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Impressive Enterprise Resource Planning Solution Gets A Little Help From Its Friends

Written By: Predrag Jakovljevic
Published On: July 27 2006

Nothing Without a Little Help from its Friends

Strategic Systems International (SSI) (http://www.ssi-world.com) has been developing, implementing, and supporting packaged enterprise systems since 1982, initially as an information technology (IT) department of Powell Duffryn plc, and since 1998 as a privately held company (till becoming part of Chelford in the early 2000s). Despite impressive product depth and breadth (for instance, customer relationship management [CRM], workflow, traceability, and quality management are provided natively), SSI has longstanding partnerships with several best-of-breed specialists. These specialists include Mitrefinch for human resources (HR), payroll, access control, and time and attendance (T&A); and Greycon for advanced planning and scheduling (APS) tools. They also include several partners, such as Barloworld Optimus, for demand planning; i2i for export documentation; Cognos for business intelligence (BI) and reporting; and Intermec for mobile computing, bar coding, and shop floor data collection. The long-term and embedded nature of these partnerships makes the integration almost seamless and transparent to the customer.

Part Four of the series Vendor Defends Its Strongholds With Focused Enterprise Resource Planning Solution.

The deepest partnership has likely been with CODA, a preferred best-of-breed financial management and accounting system, with over fifty common customers. Having CODA as an intrinsic part of TROPOS places SSI in a very competitive position, even against the likes of SAP or Oracle. For instance, some of the key attributes of CODA's offering include multi- "just-about-everything" (such as multiple companies, multiple currencies, multiple languages, and multiple charts of accounts) in a single product's instance or database. Thus, single or "unified" ledger architecture, which processes and holds all the user company's financial data in a single ledger and in real time, eliminates traditionally lengthy and tardy batch updates between sub-ledgers. Consequently, a near real-time financial position is always maintained, and the entire general ledger (GL) should always be in balance. In addition to the real-time update, the unified architecture advantages include elimination of the need for reconciliation, simpler data retrieval, more efficient processing, and a consistent "look and feel."

CODA solutions are also being used to address the complex requirements of those companies that need to intelligently share information with business partners, customers, and suppliers, which is in tune with the SSI's collaborative framework philosophy. For more information on CODA, see Best-of-breed Approach to Finance and Accounting.

Furthermore, when the Chelford Group acquired Shian, it acquired considerable Microsoft expertise in Microsoft SQL Server, Reporting Services, Analysis Services, Business Scorecard Manager, SharePoint, and Web services development. With these, SSI can develop impressive in-house information worker and BI solutions, intranets, extranets, and so forth, to combine the functionality of TROPOS with the information delivery capability and role management provided by Microsoft's range of tools and technologies. With the arrival of Microsoft CRM 3.0, this has become even more pervasive, as the Microsoft environment we are all familiar with on the desktop now becomes the enterprise environment, encompassing all enterprise resource planning (ERP) and office functions in one, including contact management. This is one area that will likely see significant growth as companies finally manage to "join up" their ERP and desktop systems. See Major Vendors Adapting to User Requirements for more insight on this area. Prior to the Shian acquisition, SSI had largely relied on the agreement to offer VECTA sales intelligence software integrated with the TROPOS ERP solution.

For background information on SSI and TROPOS, see Vendor Defends Its Strongholds With Focused Enterprise Resource Planning Solution. For a discussion of TROPOS for selected industries, see A Focused Web-based Solution for Chemicals, Drugs, and Mill-Based Industries. For an examination of the TROPOS strategy, see Web-based Enterprise Resource Planning Solution Exhibits Lean Approach.

Holistic Inventory Optimization

Early in the 2000s, the company launched a partnership with UK-based Barloworld Optimus, whose inventory optimization Optimiza tools (privately labeled as the TROPOS Optimiza module by SSI) have helped many user companies cut stock levels while simultaneously improving product availability. Optimus has an innovative inventory management methodology, whose aim is, like many other inventory optimization products to enable clients to reduce investment in stock while at the same time maintaining or improving customer service levels (see Inventory Planning and Optimization: Extending Your ERP System and Lucrative but "Risky" Aftermarket Business—Service and Replacement Parts SCM). Managing risk is about managing the cost of maintaining unnecessarily high levels of inventory against the risk of running out of stock at the crucial "moment of truth," when a customer actually wants something. Most traditional ERP systems record transactions, run programs to suggest re-ordering based on fairly crude algorithms or even more dubious forecasts, and occasionally come with some form of management reporting. Few, if any, include more sophisticated inventory management tools of the level required for the consumer goods supply chain.

For a more detailed discussion of inventory optimization, see Yes, We Have No Bananas: Consumer Goods Manufacturers Serve Demanding Customers.

With functionality aimed at both senior executives (who can fairly quickly and easily see the cost of excess inventory and potential stockouts) and purchasing specialists, Optimus touts a solution that provides more complete decision support capabilities for the inventory chain. Established in South Africa for over 15 years, Optimus has over 350 operational customer sites in 5 continents, many of which come from dozens of partnerships with vendors like SSI. Areas of particular expertise include the automotive aftermarket, steel production, and chemicals.

The central problem of inventory management is the lack of any link between the macro level of corporate stockholding policy, and the micro level of day-to-day purchasing. But the Optimiza methodology makes that link. The product generates a risk profile—taking into account the level of forecast accuracy and the performance against schedules of the supplier—for each line item. Items are classified according to their criticality, with the computer generating forecasts for less important classes (the most crucial items, though, require management input). Part of the complete Optimus methodology is a regular executive review of forecasts and inventory levels, and SSI consultants can, if needed, facilitate this meeting for common customers.

There are a number of stand-alone forecasting packages on the market, and although useful, their effectiveness is limited because of the difficulties in using them as an integrated part of the planning and decision-making process. Namely, these typically do not take risk management and inventory optimization into consideration, and are difficult to integrate with ERP systems. Conversely, Optimiza includes forecasting as an integral part of the inventory optimization process. There are a number of factors that affect the accuracy of a forecast, and the most common basis of forecasting is historical information, despite the fact that most historical data is a record of what actually happened, and not necessarily of what the customer actually wanted to happen. For example, stockouts might appear as reduced demand, whereas in fact they were unsatisfied demands. The effect of stockout hence needs to be automatically eliminated from the sales history to provide a more realistic forecast. Thus, a key feature in the forecasting module is automatic "outlier elimination," which allows the system to adjust historical data points to reduce the effect of non-standard market events, such as once-off sales (or missed sales), on forecasts. The benefits are better forecasts, and less time spent in identifying these anomalies over thousands of line items.

Embedded in Optimiza is the "Tournament" forecast method, which runs many algorithms, optimizes parameters (based on root mean square errors [RMSE], a mean absolute deviation [MAD], or a fit coefficient, which is a measure of how well the predicted values from a forecast model "fit" with real-life data), measures accuracy against past demand, and then recommends the optimum forecast method by measuring the accuracy of the method for each line item.

The forecasting module incorporates more than twelve algorithms, including moving averages, weighted moving averages, and exponential smoothing with trend analysis and seasonality options. Manual adjustments can be electronically recorded, together with user comments in the meeting (or conferencing) comments facility to collaboratively support changes. Adjustments to future demand can also be made so that promotional or marketing events can be added to the underlying demand by product. The net result should be reduced risk, leading to improved service levels and optimized inventory levels, and distribution operations too can run their businesses using planned demand where decisions are made proactively. The forecasting module enables users to conduct detailed reviews of their data, segmenting it into templates by product, supplier, major grouping, product grouping, or Pareto/ABC-type analysis category. The business can forecast on a group or family level, and explode the forecast down to the lowest item level.

An ERP system typically uses parameters to control replenishment policy, and this is usually set by category or product group. However, different product categories may require different decisions relating to replenishment, safety stocks, and customer service levels. Without a specific inventory optimization tool, it is difficult to identify which product requires which set of inventory rules, since these can vary according to item cost, demand, order frequency, supplier, stocking location, value, shelf life, perishability, purchasing rules, or often rather subjective criteria set by sales and marketing policies. Certainly, inventory targets must be scientifically derived and realistic in terms of being achievable, and Optimiza allows the user to filter products according to criteria such as product hierarchical categories, or supplier categories via templates for each user or stocking location. Thereafter, the user is able to allocate products into Pareto groupings based either on turnover value by cost price, selling price, or profit margin; or by unit of issue, which ensures a more strategic focus.

Furthermore, appropriate policies for inbound lead times, review periods, and replenishment cycles, can be set prior to using sophisticated safety stock tables to model optimum safety stock levels. Optimiza has user-defined mechanisms for quick and easy identification of both non-stock and slow-moving items, whereby both have a specialized category that allows the user to separate them for different focus and attention. Consequently, slow-moving, erratic-demand, or maintenance items can be treated differently from fast-moving items, using tables to set optimal levels based on lead-time annual demand and service level. Service level settings can be set by criticality of the product to determine the optimum bin level for just-in-case or insurance items.

The calculated inventory model allows the user to determine the impact of alternative policies, prior to implementing changes. Optimus highlights the trade-off between the cost of carrying stock and the cost of stockouts, which allows the user to exercise "what-if" scenarios with different policy settings. When the optimum policy has been modeled and set, the product flags any sub-optimal transactions, by alerting the user through a management cockpit, which highlights problem areas. The management cockpit presents the top suggested value-adding activities to the user on a daily basis, with graphical profiles being used to monitor the daily progress to realign inventories against the optimal policy. The profiles display a graphical view of the risk profile for each individual item, as well as the minimum and maximum levels in graphical and numeric formats.

Keeping Planners, Buyers, Executives, and Suppliers on the Same Page

The other side of inventory is the supply side, and the unpredictability of supply is often as large a contributor to excess inventory as difficulties in forecasting demand. Any attempt to reduce inventory must also focus on managing the supply chain, and key to managing the supply chain is the ability to manage suppliers and their lead times. Most good ERP packages record supplier performance data, but few include pre-built supplier performance analysis. Often this is limited to a few reports, and to make most effective use of the data, expensive custom reporting suites have to be built around BI reporting tools. But using the Optimus supplier evaluation module, the buyer is able to quickly identify if a supplier is not performing to the quoted lead time. Even more, the product will not only identify problems, but also suggests what the lead time should be, using line item delivery information displayed numerically and graphically. Again, anomalous deliveries (outliers) can be excluded from the lead time calculation for more accurate results, and lead-time outliers can be excluded from the automatic supplier lead-time update. The result should not only be better predictions for lead time, but also more optimal safety stock—set to take account of actual delivery performance.

The common theme running through the challenge of optimizing inventory levels is identifying the real causes of inventory, whereby treating only the symptoms can often hide the root cause of excess stock or shortages. Organizations holding stock at multiple locations have the additional challenge of not only setting the optimum stockholding, but also deciding where to hold the inventory within the supply chain network's nodes. Again, historical transactions can provide misleading information, as stock movements between locations can indicate artificial demand and supply, whereas the real demand exists at only one location. To that end, distributed demand management (DDM) is a key component of the Optimiza suite, whereby the forecasts can be run across remote sites and aggregated with projected replenishment quantities back to a central warehouse level. The module takes into account all inbound orders from suppliers, existing customer orders, internal lead times, and forecasted branch sales, with the result being stocking to external demand, rather than according to historical branch transfers.

The DDM module is driven by strategic policy decisions regarding the company's inventory investment. These policy decisions, which essentially dictate the way the company wants to respond to the demands placed on it, can be far-reaching. The fair share logic algorithms ensure that the inventory is optimally apportioned across locations where there is insufficient inventory to supply their needs from the central warehouse. In addition, various reports allow the user to see where inventory can be re-distributed across locations, rather than placing new orders on suppliers. Inter-location orders can be automatically generated and uploaded into the host system—reducing workload and automatically producing picking slips for inventory transfer. This should reduce the overall inventory level, and create a more balanced inventory across the supply chain.

Still, the best inventory optimization methodology can become worthless unless it is applied constantly: a once-off exercise might make a difference, but without constant adjustments, fine-tuning, and warning of impending shortages or inventory build-up, the same problems typically recur.

Sure, a customized inventory manager's dashboard can be built using BI or report generator tools, but it can be costly and time-consuming. However, Optimiza contains a suite of reports to provide the focused information needed to manage the inventory process effectively, including forecasting, product management, order management, and supplier performance. Furthermore, the product continuously monitors and compares all inventory-related transactions against policies, flags any sub-optimal transactions, and alerts the inventory controller on a daily basis with built-in automatic e-mail facilities. A task bar presents the recommended top value-adding activities to the inventory controller, also on a daily basis. Graphical profiles are used to monitor the daily progress on realigning inventories against the optimal inventory profiles. An overview shows each action category as a relative percentage of the total inventory value, and detailed information can be obtained by drilling down into each category to identify the relevant line items contributing to the profile. The landscape graph details how the current inventory is invested by showing the monetary value of inventories associated with the various months of inventory holding.

Graphs are printed at regular intervals to provide an indication of progressive improvement of the way in which the inventories are managed. Based on still outstanding and forecasted sales, current stock levels, existing orders, and supplier performance data, the Optimus toolset calculates ideal levels of safety-stock value inventory for every line item. The "cockpit" view, which appears upon starting the tool, shows the value of orders which might be lost through stockouts, the current value of excess stock, the service level being achieved at the moment, and the value of outstanding purchase orders deemed unnecessary by the system.

In a nutshell, as long-term forecasts are inevitably wrong, users can now move more towards short lead-time planning within the more immediate horizon, which should enable their suppliers to see the real demand rather than the forecasted demand. The inventory optimization modeling has been the key for some enterprises that have been able to use the Optimiza tool to run "what-if" scenarios, and then refine their plans accordingly. The system is also often used to identify changes in the sales trends data—helping management to identify which items are gaining or losing popularity, whereas staff can consequently focus more on monitoring what has been ordered versus what is actually required.

 
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