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Inventory optimization techniques like inventory reduction, transportation and lot size optimization help counter supply chain uncertainities

Inventory optimization techniques like inventory reduction, transportation and lot size optimization help counter supply chain uncertainities

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  • 1. SETLabs Briefings VOL 5 NO 3 Jul-Sept 2007 Inventory Optimization: A Necessity Turning to Urgency By Greg Scheuffele and Anupam Kulshreshtha Counter your supply chain uncertainties with inventory optimization techniques and technologiesA key concern for global manufacturers today is reducing inventory and inventorydriven costs across their supply and distribution on inventory management across extended supply chains. In this context, manufacturers havenetworks [1]. Pressure to cut inventories continues difficulty reducing inventory with traditionalto build for several reasons. Manufacturers no or even advanced inventory managementlonger manage linear or stable supply chains. techniques. Today’s global manufacturers haveThey juggle vast supply networks. Globalization largely hit limitations in leveraging materialof the supply network and supply base drive requirements planning and managementhigher inventories and make cutting inventory processes and systems to cut inventories. Evenmore difficult. Globalization among consumers advanced inventory management techniques,is putting pressure on product availability, such as sales and operations planning orprompting manufacturers, distributors and developing demand-pull replenishment systemsretailers to upgrade their stock keeping with suppliers using Kanbans, have been eitherpolicies. Emerging market consumers are embraced or found to deliver less impact onbecoming as demanding as those in developed overall inventory reduction than anticipated.markets. These challenges are exacerbated In the last few years a new paradigmby manufacturers’ own product development has emerged: where one finds operations teamsdecisions. The drive to innovate and increase and planning teams of the manufacturer applyingthe rate of new product introductions leads to the latest techniques and technologies to improvehigh rates of new technology adoption for next inventory visibility, control and managementgeneration products, putting enormous pressure across the extended supply network. 1
  • 2. Uncertainty Reduce through forecast accuracy Hedge through increased inventory Address head-on through flexibilityFigure 1: Addressing Uncertainty through IO Techniques Source: Infosys Research We call this collection of efforts as levels; to enhance service levels and supplyInventory optimization. Inventory optimization availability; and to establish the right producthelps discrete manufacturers control inventory inventory mix and level in each geography anddriven costs and address today’s demand channel. Many manufacturers also focus onvolatility and supply chain complexity. inventory as part of shifting their operations We discuss several trends in inventory to achieve demand-pull replenishment acrossoptimization including opportunities to apply their supply network – hoping to achievethis new technology to solve more that just high the performance demonstrated by leadinginventory costs. Inventory optimization can manufacturers who have succeeded in this suchenable smarter product launches, lower direct as Dell, Procter and Gamble, Nokia, and Toyotamaterial cost of goods and faster manufacturing Motor [2].and distribution execution. A key driver of the renewed focus on inventory lies in the recognition that traditionalNEED FOR OPTIMIZING INVENTORIES techniques are failing to reign in inventories inThere are several reasons manufacturers are the wake of increased supply chain complexity.increasing focus on optimizing inventory by This complexity is characterized by increasedapplying the latest tools and techniques for uncertainty. Demand is more volatile andinventory control. Traditionally, competitive therefore less predictable. This is true notpressure has always driven manufacturers to only for aggregate demand but for forecastingseek enhanced capabilities to reduce inventory splits and volumes across channels and 2
  • 3. markets. Traditionally, three strategies have IO Techniquesbeen employed by manufacturers to address IO techniques apply rigorous and discreteuncertainty: (a) increase inventory levels to analysis to analyzing inventory performance.hedge against uncertainty, (b) develop supply They then use the analysis to identify product-chain flexibility to be more responsive to specific changes to inventory stocking anduncertainty, (c) improve forecast accuracy so that replenishment policies; to identify the supplyless uncertainty propagates to the manufacturing network configuration; or, to correlatefloor. Inventory optimization techniques and inventory investments to item revenue or profittechnologies map to the flexibility and accuracy generation.strategies [Fig. 1]. On the planning side, a key inventory What is driving the dramatic increase optimization technique is profit-driven analysis,in the complexity (and therefore uncertainty) where the profit each individual productof managing large supply and distribution contributes is ranked; a pareto distribution isnetworks? Globalization is one big driver, the developed to separate high profit products fromevolution of emerging markets such as China lower profit products; and inventory holdingand India present new challenges in effective policies are adjusted to cut inventory on lowproduct distribution with low inventory levels. ranked products and increase inventory on highGlobalization of supply networks means that ranked products, resulting in an intelligentlykey functions such as R&D, product design and applied net inventory reduction.manufacturing are now geographically spread On the execution side, manufacturersout, which hampers inventory reduction efforts, are striving for IO by applying improvementthat are often best executed by a cross functional concepts based on lean principles, and byteam working together very closely. Increased expanding the use of collaborative and demand-rates of new product introduction and product pull replenishment schemes such as vendor- orinnovation are also driving complexity into supplier-managed inventory to drive highlysupply networks. Finally, because increases in precise replenishment and fulfillment activity.transportation and logistics options have made These techniques are also benefiting fromcareful control and planning of in-transit or improved supply chain planning and control.pipeline inventory difficult, manufacturers are Lean seeks to optimize inventory by driving outtending instead to let inventory drift upwards. non-value added inventory management tasks in the factory or warehouse and by improvingINVENTORY OPTIMIZATION DEFINED planning and control at a granular level acrossInventory Optimization (IO) is the application each manufacturing or distribution step. Vendor-of a range of latest techniques and technologies or supplier- managed inventory schemes seek tofor improving inventory visibility, control, share risk and offload inventory ownership.and management across an extended supplynetwork. As we will illustrate later in this paper, IO Technologiesthese techniques and technologies are driving A key inventory optimization technology is theimprovements beyond what traditional inventory IO engine. IO engines reveal opportunities to cutmanagement techniques – even advanced inventory by analyzing inventory performancetechniques – have been able to deliver. holistically - looking at data from across the 3
  • 4. “Classic” Inventory “Advanced” Inventory Inventory Advantage of Inventory Management Management Optimization Optimization over prior methods Material Constraint based IO Engine Better characterizes demand Requirements planning (APS) uncertainty and lead time Planning (MRP) variability Advanced modeling Integrates with MRP and APS Days of Supply rules ABC Classification Profit-Driven Rationalizes inventory with for setting inventory Analysis minimal impact on revenue /profit levels Cycle counting Materials Closed loop planning Provides more predictable control Management system and analytics with over material flows inventory control via Enables faster re-configuration of exception management supply chain Supports smoother absorption and handling of unexpected supply or demand swings impact No control over Resorts to Chase Optimizes considering Better synchronization between production Scheduling techniques production and Production and dispatch transportation batchesTable 1: Different Types of Inventory Management and Source: Infosys ResearchControl Techniquesextended supply network. They integrate with (non-linear, algorithmic, etc.) models thatAdvanced Planning and Scheduling systems are then solved to identify optimal inventoryand Material Requirements Planning systems to policies, stocking locations, or quantities. Theincorporate policy updates into the supply chain uncertainties addressed by IO engines include:planning cycle. IO engines identify ‘smarter’ demand uncertainty (or forecast error), cycleinventory holding rules and replenishment time variability and replenishment lead timepolicies that increase overall supply chain variability. The output of running an IO engineplanning accuracy. “Smarter” typically means is fed back to the ERP, constraint based planning,applying these policies at a more discrete level, or other discrete planning system, adjustingsuch as at an item /stock keeping location inventory policies as a finite level. IO enginescombination level instead of just at the item have a range of applications, from modeling andlevel. optimizing safety stock across the supply chain IO engines characterize supply network to identifying optimal re-order point sizes inuncertainty present in a variety of specific steps environments with highly erratic demand.or links in manufacturing and distribution The rise in interest in IO engines isprocesses using advanced mathematical likely linked to their increased ease of use and 4
  • 5. accessibility over time. In the last few years, ways to solve complex mathematical equationsa new generation of technology and related in acceptable time durations. This progress is asoftware vendor community has developed key reason for manufacturers to have applied IOaround IO engines [3]. The latest IO engines techniques across a wide range of problems inbring the computational horsepower to solve their supply, manufacturing, and distributionvery large optimization problems quickly. networks. We make a distinction betweeninventory optimization engines as a new APPLICATION AREAStechnology and a broader collection of concepts, IO techniques and technologies are being appliedtechniques and technologies called inventory within both supply chain planning and executionoptimization. processes. Manufacturer are using these enhanced In spite of the significant advantages capabilities to cut inventory, enhance service levelsavailable from the latest IO techniques and and maximize return on investments by setting thetechnologies, leveraging them effectively requires right inventory levels in the right production linesmuch more data than traditional or “advanced” and distribution channels.inventory handling techniques. This is a valid Application of an IO approach dependsconcern some manufacturers express when on a deep understanding of the existingplanning to leverage these techniques. A second environment around the supply chain. Anconcern is frequently raised around the complexity accurate knowledge of the various costof the calculations, formulae and mathematics elements of the supply chain, along with a goodemployed. For traditional techniques and even understanding of existing lead time and demandfor most advanced approaches, the computations variability is required to model the processrequired are limited to simple calculations and correctly. Modeling also demands a proper andstraightforward mathematical formulae, which accurate definition of the optimization objective.are simple to communicate and explain to teams Some suitable objectives can be minimization ofoutside the supply chain function including costs, maximization of revenue or maximizationupper management. For inventory optimization of profit. One also needs to define business relatedhowever, typically higher order calculations, constraints in unambiguous terms. Examples ofincluding complex equations in algebraic such constraints are the process capacities ofor calculus forms, are used. Understanding various plants, demand limitations of variousand deriving meaningful results from these customers and service time commitmentscalculations requires a deeper mathematics between different chain partners.background and greater computing power. A mathematical representation of Despite all the complexity, IO these objectives and constraints represents thetechniques and technologies are gaining model for the subject supply chain. Typically,ground and finding more and more applications factors that drive the business constraints orbecause of recent advances in information the defined optimization objective, or both aretechnology and far greater computational power nonlinear in nature. In addition, the optimizedavailable at the disposal of today’s supply value of the IO objective function is point inchain architects. Similar advancements on the time value that changes over time as componentoperations research front have also led to newer parameters in the function change. The same 5
  • 6. is true for the constraints as well. For example, Inventory Reduction in Plant Operations /assume there is a constraint on total man hours Assembly Linesavailable for production. As the number of The production operations are subject toproduction units increase, the man hour per various process center capacities. In typicalunit shall decrease at some rate, resulting in non assembly line scenarios, the process capacitieslinearity in the constraint. Characterizing this of successive work centers interact with eachdynamic is difficult using traditional analysis, other in a complex fashion and have substantialinventory optimization is an ideal approach impact on the quantities of WIP or stagedfor the complex mathematics this entails. In component inventory for each such workaddition, there is variability in demand and in center. To reduce inventory at each workfulfillment and replenishment cycle times which center, one needs to optimize the inventoryby themselves can be difficult to characterize. requirements across the processes in aThankfully, advancements on the mathematics holistic fashion keeping in mind the requiredand IT technology fronts have made it possible manufacturing throughput rate and existingto capture these situations in great detail and product availability /service commitments thesolve them efficiently. manufacturer must achieve. Non-linearity in factors that drive business constraints or optimization objectives call for the adoption of mathematics-intensive inventory optimization approach Inventory optimization techniques Inventory requirement can becan be applied to specific areas across a broad optimized by balancing the assembly linerange of supply chain planning and execution for a smooth work flow. One also needs to locateactivities. The needs, constraints, participation, the most critical resource or bottleneck inprocess changes and benefits of each supply chain the assembly line. This bottleneck definespartner will vary depending on the industry the maximum throughput rate through theand type of optimization problem. Some newly assembly line with minimum inventoryemerging and proposed applications of IO requirements. The decision has the potentialtechnology to specific operational issues and to influence the process batch size, transfersupply chain problems are explained in the batch size and the buffer capacities for each workfollowing sections. center. At a macro level, these decisions also The following lists emerging areas of impact the lot sizes that are required to beapplying IO technologies to address complex procured from suppliers and provided to thesupply chain and inventory issues: next stage in the supply chain. 6
  • 7. Inventory Reduction Across Transportation to impact pipeline and safety stock inventoryNetworks levels. Many procurement decisions andEach stage in the supply chain has transportation activities only indirectly drive inventoryoptions available with different cost structures. levels because decisions are made prior toThe choice of each transfer mode, or combination actual sourcing execution or because of thethereof, impacts the inventory needed to be longer cycle times associated with tacticalcarried in pipeline between those stages. A procurement. While performing the sourcesimultaneous optimization of the engagement selection as a strategic initiative, little can beof various transportation modes and the level done on inventory optimization as a tacticalpipeline inventories can have significant impact exercise. The mix of procurement from variouson the overall working capital invested in sources provides significant benefit if optimizedsupply chain inventory at any given time. Trade along with the inventory requirement for eachoffs are involved on two fronts while making possible configuration of procurement mix fromthe transportation decisions. On one hand, various suppliers. From inventory reduction in plant operations to transportation networks, via sourcing policies to lot size optimization — inventory optimization technologies can address the most complex inventory issuestransportation costs are compensated against Inventory Reduction via Lot Size Optimizationthe pipeline inventory. On the other hand, Cycle inventories can be reduced by decreasingtransportation choices also impact customer the lot sizes used in production and distributionresponsiveness and thus the service level replenishment. In production, optimal lot sizes(product availability) commitments. A careful depend on the fixed cost of forming the lots. Aselection of the right configuration of modes, problem arises when different produced unitsinventory holding in pipeline and service level have different optimal lot sizes for productioncommitments can optimize and improve the but share the same work center resource oroverall cost incurred in the chain. transportation resource. Considering these limitations and the cost structure in place, theInventory Reduction via Changes to Sourcing lot sizes for different produced units can bePolicies optimized so as to utilize the available resourcesLow cost country sourcing strategies open to the maximum. This concept can be appliedup several options for applying inventory across multiple manufacturing work centersoptimization. The key is identifying which and transportation resources via customprocurement decisions are significant enough optimization routines that look across the supply 7
  • 8. Application Area Approach with Inventory Advantages over Incremental Value Optimization traditional approaches Realized Inventory reduction Finding out the Critical System runs at the maximum Provides an effective tool for in plants / resource and following capacity without unnecessary assembly line operations and assembly lines the bottleneck approach inventory through out the line a control mechanism for plant related inventory Inventory Finding best configuration Results in relatively lower Reduces transportation and reduction across of transportation modes safety stocks due to lesser distribution costs transportation (and costs) along with lead times and proper Can increase actual product networks required safety stocks, exploitation of available availability level with no utilizing service times to transport modes net increase in pipeline optimum inventories Inventory reduction Finding best configuration Better utilization of available A comparatively longer term via changes to of sourcing options and sourcing options. Easy to cost reduction technique that sourcing policies quantities with safety evaluate multiple sourcing can simultaneously considers stock requirements policies procurement and holding cost Inventory reduction Finding joint lot sizes for a Better overall reduction in An integrated inventory via lot size suitable group of products inventory and transportation reduction approach for product optimization sharing similar resources costs families, and transportation Optimizes inventory with schedules suitable MRP data Risk Pooling Finding right size of Better overall reduction in A time tested approach, inventory by analyzing the inventory across geographies Emerges as an efficient demand patterns across technique for controlling geographies for group of inventory in distribution items function Inventory reduction Finding requirement Significant reduction in the Value realized in the via common schedule and quantities of component inventory across procurement of components component components common to a multiple finished products with for ATO or similar planning group of products common component parts environment, Inventory reduction Postponing the product Allows mitigation or A trusted inventory reduction via postponement differentiation to later elimination of early WIP stage technique, casts drastic stages in chain by inventories impact on supply chain involving customer No additional inventory complexity by reducing preferences at later needed to handle varying number of products stages customer preferencesTable 2: Inventory Optimization vs. Traditional Approaches Source: Infosys Researchchain, producing a globally optimal result. An Risk Poolingeffective optimization function would consider Safety stocks across various geographies,local lot size optimality, the arrival rate of work over various time periods and for differentin-process lots from each upstream process, and product groups can be aggregated to reducethe storage or staging capacity at each resource. the total stock carried for providing a pre- 8
  • 9. defined service level. Many companies like Dell, This enables the organizations to check theHP and Amazon practically optimized their impact of variations in the consumer demandssafety stocks over different dimensions to for product variants, very early in the chainexploit the benefit of risk pooling. If the and thus avoiding the need to carry safetydemand patterns across geographies or stocks for different variants in the initialproduct groups are independent, the variance stages of the supply chain. Both postponementfor the aggregated demand will be less than and component commonality push thethe summation of individual variances. In product differentiation towards downstreamthis scenario, IO techniques and technologies in the chain and facilitate aggregation till laterare applied to identify (a)which geographies stages.or product groups are optimal candidates for Applying IO techniques here issafety stock aggregation, (b) which inventory similar to the Risk Pooling application. IO canholding locations are optimal for risk-pooling identify (a) which products are optimal forfrom a cost standpoint, and (c)the level of postponement, (b) where postponement shouldsafety stock inventory to hold in the risk-pooled take place physically for least cost, and (c) thelocation. The same explanation holds good level of postponement inventory to carry.for aggregation over various time periods andfor different product groups as well. EXAMPLES OF MANUFACTURERS APPLYING INVENTORY OPTIMIZATIONInventory Reduction via Common Component These examples of manufacturers applyingP lanning inventory optimization illustrate real worldIn a discrete manufacturing environment, a success with the concepts, technologies, andlot of inventory is held as components across techniques.various products. Thousands of components One major manufacturer of personalrequired for various products have significant communications devices is undertaking acommonality across products. Aggregation of broad initiative to enable sell-side suppliersuch components is another form of pooling, managed inventory for its major customers.with more relevance to high tech and discrete This manufacturer’s existing productmanufacturing environment. Since the same fulfillment model is based on a classic multi-tiercomponent is required for various products, distribution channel. Before adopting inventorythe demand at the component level is more optimization techniques, the manufacturer’spredictable and requires less safety stock as distribution network was unsynchronizedcompared to a simple addition of safety stocks and contained sequential layers of distributionfor the same component without considering leading to limited visibility, excess inventory,commonality. stock outs, cycle time delays and difficulty establishing new retail relationships.Inventory Reduction via Postponement In some markets, the manufacturerPostponement refers modifying a manufacturing faced a 72% retail shelf stock-out rate. Inprocess so that more of the operation is done addition, its forecasts did not adequately accountcloser to the customer and on more of a just-in- for the potential demand if stock outs could betime basis. addressed. 9
  • 10. To address these issues, the introduced because it could not ramp upmanufacturer, developed a supplier managed new product introduction fast enough. Theinventory (SMI) approach that eliminated manufacturer’s overall goal was enabling higherdistribution tiers, review cycles and order actual product availability levels during newdistortion and improved demand response. It product introductions. It used the followingalso developed business and system models for approach to apply an IO engine to help determinecollaborative inventory forecasting with channel how to properly stage pre-build and pipelinepartners and customers, including short, medium inventories:and long term forecasts of consumption,replenishment and aggregate sales. • First, it identified a series of product The manufacturer approached IO by lifecycle stages (Product launch phase,deploying replenishment planning and execution Ramp up phase, Maturity phase,solution to support SMI with data shared across End-of-Life phase), each with its owncustomers and channel partners allowing the target service level. Demandcompany to simultaneously improve planning characteristics for each phase were alsoand execution processes. In addition, for each identified, viz.,SMI replenishment process the manufacturer • Forecast accuracydeployed, a replenishment lot size optimization • Demand growthwas performed using several months of • Demand variability (based on actualhistorical data. demand) After deploying its solution with • Demand price sensitivityseveral customers, the manufacturer realizedsignificant benefits. With a single channel • Second, it developed a supply chainpartner and distributor in Southeast Asia model that characterized these- the manufacturer was able to achieve: dimensions across timeframes corresponding to the expected duration • A $31 million increase in revenues of each lifecycle stage • A $40 million improvement in cash flow • Finally it ran the IO engine to optimize • Recovery of demand lost due to retail inventory target settings to incorporate stock outs – meeting this demand meant in planning and procurement policies for increased sales volume and market share. each product at key phases of its product lifecycle. For the old generation product, The second example is that of a inventory targets were adjusted fordiversified industrial products manufacturer. its end-of-life phase. For the newThe manufacturer has a range of industrial generation product, inventory targetsproducts that include industrial solvents and were adjusted for product launch andmachine tools. It faced complexity in managing ramp-up phase.transition from the older generation product toits newer generation replacement. It frequently By optimizing inventory targets andoverbuilt its old generation product for several replenishment policies in this manner, theweeks after the new generation product was manufacturer was able to deliver minimum 10
  • 11. cost and maximum distribution coverage for and efforts to induce flexibility in the supplyboth products. By allowing service levels chain are still necessary but not sufficientto vary with naturally occurring shifts in to manage the growing multi-dimensionaldemand variability across lifecycle stages, the complexity. A suggested approach is to adoptmanufacturer was able to reduce inventory inventory optimization concepts, techniques,by more than 18% with no impact on product and technologies.availability. Inventory optimization is a powerful In yet another case, a power tools problem solving approach backed by advancedmanufacturer leveraged an IO engine working technology. The concepts, techniques, andalong side Material Requirements Planning technologies of inventory optimization help(MRP) and Advanced Planning and Scheduling model, characterize, and account for supply(APS) systems to enable granular planning of chain uncertainty. This uncertainty is a keyWIP inventory positions (location, target buffer reason manufacturers maintain higher thanquantities, replenishment policies) throughout needed inventory levels. Inventory is a buffer Inventory optimization is the new problem solving mantra for all supply chain related issuesits supply chain [5]. The IO engine determined against the uncertainty related to variablea globally optimal placement of inventory, processing and replenishment lead times, erraticconsidering its cost at each stage in the supply demand and forecast bias or error,chain and also the service level targets and The key to effectively leveragingreplenishment lead times that constrain each inventory optimization lies in viewing it as ainventory location. problem solving approach. A specific set of constraints and parameters has to be identifiedCONCLUSION and modeled to characterize supply chainThe increased complexity of manufacturing and behavior. An objective function is to be deriveddistribution among global manufacturers due from the model to isolate the parameter requiringto greater variability and uncertainty across optimization. Finally, higher order mathematicsthe supply chain suggests that a new approach are to be applied to solving the function, oftento controlling and reducing inventory levels is aided by an IO engine or large scale computingrequired. Pressures and trends impacting a capacity. Using this approach, changes can tomanufacturer’s ability to effectively manage be effected to inventory planning, inventoryinventory at a global level are increasing. stocking, replenishment, and transportationTraditional methods such as accurate forecasting processes. Also this could lead to defining 11
  • 12. the underlying operational policies at a very management practices Outdated,granular level. Aberdeen Group, March 2005. 4. Matthew Menner and Dan Harmeyer,REFERENCES Developing Creative Supply Chain 1. Sensing and Shaping Demand Strategies in Highly Competitive in a Consumer-driven Marketplace, Markets” Conference Proceedings, Electronics Supply Chain Assoication presented by at the 2006 SCOR Supply Report, 2006. Chain World North America Conference. 2. Kevin O’Marah, The Supply Chain Boston, MA USA. March 29, 2006. Top 25: Supply Chain Leadership for the 5. Sunil Chopra and Peter Meindl, Supply 21st Century, AMR Research, Nov 9, 2006. Chain Management’ Third Edition, 3. Beth Enslow, Are Your Inventory Prentice Hall, March 2006. 12
  • 13. Author profileGREG SCHEUFFELEGreg Scheuffele is a Principal and Solutions Manager in Infosys’ High Tech and Discrete Manufacturing practice. Hemanages Infosys’ portfolio of solutions for inventory design, optimization, and replenishment. He can be contacted atGreg_Scheuffele@infosys.com.ANUPAM KULSHRESHTHAAnupam Kulshreshtha PhD, is a Consultant with the Analytics Services practice of Infosys’ Domain Competency Group.He can be reached at Anupam_K@infosys.com. For information on obtaining additional copies, reprinting or translating articles, and all other correspondence, please contact: Telephone : 91-20-39167531 Email: SetlabsBriefings@infosys.com © SETLabs 2007, Infosys Technologies Limited. Infosys acknowledges the proprietary rights of the trademarks and product names of the other companies mentioned in this issue of SETLabs Briefings. The information provided in this document is intended for the sole use of the recipient and for educational purposes only. Infosys makes no express or implied warranties relating to the information contained in this document or to any derived results obtained by the recipient from the use of the information in the document. Infosys further does not guarantee the sequence, timeliness, accuracy or completeness of the information and will not be liable in any way to the recipient for any delays, inaccuracies, errors in, or omissions of, any of the information or in the transmission thereof, or for any damages arising there from. Opinions and forecasts constitute our judgment at the time of release and are subject to change without notice. This document does not contain information provided to us in confidence by our clients.