Train manufacturing is highly outsourced Manufacturer Assembly Assembly Shaft / Axles Exterior Manufacturer Under Carriage Assembly Wheel Assembly Exterior Sheet Manufacturer Manufacturer Shock Abs. Form. Tubes January 11, 2006 Manufacturer Manufacturer Charu Chandra, University of Michigan - Dearborn 9
    • Process Data Model for Supply Chain Network Configuration A process model representation for an automotive SC utilizing a car with four main components Body, Interior, Under Carriage, Power Train, and several constructive elements within these components. In order to manufacture this car, various automotive SC production models may be created; different configurations are being evaluated using experimentation One-Stage Distributed Production Model Parallel Distributed Sequential Distributed Production Model Production Model January 11, 2006 Charu Chandra, University of Michigan - Dearborn 10
    • Supply Chain Taxonomy Finished Product System L(0) Supply Production Distribution Information Subsystem L(0) Production Attributes Distribution Attributes IT Attributes Taxa/Classification Supply Attributes Tier 1 Supplier Dealer System L(1) Supply Production Distribution Customers Subsystem Supply Attributes Production Attributes Distribution Attributes Dealer’s Attributes Customer's Attributes Taxa/Classification System L(2) Tier 2 Supplier Supply Production Distribution Subsystem L(2) Supply Attributes Production Attributes Distribution Attributes Taxa Raw Material Supplier January 11, 2006 Charu Chandra, University of Michigan - Dearborn 11
    • Taxonomic Representation of Supply Chain Member Supply Attributes Production Attributes Distribution Attributes Information SC Member specific costs •Backorder Penalty • Component Inventory • Work Process Inventory • Finished Product Inventory • Demand Planning • Product coordination • Product Coordination • Distribution configuration •Cost of Resources •SC Membership specific costs •Costs of resources utilization •Inventory Handling Component Inventory Work In Process Inventory Finished Prod Inventory •Lead Time •Customer Demand •Lead Time •Lead Time •Number/quantity of component •Product costs •Customer Demand •Trans Costs per Unit •Holding costs •Number of Product •Trans Costs per Batch •Transportation costs •Transport. Costs •Product costs •Assembly costs per Unit •Product costs •Holding costs •Assembly costs per Batch •Holding costs •Set up time •Set up time •Service level •Ordering Policy •Delivery Policy •Buying price •Selling Price Product coordination •Assembly Distribution configuration •Fixed Capacity costs •Number of warehousing •Operating Time unit •location System Components •Supplier Integration •size •Lead time •space of Product •Demand Planning •Batch/lots size •Transport resources •Capacity Utilization •Capacity •Assembly Policy January 11, 2006 Charu Chandra, University of Michigan - Dearborn 12
    • Prototypic Implementation of the Supply Chain Object Model 1:∞ CLASS Class_ID 1:∞ • Utilizes flexible data Class_Name Class_Coment modeling approach OBJECT ATTRIBUTE 1:∞ 1:∞ Attribute_ID based on the Design Object_ID Class_ID Object_Name Class_ID Attribute_Name of Structured Objects (DESO) architecture. Object_Coment Attribute_Coment VALUE Value_ID Attribute_ID 1:∞ • Allows modeling Object_ID Value_Value any object structure HISTORICAL_VALUE Value_ID with limited number HValue_Time HValue_Value of relations (tables). January 11, 2006 Charu Chandra, University of Michigan - Dearborn 13
    • A Supply Chain Dynamic Constraints Network Generic Object Data Model Supply Chain Product Production Unit Component Process PU – Product PU–Product–PU Generic Activity HISTORICAL VALUES HISTORICAL_VALUE 01.01.01 $1,500 Resource 02.01.01 $1,520 03.01.01 $1,550 CLASSES CLASS OBJECT OBJECTS VALUES VALUE Production Unit Power Train Manufacturer (PTM) Power Train Manufacturer Product Transmission PU - Product Relation PTM - Transmission Transmission T-234 ATTRIBUTE ATTRIBUTES PTM Producer Product Price Transmission January 11, 2006 Product Name Charu Chandra, University of Michigan - Dearborn Producer Name 14
    • Supply Chain Network Components Product Network Flow Organization Structure structure coordination Definition definition Inventory Manufacturing Common Data Model Management Capacity Forecasting Control Management January 11, 2006 Charu Chandra, University of Michigan - Dearborn 15
    • An Enterprise Information Management System Advertising, Catalog Stock Marketing Forecast Capacity Level Report Report Surveys Report Customer Order, Sales History Customer Payment Aggregated Orders Customer Sales Production Planning Sales History Order Status, Procurement Production Shipping Shipping Sales Invoice Schedule Schedule Schedule Invoice Vendors Procurement Receiving Production Shipping Purchase Order, Payment Shipping Shipping Shipping Shipping Notice Notice Notice Invoice January 11, 2006 Charu Chandra, University of Michigan - Dearborn 16
    • SAP R/3 Integration Model SD FA Sales & Financial Distribution Accounting MM Materials CO Management Controlling PP AM Production Fixed Asset Planning Management R/3 Client / Server QM Quality ABAP / 4 PS Project Management System PM Plant WF Maintenance Workflow HR IS Human Industry Resources Solutions January 11, 2006 Charu Chandra, University of Michigan - Dearborn 17
    • Logistics Sub-Modules • Sales and Distribution • Production Planning • Materials Management • Quality Management • Plant Maintenance • Logistics Information System • Project System • Product Data Management January 11, 2006 Charu Chandra, University of Michigan - Dearborn 18