Stock and Service Level Optimization: The way forward with SAP-ERP

Transcription

1 Stock and Service Level Optimization: The way forward with SAP-ERP As they grow, many organizations are confronted with costs and service level delays which were previously unseen or not critical. This is because the cumulative cost of inefficient logistics and production processes take on weight and risk as the number of customers, products, suppliers, production lines, etc. grows. While trying to improve the situation, a common mistake is to narrowly focus on a particular stage of the process such as shipping or vendor collaboration and then apply new software applications which are just as narrowly focused, i.e. as specified by the requesting department. Mid-size corporations in particular, sensing a need to revamp business processes in a period of strong growth set out to implement supply chain management (SCM) on top of their SAP-ERP system. SCM will be viewed by management as a kind of modernization or "upgrade" of an ERP system already in use. If not well analyzed and weighed off against simpler alternatives, this will turn out to be a costly strategy with only marginal returns. Why? Because SCM is not in itself a modernization of ERP; it is a complex, centralized planning system based on other assumptions. If the inefficiencies observed stem from business processes which are neither transparent nor strictly adhered to across departmental lines, implementing SCM or other software will not improve things. When does supply chain management make sense? SAP-SCM, also known as APO is a powerful system separate but fully synchronized with SAP-ERP. Many of the functions in ERP dealing with forecasting, capacity planning and MRP are duplicated in SCM and augmented by other functions and more advanced algorithms. For example, finite scheduling which integrates MRP and capacity planning and bottom-up re-scheduling are features of SCM which can reduce planning effort in complex logistical networks. The purpose of this paper is not to support a decision regarding supply chain management. Suffice it to say SCM can be the right solution if a) you want to centrally manage your supply chain according to global standards and b) if processes can be so well-defined and coordinated across multiple supply and production facilities so as to truly make the added power of SCM worth the significant additional costs of implementation and future system maintenance. Most improvements can be achieved within ERP This comes as a surprise to some, but there is often more to be gained by investing the time to understand and implement the principles behind good ERP than by buying or writing more code. In other words, "best practice" outscores "best software." Reducing product lead times, inventory and administrative costs associated with supply chain or logistics planning is an iterative process which should never be allowed to end, even after initial Seite 1 von 8

2 benchmark goals have been achieved. Because products, suppliers, technology, plant capacities, budgets and customer requirements are all in a state of flux, there is no such thing as a place to rest after "winning the game". That's why a key feature of excellent business processes is adjustability using a standard catalogue of parameters. So, if there is a process in place which is repeatable and tweakable, a greater degree of efficiency in production and logistics processes can be reached at less cost in the long term than with software add-ons and modifications. Catching up on your homework It all begins with good analysis of your data. Inventory and service level optimization are subject to mathematically-related events. Only when the quantifiable objectives and their dependencies are fully understood will the resulting new processes pay off. No MRP or production planner, no purchaser, warehouse or customer service manager will devote himself to a process he does not understand. Unless you already have good practices in place for analyzing logistics data, some staff training will be necessary. The following list of analyses is not by any means exhaustive but they can all be generated directly from SAP ERP, for example from Logistics Controlling, Inventory Controlling or by way of addon tools, such as Replenishment Lead Time Monitor from SAP. Two analysis objectives 1. Identify current weaknesses First, it is a good idea to identify materials which are obsolete or else carried in unjustifiable quantities and therefore driving up cost of inventory. - Residual or "dead" stock This analysis identifies that portion of inventory which has not moved within a defined period. The analysis should deliver a list of materials having high quantities of dead stock. S t o c k l e v e l Example Material ABC-002 Stock which was never used June End of analysis period The value of residual stocks (acquisition price x dead stock qty.) highlights the potential for optimization Therefore specifics of the materials should be analyzed as well, especially safety stock level lot size and rounding quantities procurement lead times demand curve during the same period Other analyses which can help identify weaknesses are - Slow moving items This standard report lists materials with no consumption within a defined length of time. A meaningful option is to select only those materials with stock on hand. An ABC classification can be carried out directly with the resulting data. - Range of coverage It is possible to identify those materials having a range of coverage less than their respective replenishment lead times. Such materials pose the risk of a stock out situation. At least they should be checked to make sure the given replenishment lead times are accurate. Seite 2 von 8

3 Materials with a range of coverage significantly greater than their lead time point to excess stock levels and capital commitment. Replenishment Current range of coverage Lead Time no stock 1-7 days 8-20 days days > 40 days 60 days 120 days 1-7 days days days days No data TOTALS By way of example, the table above groups the number of materials in ERP by replenishment lead time as set in the material master and range of stock coverage in days. Range of coverage can be based on different factors, for example, historical consumption, current demand or stock on hand, with or without consignment stocks. The lightly shaded figures are those facing stock-out situations. The darkly shaded figures are materials which are likely stocked too high, and those in the middle (white) have current stock on hand compatible with the lead times set in the ERP system. In a nutshell, this data extract shows that only 20 % of the materials selected (118 out of 589) have stocks within their defined lead times. Approximately 28 % of the materials are potentially over stocked and 24 % are at low levels which can lead to production or shipment delays. 28 % of the materials selected have no replenishment lead time recorded in the system and should be reviewed. These might be materials recently added to the system, having few historical data and low master data quality. By calculating the stock values of these categories, you can see roughly what financial risks are involved in case of missing parts and excessive inventory. By considering also acceptable lead times for the customer, the risk of lost sales can also be quantified. 2. Classify materials and products into "planning profile groups" The greatest value of logistics data analysis is a clear picture of how to manage, procure and dispatch various materials. What strategy is best for which type of material? - ABC Analysis ABC analysis categorizes materials according to their commercial impact. It is a well-known and accepted quantitative method which enables you to better determine which materials deserve the time and effort involved in optimizing the re-supply process. 1 Where stock optimization and service level is concerned, the basic process is to assign each material to a segment which measures importance or impact from high to low. The key figure you want to focus on is free to choose (avg. stock quantity, value of goods received, stock usage, etc.) for a user-defined period, material category and organization. ABC analyses are usually done in multiple steps, starting with rough, high-level groupings which can then be broken down into more discrete segments. Because there are multiple units of measure in the company's inventtory, a useful focus to start with would be value of stock used as a percentage (as opposed to pieces, tons, etc.) whereby A - items are those having the greatest impact, for example 70 % of total stock consumed within the period B - items are those having a significant impact, for example the following 20 % and C - items are the materials representing approximately 10 % of total inventory value used Obviously, the greatest amount of time should be spent on the A and B segments when seeking to optimize re-supply schedules, stock levels, sourcing, lot sizes, etc., because doing so promises much more benefit. Materials in segment C constitute the bottom 10 % in terms of value and 1 ABC analyses can also be applied to work centers, vendors or customers to assess which objects (e.g. suppliers or customers) have the most impact on your business based on the number of orders or the value of business conducted with them. Seite 3 von 8

4 complicated, optimized re-supply processes tend to be more expensive than they're worth. The illustration below shows that a total of 1,960 different materials were used within the analysis period. Of these, only 68 (3.5 %) accounted for more than 70 % of the total value. Optimizing processes for this group would have a high payoff. The C group on the other hand some 1,678 materials could probably be planned for and handled most economically by way of standardized, automated processes. ABC analysis based on usage value in EUR Segment Material masters Total usage value Group A 68 3,5% ,29 73,35% Group B ,9% ,36 20,24% Group C ,6% ,66 6,41% Totals ,31 The application features four different strategies to show the impact of a user-defined group of materials: 1. Selected key figure as a percentage (above example) 2. Selected key figure absolute 3. No. of materials as a percentage 4. No. of materials absolute Using these strategies and various selections, it is possible to quickly generate answers to questions such as - What are the top 100 materials used in the past half year and what was their value? - How much stock does the top 5 % of my semi-finished goods represent? - XYZ Analysis The second step involved in classifying materials into "planning profile groups" defines how predictable the material's usage is. Category X materials easily forecasted because their usage is fairly constant over time. At the other end of the spectrum are Z materials with sporadic usage and high variability. Y materials are in the medium category regarding predictability. The usage pattern of each material can be mapped from SAP ERP data. The example shown here places material A-4711 in the Y category based on the factors average usage, standard deviation and variation coefficient, whereby the ranges for X, Y and Z were defined as: X = 0 10 % Y = % Z = % Note: The more usage quantities analyzed the better. It is important to make sure a complete planning term is captured, keeping in mind such things as seasonal deviations, and product life cycle. Otherwise the usage pattern will be unrealistic and undermine the objectives of the analyses. XYZ Analysis: Material A-4711 usage over the past 13 months PERIOD USAGE Jan EA Feb EA Mrz EA Apr EA Mai EA Jun EA Jul EA Aug EA Sep EA Okt EA Nov EA Dez EA Jan EA Average Standard dev Variation coefficient as % 12,71 XYZ category Y Seite 4 von 8

5 The third step is to build a matrix based on the desired ABC and XYZ categories and then decide which planning strategies should be used for each group. As there is no standard function in ERP for this, a new info set has to be defined and populated or else the data can be extracted and processed in a spreadsheet. All materials analyzed are now placed in blocks for the purpose of defining appropriate planning profiles, whereby group A with 68 materials has been broken down into two subgroups. Materials in the value segment A tend to be highly unpredictable in the industrial sector (Z) while the high-value consumer products can be managed by using good forecasting methods. The task at this point would be to define planning profiles for the materials in each block. Planning Profiles ABC analysis based on usage value in EUR Segment Material masters Total usage value Group A 68 3,5% ,29 73,35% Group B ,9% ,36 20,24% Group C ,6% ,66 6,41% Totals ,31 Segments P r e d i c t a b i l i t y X Y Z A1 Industrial A2 Consumer Group B Group C Planning profiles are groups of settings which help define the methodology for managing availability and stock levels of similar groups of materials. This paper concludes by describing various strategies and parameters which can be helpful in defining a planning profile. When the various planning methodologies have been tested and approved they can also be formally recorded in SAP ERP and applied as part of master data maintenance processes. Once a group of materials has been allocated in the ABC-XYZ matrix, it is possible to construct a profile for that group addressing four basic aspects: - Procurement type A material can be manufactured locally, ordered from another plant within the same organization or from an external supplier. - Planning policy The planning policy basically defines a. How demand quantities are determined. For example, it is possible to say if forecast requirements, customer orders, safety stock, planned orders for parent assemblies and other factors are to be considered when calculating quantities required. b. Whether requirements should be determined plant-wide, individually by storage location or by user-defined organizational units within the plant, so-called MRP areas and c. Kanban process, if applicable - Order quantities This aspect of the planning profile defines how much to order in a given situation. Whether the exact quantity required should be ordered or some other lot size should be described here. Safety stock should be addressed here as well. Safety stock can be set manually or calculated periodically based on different algorithms each time a forecast is run. Economic order quantities optimized for cost can be determined statistically (periodically) or dynamically, i.e. each time MRP is run. The method can be defined in SAP ERP for each individual material. Dynamically calculated lot sizes are not widely used simply because the results are opaque. Most dispatchers and purchasers will not intuitively grasp why their ERP systems propose the order quantities appearing in their work lists. Economic lot size determination factors in only the cost of procuring a quantity of goods, the value of those goods and the costs incurred by holding the goods in the warehouse over time. Seite 5 von 8

6 Other factors, such as the capacities and setup costs of work centers which produce multiple materials are not part of the equation. Economic lot size determination is therefore most profitable when applied to externally procured materials where purchase prices are transparent and stable. Multiple methods can be applied in SAP ERP (see Appendix 2 Dynamic Lot Size Determination in Practice), all of which cover demand completely and on time. - Forecast methodology Whether or not a given material is subject to forecasting based on past usage is determined here as well as the time basis (monthly, weekly, etc.), whether safety stock should be newly calculated, and the forecast model. Possible forecast models are: No forecast (or forecast by external application) Constant model Constant with smoothing factor adjustment Trend model Seasonal model Seasonal trend model Moving average Weighted moving average No forecast/no external model 2nd order trend with adjustment of smoothing factor 2nd order trend Automatic model selection Planning Process Depending on the planning profile, the ERP tools and users must be defined as part of a rigorous planning scenario. Standardized procedures such as "Standard Order Launch Cycle" 2 can cover most production planning scenarios. Materials in outlying classes such as AZ or CX are best managed using different procedures. The high risk of stock out situations in the case of AZ materials, for example calls for a more detailed process using tools such as the multilevel order report and/or exception reports, which alert the dispatcher in charge to specific, user-defined warning signals. CX materials on the other hand will most likely be planned and ordered automatically using daily MRP cycles, source determination and tools such as automatic release of purchase requisitions or kanban events. Contact XEPTUM Consulting AG Carl-Zeiss-Strasse Neckarsulm GERMANY phone fax consulting@xeptum.com 2 Description on the XEPTUM website under Solutions Logistics Seite 6 von 8

7 Appendix 1: Dynamic lot size determination in practice Depending on the unit price, fixed order cost and percentage of unit price accrued in holding the stock, two different lot size methods will produce different results. The length of time in stock drives the two methods to different results; The Groff algorithm factors in the additional cost of holding inventory over time, which proposes lower order quantities than the unit cost method. Optimal lot size by Groff is compared below against lot size by unit cost method. The same requirement quantities and dates are used to show how MRP would determine lot size by the two methods depending on the relationships of unit cost and order cost: Unit price 114,00 Unit price 311,26 Fixed order cost 175,00 Fixed order cost 123,00 Holding cost in % 15,0 % Holding cost in % 15,0 % Date Demand Lot size Date Demand Lot size Lot size by Groff Lot size by unit cost method Seite 7 von 8

8 Appendix 2: Transactions and Settings SAP ERP transactions and setting referred to in this paper are listed below. Transactions MCBA Plant Analysis MCBE Inventory Analysis by Material MCYN Exception report MC40 ABC analysis by consumption MC41 ABC analysis by requirement (MRP) MC42 Range of coverage by consumption MC43 Range of coverage by requirement (MRP) MC46 Slow moving items MC50 Dead stock MD4C Multilevel order report ME59N Automatic conversion of purchase requisitions MMD1 MRP Profiles Planning parameters - material master MRP Type MRP Controller (Materials Plan ABC Indicator Planned Delivery Time in Days In-house production time Scheduling Margin Key for Float Dependent requirements indicator Indicator for Requirements group Safety Stock Reorder Point Lot size (materials planning) Rounding value for PO Minimum Lot Size Maximum Lot Size Fixed lot size Maximum stock level Assembly scrap in percent Ordering costs Storage costs indicator Splitting Indicator Service level Goods Receipt Processing Time Quota arrangement usage Period Indicator Fiscal Year Variant Purchasing Group PPC planning calendar Ind.: Repetitive mfg. allowed Repetitive manufacturing profile Planning time fence Consumption mode Consumption period: backward Consumption period: forward MRP Group Component scrap in percent Method for selecting alternate BOM Mixed MRP indicator Total replenishment lead time Planning material Planning plant Conv. factor for plan material Tact time Range of coverage profile Planning cycle Rounding Profile Planning strategy group Special procurement type Backflush indicator Indicator: Bulk Material Safety time indicator Safety time (in workdays) Action control: planned order Issue Storage Location Checking group availability check Procurement Type Determination of batch entry Minimum Safety Stock Consider Planned Delivery Time Forecast model Number of historical periods Number of forecast periods Number of periods for initialization Fixed periods Number of periods per seasonal cycle Initialization indicator Tracking limit Model Selection Indicator Model selection procedure Indicator for parameter optimization Optimization level Weighting group Basic value smoothing using alpha factor Trend value smoothing using the beta factor Seasonal index smoothing using gamma factor MAD (mean absolute deviation) smoothing Seite 8 von 8