Modeling agile demand-supply network of industrial investment goods delivery process

Transcription

1 Modeling agile demand-supply network of industrial investment goods delivery process Petri Helo & Bulcsu Szekely University of Vaasa, Logistics Research Group, PO Box 700, FIN Vaasa, Finland. ICIL Montevideo, Uruguay.

2 Contents The agility of a logistics chain may be defined as the ability to operate under uncertainty whilst maintaining stable level of productivity (Yusuf, Gunasekaran, Adeleye & Sivayoganathan 2004) New tools are needed: (1) Design of supply-demand network architecture, (2) Methods for screening the current constraints and risks in the network, (3) Tools for optimization of supply network in terms of inventory and capacity levels, and (4) Measures connected to financial performance of fulfillment process. Demonstrate some potential approaches in software design for SCM.

3 Background Truly global network Multi-site manufacturing Transportation Capacity management Requirements for: On-time delivery Low inventory Fast delivery Decreased cost of capital Steel Copper Semiconductors. Component manufacturers Component Vietnam Component Finland Component China Component China Final assembly Assy Sweden Assy Finland Assy US Assy Switzerland Distributors Sales HK Warehouse Germany Warehouse China Site installation Site China Project China Customer Middle-East Spare-parts Asia Figure 1. An example of Supply-Demand network infrastructure. Research task: Determine the right level of inventory and service in each stage of the network.

4 SCM Tools in Inventory Optimization Users Invest In Inventory Optimization Tools Solutions, such as advanced planning, distributed order fulfillment, and ERP, have tackled the problem of managing inventory from various angles. Yet user companies in industries like high tech and consumer durables are investing in a newer breed of SCM tools: optimization tools that determine the right levels of inventory to meet commitments to customers while minimizing cost. ISVs continue to differentiate themselves based on functionality - stochastic versus deterministic, one- versus multistage - but will quickly need to align themselves with large SCM providers. Noha Tohamy with Liz Herbert (Forrester Research - March 30, 2004)

5 Enterprise Resource Planning Configurator Customer orders Forecasts Production plan Order entry and promise Distribution Requirements Planning Final Assembly Schedule Master Production Schedule (MPS) Rough-cut capacity planning Product structures records (BOM) Material Requirements Planning Capacity Requirements Planning Inventory Status Records Work orders Purchase orders HR / Payroll Invoices General ledger Accounting

6 Time horizon of logistics planning

7 APS systems in SCM A P S s y s t e m s Supply chain network planning for procurement, production, distribution and sales Master Planning Schedule for procurement, production and distribution Purchasing & Material Requirements Planning LONG TERM LEVEL Production planning and Scheduling SHORT TERM LEVEL Distribution planning Transport planning Demand planning & CTP Demand planning and fulfilment & ATP EAI software solution Transactional IT - systems: ERP - software SCE software solutions Figure 1. APS software modules as an analytical IT system (adapted from Stadtler & Kilger 2002, 99)

8 SCM Software structure Structures of many other software packages follow the same structure: (Buxmann König 2000, 100): (1) The planning modules consist procedures for Demand Planning, Supply Network Planning, Production planning and Detailed Scheduling, and Available to Promise. (2) User interface The Supply Chain Cockpit is giving the chance of visualizing and controlling the structure of logistics chains. The UI facilitates the graphical representation of networks of suppliers, production sites, facilities, distribution centers, customers, transshipment locations. Additionally, by using Alert Monitor engine it is possible to track supply chain processes and identify event-initiating problems and bottlenecks. (3) Solver is an optimization engine that employs various algorithms and solution procedures for solving supply chain problems. This includes forecast modeling techniques such as exponential smoothing and regression analysis are built in for demand planning, but also branch & bound procedures, and genetic algorithms are available for production and distribution planning. (4) Simulation of changes is enabled by an architecture for computing and data-intensive applications that makes it possible simulations, planning and optimization activities to be in real time.

9 Real time SCM: uses for configurators in ERP context Customer Inquiry Offer Order Confirmation Salesperson - configuration Product configuration Salesperson - Offer Offer Pricing Production - Order Order Management - reporting Offer DB Order DB

10 Configuration approach Figure 2. Configurator view for the sales stage. The system generates a type code and bill of materials.

11 Maintaining product and routing rules in engine

12 Typecodes as tools between sales and production Brand code Size Option slots Brand code Modification parameter Secondary parameter XY PA 1300 X Y F F 3002 XY FR FR0123 Y F F 3002 Desc. param. 1-4 Secondary module Option cards / selections Product family Main module / frame Figure 3. Typecode conversion from sales to production.

13 BOM Creation XYZ 124 AA BA Frame XCZ AM AA Component X Component A Kit 12 Option 233 Configuration BA Figure 4. Bill of materials structure created based on type code generated by a feature configuration.

14 Customer demand query Parameters Quantity Requested delivery date Sales configuration Type code for finished product Bill-of-Materials Routings Cost structure Creation of Bill-of-Material Type code Quantity Quantity Resources required Inventory status look-up Capacity check-up Purchase and intransit check up Type code Quantity Requested date Request for supplier capacity Schedule for component delivery Capable to Promise Figure 5. Capable to promise request flow chart.

15 Order decoupling point and configurators Production Planning Design in Process Raw materials Work in Process Finished products Order decoupling point Configurations Engineering configuration Feature configuration DIP RM WIP FP Engineer-to-order Make-to-order Assembly-toorder Make-to-Stock X X X X X BOM X X X configuration Selector X X Figure 6. The order de-coupling point defines the production type (Bertrand et al. 1990b).

16 Conclusions Management of configurable items and BOMs is an important feature of many industrial products. A parameterization of a customizable product defines the parts required and the processing times for each stages in capacity. Many current planning tools are still concentrating on standard items instead of configurable products and distributed production networks. (Frank 2004). Re-configurability of network needs definition of rules. The network is not stable. In practice this means automated answers for questions like: who should make the part and in which conditions? (Verwijmeren 2004; Chandra, & Kumar 2001) More intelligent systems are needed for large networks. Agents and semantic web may be seen as key technologies for creating standardized platforms for standardized communication over the internet. Intelligent systems use more complex rules for automated decision making an may have learning capabilities as well. The practical example could be combining customer behaviour analysis from retail data mining and combine this to demand management. (Trappey & Trappey 2004, (Lau, Wong, Pun, & Chin 2003) Future APS systems need to be dynamic in many senses. Real-time information should be combined to long-time range decisions such as product ramp-up and rampdown, capacity installation plans etc.

17 University of Vaasa, Logistics Systems Research Group (2005)

18 120 University of Vaasa, Logistics Systems Research Group (2005) Merchandising stock Pipeline stock Supply safety stock Demand safety stock Cycle stock 20 0 Jan Feb Mar Apr May Jun Jul Aug Component manufacturers Final assembly Distributors Site installation Steel Component Vietnam Assy Sweden Sales HK Site China Component Finland Assy Finland Warehouse Germany Project China Copper Component China Assy US Customer Middle-East Semiconductors. Component China Assy Switzerland Warehouse China Spare-parts Asia Figure 1. An example of Supply-Demand network infrastructure.

19 University of Vaasa, Logistics Systems Research Group (2005)

20 ASDN Approach for network design