Brian Downs, Supply Chain Advisor, Aspen Technology Mike Self, Director of IT, Swift & Company

1. Tales from the Cutting Edge: An LP-based Multi-process, Multi-plant CTP Application for the Beef IndustryApril 4, 2005 Anaheim, California Brian Downs, Supply Chain Advisor, Aspen TechnologyMike Self, Director of IT, Swift & Company

2. Slaughter and Fabrication Operations • Five beef plants • Three fed cattle plants • Two lean cattle plants • Four primary processes in each facility • Boxed beef (steaks and roasts) • Offal (variety meats) • Trimmings • Ground beef • $8 billion in annual sales • Margins are typically less that 1% • Product shelf life is extremely short

3. Volatile Markets at Both Ends of the Supply Chain $ CattleProcurement Slaughter CarcassInventory $ $ USDA Grading Fabrication FGI Customer Sales $ $ $ $ $

4. The Margin Opportunity • The potential of the firm to be profitable is determined by its ability to plan and execute its sales and production processes to • Commit only to customer orders that can be delivered complete and on time • Optimally allocate the available cattle to finished goods production to satisfy those orders with the highest possible margin. • Margins in the beef industry are typically 0.5%-1.0% • Poor execution immediately puts profitability at risk.

5. The Beef Industry Supply Chain • Tremendously high cost of raw material (in excess of 85%) • Extreme variability in manufacturing • Disassembly operations or ‘reverse bills-of-material’ • Cold chain management and rapid spoilage rates • High volume and brutal velocity • Commodity driven prices with complex opportunities to differentiate • Disproportionately more complex data than price/margin indicates • Difficult logistics with high risk of loss

6. Business Challenges • Our business model is defined by a complex set of operational and decision-making parameters in a fluid, often volatile, marketplace. • The nature of our disassembly processes, coupled with high volumes, cold-chain management, and high raw material costs creates extreme variability in margin performance. • Historically, the systems and tools used to facilitate decision-making are were unable to accommodate the breadth and depth of data permutations needed for transparency into alternatives. • Decision-making was driven by anecdotal knowledge rather than facts derived from information driven by data.

7. Primal Cuts

8. The Carcass Disaggregation Problem • After slaughtering, each animal is chilled and then graded for both quality and yield. • Cattle may grade-out differently than planned. • Each side yields seven primal cuts. • Each primal is disaggregated into pieces which are cryo-vacuum packed and boxed. • Each box may contain from one to fifteen or more pieces. • This is what you see at the market.

9. The Carcass Disaggregation Problem • Better market for some products than others • Two inch bone-in ribeyes have high demand, and therefore a high margin. • Less desirable cuts have a lower demand, lower margins and may often be discounted to move inventory. • Every part of the animal must be sold in order for the business to be profitable.

10. Path: 3222 - Commodity Choice 30 PECT WCNO MTNB BICR PECT CHBR CSQU BICR MTNB BICR CART C2PC BKST PECT CHSC CSQU BICR NKMT STRS CART PECT BBSW NOSL BBSW BBSW BCSR CFMT CHSI BCSR CFMT BBSW BCSR CFMT BCSR CFMT CHCD CHSC CHRL SLCD INCD SWCD 1CHR CSQU NKMT STCE CSQU BBFC SCCB CDFL CDSH Each fabrication operation produces a primary product and several co-products. The Carcass Disaggregation Problem For each primal, there are many possible fabrication pathways. At Swift & Company, the schedule must consider over 8000 possible production operations.

11. The Carcass Disaggregation Problem • Every carcass is completely processed during a single shift. • Approximately 2500 head per shift • Between 18,000-25,000 head of cattle per week per plant • The business problem is to make the proper mix of products that will • Satisfy the particular combination of customer demand and product age requirements • Return the highest margins • 600+ products • 15,000 ship-to locations

12. ATP at Swift As It Was… • Legacy systems could only move down the disaggregation tree. • Given a planned production schedule, the legacy system at Swift could only provide ATP functionality. • The system could only alter their planned production by adding further disaggregation to their fabrication schedules. • Customer sales representatives were in the position of selling only the product that was available according to a fixed schedule. • Swift lacked the ability to look back up the disaggregation tree and identify an opportunity to fill a customer order by changing the fabrication schedule using an entirely different branch of the tree.

13. Path: 3222 - Commodity Choice 30 PECT WCNO MTNB BICR PECT CHBR CSQU BICR MTNB BICR CART C2PC BKST PECT CHSC CSQU BICR NKMT STRS CART PECT BBSW NOSL BBSW BBSW BCSR CFMT CHSI BCSR CFMT BBSW BCSR CFMT BCSR CFMT CHCD CHSC CHRL SLCD INCD SWCD 1CHR CSQU NKMT STCE CSQU BBFC SCCB 100 CDFL CDSH 100 …without Product Optimization… Orders and balance items were used to determine a production schedule. 500 200 100 200 But an order requiring 300 pieces of WCNO would have come up 100 pieces short because the system could not move up the tree structure. What was the likely outcome in this case? Further disaggregation was possible, e.g., 100 pieces of CHRL could be taken apart to fill an order requiring 100 pieces of 1CHR, leaving 100 pieces of CHRL to sell.

14. …Contributed to Poor Margin Performance • Frequent discounting to move unwanted or unordered product • Shortages when variability in carcass grades and yield produced deviations from the planned schedule • Customer service problems • Took orders they could not ship, and the customer had to be contacted and given a new ship date. • Modified the production schedule almost constantly to expedite orders. • Sales history did not reflect actual demand • Impossible to forecast

15. Prior Business Model: ‘Production-Push’ Produce What The Plants Prefer Buy The Cattle ...purchase raw material before knowing what customers want then make what you can and sell at whatever price moves the product... Sell What We Made Show Product Inventory

16. Other Critical Components of the Business Problem • Modeling the age of the inventory is essential, as each customer specifies a maximum acceptable age upon delivery. • Limits on the production of certain pieces must be enforced to maintain a certain chain speed. • Warehouses for fresh and frozen products must be included in the model. • The potential model size can be quite large due to • Number of finished products • Number of inventory locations • Product age • Fresh vs. frozen

17. Other Production Processes at Swift • Offal is organ meat and other internal parts such as tripe • Ground beef is made from trimmings that are a by-product of the disaggregation process • Trim is graded by its lean point and may be sold or used in grinds.

18. Supply Chain Optimization - Phase I Project --- Our overall supply chain was in dire need of reengineering - we partnered with AspenTech to begin a bold multi-phased project to re-define our future ---- codename: Phoenix

19. Objective • Redefine the fundamental assumptions underlying our business model - move from production push to demand pull • Reshape the competencies of our team around a new set of performance expectations (business, team, and personal) • Implement a new system foundation capable of handling the complexities of data, constraints, variability, number of dimensions, speed of execution, and adaptability / interoperability

20. Why was the AspenTech solution selected? • Product Optimization • Demonstrated the ability to navigate the full span of the disaggregation trees (by brand.grade.weight.yield) as demand/order information changes. • Demonstrated capable-to-promise functionality: Can we fill this order line item given carcass availability and already committed orders? • Inventory Management (Age) • Demonstrated the ability to efficiently allocate production and inventory to orders based on product age and order out-by date and time.

21. Why was the AspenTech solution selected? • Inventory Availability • Demonstrated the ability to place product availability in the hands of sales associates on a near real-time basis. • Flexibility as Business Requirements Change • Modeling approach allows for addition or deletion of business requirements as they are identified or changed.

22. Path: 3222 - Commodity Choice 30 PECT WCNO MTNB BICR PECT CHBR CSQU BICR MTNB BICR CART C2PC BKST PECT CHSC CSQU BICR NKMT STRS CART PECT BBSW NOSL BBSW BBSW BCSR CFMT CHSI 300 BCSR CFMT BBSW BCSR CFMT BCSR CFMT CHCD CHSC CHRL SLCD INCD SWCD 1CHR CSQU NKMT STCE CSQU BBFC SCCB 100 CDFL CDSH 100 With Product Optimization 500 200 100 200 The LP model can “reassemble” the 100 pieces of CHRL and alter the production schedule to make 300 pieces of WCNO. It is the capability to see the entire tree structure that is at the heart of the model’s ability optimally allocate raw materials to finished goods. Of course, the LP model can also go down the tree as well.

23. CTP As It Is Now • Uses the power of LP models - the disaggregation tree can be modeled as production operations that turn a parent item into one or more children and by-products. • Allows the LP to go Up and Down the tree, reconfiguring the schedule, as needed, to satisfy demands as they arrive from the order entry system. • Provides a capable-to-promise (CTP) functionality that does not exist anywhere else in the beef industry, or in similar industries such as lamb or poultry.

24. New Business Model: ‘Demand-Pull’ Run Product Optimization Models Create Production Schedule Identify Customer Demand …identify customer demand create plans to produce balance production and inventory procure the right raw material sell at optimum values…. Match Cattle Supply to Schedule Sell What Customers Prefer Produce What is Scheduled Product Availability Based on Maximized Value

25. Boxed Beef Boxed Beef Every 15 Minutes Unsold Production Availability Display Ground Beef Ground Beef Offal Offal Greeley Production Availability Greeley CTP Aspen MIMI Model Cluster for Each Plant Boxed Beef Refresh Cycle Twice Daily Ground Beef Offal Greeley Scheduling Committed Orders, Inventory, Schedule

26. Boxed Beef Boxed Beef Boxed Beef Ground Beef Ground Beef Offal Offal Ground Beef Offal Boxed Beef Dumas Boxed Beef Omaha Greeley Ground Beef Ground Beef Offal Offal Nampa Grand Island CTP Application Design CTP Query Order Entry System Aspen CTP Engine CTP Response

27. The Swift & Company Supply Chain Plan Sales and Marketing Support • Capable-to-promise functionality for order entry • Projected unsold inventory availability Source Cattle Procurement Make Slaughter and Fabrication Operations Deliver Inventory Pickingand Shipping • Projections for future cattle requirements • Product mix optimization • Shift level fabrication schedules • Product availability by shipping date and inventory age • Increased inventory turns • Elimination of excess warehousing costs Span of Aspen Scheduling/CTP Application Return

28. Transaction Volume • Peak loads of 300 transactions per hour • Majority are for boxed beef models • Typical weekly transaction volumes of 20,000 queries of all types • Translates to over 50,000 line items • Query types can be mixed within a single transaction, as long as a query and a commit are not in the same transaction. • Because of model management issues • Java-based queue manager in CTP engine allows combining of transactions during peak load periods.

29. Project Summary • Completion of the Phoenix Scheduling/CTP project required 16 months and over 25,000 man-hours • System has been in production since June 2002 • Project was a success • Management and vendor made the required resource commitment • Consequences of failure were enormous • This is a real-time, mission critical system • Rigorous audit was conducted in 2003 to identify benefits obtained from the scheduling/CTP application

30. KPI Improvements • Average Weekly Percent Sold Position increased 22% over an eight week rolling horizon, accounting for an additional 100,000+ boxes of beef per week. • Average On-time Shipment Performance (+/- 1 day) increased 8% on average and 22% seasonally (during peak demand periods). • Estimated Number of “Value-added Hours gained” was 7280 hours/year. • A 90% reduction in the number of “no name” trailers (used as overflow inventory capacity) was observed.

31. Financial Benefits Realized • A total yearly bottom line benefit of $12,740,000 was identified, representing a one-year project ROI of 200%. This number consists of the following components: • Optimized product mix • Reduction in lost orders due to system issues • Reduction in price discounting practices • Reduction in temporarily lost customers • This represents a 13% improvement in the bottom line for Swift & Company’s beef business before taxes. This is especially significant as the average margin in the beef processing business is around 0.5% – 1.0%.

32. Qualitative Benefits • Sales team now has a clear line of sight to all of the product available to sell and cannot oversell. Production schedule for beef fabrication is more complete and accurate than at any time in Swift & Company history. • New initiatives such as forward demand planning have been undertaken because the system provides a solid foundation for creating a reliable demand picture. • As new opportunities have emerged in the market place, the number of brands and cattle programs to be managed has increased. The application and surrounding infrastructure has provided an effective tool for managing this additional complexity.

33. Questions?