Spare Parts Planning with SAP at a glance. Deloitte.pdf

by grgoire schaub and diederik lebbink spare parts planning with sap at a glance. 2 table of contents introduction 3 sap spare parts planning (spp) 3 tactical planning 4 bill-of-distribution (bod): a new planning concept 4 a starting cycle 4 balancing the demand 5 balanced history to balanced forecast 5 economic order quantity (eoq) and safety stock 6 operational planning 7 from tactical planning to operational planning 7 through the bod 7 life cycle management 7 conclusion 8 a parts planning tool, but not only 8 spare parts planning with sap - a ford testimonial 9 deloittes service effectiveness dimensions 10 contacts 12 spare parts planning with sap at a glance introduction 3 introduction sap spare parts planning (spp) spare parts planning (spp) is the planning module of the sap spm solution integrated within the supply chain management (scm) business suite of sap. it has been specifically designed to meet the requirements of the spare parts business, among which the most significant ones are listed below. many other industries can also take advantage of the numerous spp advanced planning features. deloitte clearly expects a growing interest for this solution outside and inside the service industry in the near future. industry-specific requirements: nowadays customers expect more and more a high service level regarding the service parts. aftermarket companies are typically dealing with a very high number of skus. several hundreds of thousands active product references are not unusual. the distribution network, and as a consequence the planning processes, are more complex than in a normal supply chain. dynamic distribution networks, horizontal moves and remanufacturing processes, among others, are standard attributes of service parts supply chains (cf. figure 1: classic vs. service parts supply chain) single customers can typically be supplied by multiple distribution centers (cf. figure 1) depending on the parts and their dynamic stocking policies. high variablity of the parts portfolio in term of volume and demand patterns, physical dimensions, price. for a long time the service parts industry has realized that the planning tools available to them did not have the necessary capabilities to effectively organize and plan their operations. in 2002 an alliance was formed between the ford motor company, caterpillar logistics and sap in order to join forces and build a new state of the art service parts management (spm) tool. this journey resulted in the sap spm1 suite providing an integrated solution for parts planning, parts procurement and extended warehouse management. in this alliance, sap was selected as the key technology solution enabler and deloitte as the key integrator for providing support, advise and implementation services towards both ford and cat. supplierplantdistribution centercustomer finished goods supply chain figure 1: classic vs. service parts supply chain service parts supply chain supplier plant remanufacturer primary distribution center distribution center distribution center regional distribution center customer regional distribution center regional distribution center regional distribution center 1 more information about the sap spm suite at http:/ featuresfunctions/serviceparts- management/index.epx 4 bill-of-distribution (bod): a new planning concept one cannot talk about sap spare parts planning without presenting what the bod concept is. the well known bom (bill-of-materials) which specifies the list of parts used to build a product has now its distribution counterpart; the list containing the structure and the locations used to distribute a product is saved as a new master data element and called bill-of-distribution (bod). this concept is central in the service parts planning solution and all the planning functionalities, without any exception, are linked to this main product attribute. this feature acts as a decoupling point between the planning processes and the physical distribution network which, in the aftermarket, typically requires regular modifications. this bod-based architecture allows the planning supply chain model to be dynamic and adaptive to the market conditions. this flexible feature actually assumes that each part belongs temporarily to a pre-defined distribution network, and that this network can be, transparently for a user, replaced by another one without harming any planning process nor without the need for any manual cumbersome maintenance. a starting cycle there is no single starting point in the functional flow of spp, but if there were one, it would be somewhere on the cycle represented on the following figure. on one hand, the sales orders of the service parts business are captured on the right location in spp (capture demand function) whereas on the other hand the stocking/ destocking feature determines which one is the right location. capture demand records each order item on the best facing location depending on defined rules and dynamic decisions (e.g. stocking/destocking). the rules can be static and very straightforward like a geographical assignment based on the closest delivering plant, or can be much more advanced and supported by dynamic rules together with gatp, the global available- to-promise tool of sap. the idea that an order can be assigned to different locations is directly impacting the operational flow; it also means that a single order can potentially be delivered from diverse sources to the final customer. this is where the second half-cycle, stocking/ destocking, answers the question: “where should an order supplied from to reach a certain customer?” this rule-based tool decides automatically for each part where it needs to be stocked or not, and by consequence where it needs to be delivered from. the dynamic nature of an aftermarket supply chain requires that kind of decision much more than for a traditional supply chain for which a single plant is classically figure 2: examples of bods contract packager concept in many service parts companies activities like packaging and/or repackaging within the network are outsourced. sap spp enables an easy activation or change between several contract packagers at location product level. the bod is then extended to the packager which can be defi ned on product location level. this additional network function is transparently integrated within the planning calculations (e.g. lead times, deployment) capture demand stocking/ destocking tactical planning spare parts planning with sap at a glance tactical planning 5 delivering all the customers in a given geographical zone around it. in order to optimize the distribution of the numerous skus of an aftermarket organization, this automated “relocation” is a must. although very flexible, the rules at the origin of the stocking decisions are mainly based on the combination of volume and cost. of course additional rules critical to the industry are foreseen like for instance the exclusion of items based on regulations or hazardous conditions. the usual heavy, expensive and slow moving parts of the automotive industry like the body shells or the engines are not forgotten by this process: a procure-to-order scenario can also be automatically assigned (no stocked location in the bod) when the service level becomes less critical compared to the inventory carrying cost or the obsolescence risks. balancing the demand when stocking/destocking stops the planning (destocking decision) of a product location, the associated demand history is transferred to another facing location of the bod in order to still generate the right level of forecast for the customer base even though no longer from the same location. the demand history, and consequently the service, is simply switched from a warehouse to another in order to optimally deliver the parts. this mechanism, called demand realignment, is following the same principle as the one used during capture demand and ensures that each sales order is always optimally assigned within the supply chain. next to the stocking/destocking decisions, other planning processes can trigger the realignment of the demand history like a modification of the bod assignment, a promotion or a supersession chain. balanced history to balanced forecast the sections above explain why, how and when the demand history is captured and realigned depending on the planning decisions. but this process would be meaningless without an ultimate goal which is the generation of the forecast. the guarantee that the demand is at any given moment in time recorded on the right location signifies that the forecast is generated always in the locations where the goods will be physically delivered from. the spp suite contains nine predefined forecasting models (based on the realigned demand history), which are capable of accurately forecasting (if possible) the future demand of parts according to different types germany parent germanyfrancespainpoland sales ordersales order figure 4: destocking decision leading to a demand realignment supersession in a nutshell supersession planning handles the replacements (complex or simple) and discontinations of parts during their lifecycle. typically the supersession chains create a lot of confusion within the aftermarket industry as well as others mainly due to the fact that the chains can contain a huge number replacements and that they have an impact on all the departments, from the warehouse to the senior management. spp gives the visibility regarding the supersession chains and handles them efficiently in each planning process, thanks for instance to the demand realignment. more information on supersession can be found in the article: managing supersession by euan macleod april 2009 deloitte. 6 of demand patterns such as constant, trend, seasonal, intermittent and exponentially declining. some models are part of the eminent algorithms known by all within the supply chain planning world while others have been specifically designed by aftermarket industry insiders or adapted from existing models. the forecast process is by far the most comprehensive (not to say complex) of the solution and encompasses, among others, advanced tools like an automatic model selection, composite forecasting run, rough and fine tuning of model parameters, outlier corrections, trend limitations, parameters inheritance, tripping, triggs tracking, adaptive initialization of models, disaggregation throughtout the bod, manual and automated approval, recalculation in the past for optimized forecast error calculation all these topics are out of the scope of this article, but it is most important to remember that the flexibility and the best-of-breed algorithms provided make spp a very powerful tool to forecast. economic order quantity (eoq) and safety stock as briefly noted in the previous section, spp provides a recalculation of the forecast in the past methodology which gives as an advantage to always have an accurate forecast error evaluation (standard deviation) regardless of the forecast model change, realignment of demand history or other modifications of network lead times. this standard deviation is directly used by the eoq and safety stock module which generates these two values in a time-phased manner, and most important, in an interdependent way. the algorithm is based on the optimization of the total stockholding and procurement cost depending on the forecast, forecast error, target service level, lead times and probability distributions. an automated segmentation of the parts portfolio is executed regarding both the target service levels and the statistical distributions used. the first one is defined based on flexible rules in order to meet the service policies of the company while the second one is optimally assigned depending on the demand pattern of the part location. additionally the service level is also dependant on the lifecycle of the parts which allows using a higher service level during the demand peaks, as shown on the figure 5: adaptive target service level. spp inventory planning is based on the normal probability distribution as well as the poisson distribution which is particularly important to deal accurately with the abundant slow movers/lumpy demand items. figure 5: adaptive target service level time demand high service level medium service level trigger-based planning the high number of parts and locations creates it risks in term of system stability and runtimes. for instance the processing of all the very advanced features of forecasting can be time-consuming. an interesting point of the spp architecture is the event- based framework thanks to which the planning activities are only performed for the items which were affected by a particular event; a daily planning calculation refresh even for a portfolio of hundreds of thousands of parts is no longer a problem. spare parts planning with sap at a glance operational planning 7 from tactical planning to operational planning so far the main tactical planning functions of spp have been described. the outcome of these activities are used by the operational planning tasks in order to, on one hand, generate the appropriate procurement proposal, and on the other hand to manage the movements of parts inside the distribution network from the suppliers to the customers. the first action is performed by the module “distribution requirements planning” (drp) which uses the bod as the main structure to roll up the demand from facing locations to entry points in the network. its calculations are based on the forecast, external demands, eoq and safety stock values as well as on data coming from the operational system (erp) such as the stock figures. drp provides also the following interesting features: various planning horizons and stability options which ensures the alignment of the planning flow and the supplier contracts anticipated demand coverage (drp provides the possi- bility to smooth seasonal demand patterns by pulling ahead peak season demand into lower seasons) pre-season safety stock planning product group procurement optimization supplier shutdown handling virtual orders consolidation (for slow movers) planning of remanufactured products multi-sourcing advanced approval rules through the bod the creation of the stock transfer orders (sto) are generated by two processes: the first one considers the vertical top down movements throughout the bod (deployment) while the other one takes care of the horizontal requirements coming from potential unbalances inside the network. deployment differs in some ways from the classical apo deployment. among others the fact that the deployment is done day by day without any creation of orders in the future, or the use of priority tiers for the demand in case of shortage to guarantee an optimized use of the available supply. next to that, the deployment can also generate expedite shipments when the business conditions require it. as every planner knows, planning is mainly dealing with uncertainties. when these uncertainties unbalance inventories, the inventory balancing tool of spp provides an automated way to overcome these situations by generating horizontal movements within a bod. although this flow results into more expensive operations, it can be very useful when the cost of a stockout is higher than an additional sto. of course inventory balancing provides the possibilty to define the balancing areas based on the regulations or geographical zones for instance, and can be easily used as an analysis tool thanks to its embedded approval functions. life cycle management given the huge number of skus, the warranty agreements and the demand patterns of typical aftermarket parts, surplus and obsolescence issues are common. in addition to the supersession planning (cf. frame above), spp provides a surplus and obsolesence tool which handles the life cycle of the parts (end of life). old parts produced in the eighties are no longer stored forever thanks to an automated determination of the surplus stock based on the long-term forecast and the retention policies of the companies. moreover spp determines when is the best time to remove a part from stock depending, among others, on the market responsiveness. since the obsolete stock is an important source of cash loss within the parts business, the potential return of using such a tool is very high. operational planning 8 a parts planning tool, but not only following the development phase of the sap service parts management suite, ford has decided to start the implementation in europe with the planning functionality (spp) closely followed by extended warehouse management (ewm). this exciting journey supported by deloitte turned out to be a great success story; the ford customer service division (fcsd) in europe is now planning its whole portfolio of spare parts thanks to sap spp which has brought along numerous benefits. some of them are listed below: deployment of global processes and best practices across multiple departments achieved highest levels of customer satisfaction in terms of fill rates and back orders by developing and implementing new business processes reduced cost by maximizing the fill rates while reducing the network wide inventory levels improved customer satisfaction and service levels by a more accurate, fully integrated and fast responding service parts planning solution reducing it spending by reducing the investment in legacy systems proportional to the launch timeline the business expectations were more than achieved and provided a very positive return on investment for ford. being part of the sap spm project from the very beginning, deloitte has developed an unmatched inhouse expertise in the implementation of the sap service parts management (spm) suite, both on an operational and a technical level. based on its expe