How ToolsGroup s SO99+ Complements SAP APO

Transcription

1 White Paper Powerfully Simple How ToolsGroup s SO99+ Complements SAP APO March, 2014

2 White Paper Powerfully Simple The SAP Planning Platform 3 What s Changed? 3 More Products, Shorter Life Spans 4 Planner Productivity 4 Building on Your Investment with a Layered Approach 4 A Fundamentally Different Approach 5 Forecasting Volatile Demand 6 A Case Study - Systagenix 6 Synchronizing with the Demand Signal ( Demand Sensing ) 7 Anticipating End Consumer Market Demand 8 Unlocking Your Data to Address Your Planning Issues 8

3 How ToolsGroup s SO99+ Complements SAP APO 3 Although SAP s Advanced Planner and Optimizer (APO) and ToolsGroup s SO99+ are both supply chain planning (SCP) systems, they coexist in many company s supply chains. This short whitepaper will explain how and why. We ll take a closer look at each offering, their position in the market, and in what circumstances they complement each other. The SAP Planning Platform By selling to its ERP installed base, SAP APO has accumulated a large number of implementations, nearly all of which are what analyst firm Gartner calls Systems of Record (SOR), a foundational planning layer that supports and integrates demand/ supply planning processes*. An SOR forms a platform for a variety of demand and supply planning processes. In effect it is the planning repository for an enterprise supply chain. But many employing APO find it challenging. Gartner reports that APO is seen as expensive, with longer than-average implementation times and higher-than-average service-to-software costs. This added cost of ownership can come from the disconnection between the complex environments many companies face today and the environments APO was designed to handle. Most of today s supply chains are far more challenging than when APO was created in the 1990s. What s Changed? Let s start with forecasting, where there has been an explosion of available data. Growing businesses complexity has been driven by multi-channel marketing, increased influence from demand shaping (Media, Promotions, NPI), and the impact of the internet on buying behavior - to name a few. To manage and ultimately profit from this complexity, marketing departments are investing in modern data infrastructures that unlock valuable clues to customer sentiment and behavior, even including technologies such as real time POS data and social media channels. Unfortunately SAP legacy forecasting systems were not designed to take full advantage of this new data and unlock the insight within. Most are still using forecasting approaches based on algorithms and time series of aggregated sales history. This inability to integrate, analyze and take advantage of increasingly available data is causing forecast accuracy to get worse when it could be getting better. We see companies with Item-Location forecast accuracy (MAPE) of 70 percent or even less. * All Gartner references, except where otherwise noted, are taken from Gartner s Magic Quadrant for Supply Chain Planning System of Record by Tim Payne, published March 6, 2014.

4 APO also applies a traditional top-down approach to forecasting based on aggregated data. High level forecasts are then typically split to an Item-Location level of detail for inventory and replenishment planning. Aggregating demand smooths out variability, making it easier to generate a high level forecast, but the Item-Location level forecast quality is poor because demand signal detail is dismissed along with the noise. Crucial granular information about volatility and error is lost in the process. 4 For simple and highly predictable businesses with a few fast-moving commodity items and a single channel distribution, this approach may be acceptable. But for most companies, the operational forecast that drives inventories and replenishment is well off the mark. To illustrate, when one of our customers ran a benchmark study of its APO system, forecast error grew by more than 40 percent when monthly data was split into weeks. It also increased by 40 percent when National/SKU aggregates were split into SKU/Ship-From detail. More Products, Shorter Life Spans Another change is more products, shorter life spans and an explosion of product options. These characteristics add complexity to the supply chain and increase demand volatility, intermittent or lumpy long tail demand. Forecasting in this environment requires understanding Item-Location demand signals (customers trending up and down, regions growing or shrinking, SKUs exhibiting unusual behavior) because the most significant information about variability and volatility lives not in the aggregate, but in a granular level of detail. In addition, statistical models of Item-Location level demand are required to correctly determine inventories serving intermittent demand. Even when forecast quantities are identical, variables like order size and frequencies have a major impact on the right safety stock and replenishment levels needed to reach the target service level. So the only way to accurately define inventory mixes with longtail SKUs is by analyzing detailed data. Planner Productivity As supply chains become more complex, users find that their planners spend too much time on data manipulation to generate a reasonable forecast. Forecasting algorithms need to be adjusted, inventory targets reviewed, unforecastable items addressed. This time-consuming grunt work reduces the time available to work with commercial teams to enhance the forecasts. A manually intensive forecasting and planning process leaves little time for the team is free to focus on what they do best fine-tuning the plans using their market and business knowledge. Building on Your Investment with a Layered Approach There is a solution. Gartner refers to planning systems that address these challenges as Systems of Differentiation (SODs) and Systems of Innovation. They can be thought of as add- on solutions that fill the white spaces in the planning process. Gartner says, The need for a higher-quality plan will normally dictate higher planning process maturity and the use of SCP system of differentiation (SOD) solutions to help create the higher-fidelity planning data.

5 ToolsGroup s solutions can be deployed to build upon the APO platform to leverage a company s data to improve forecasting and demand management. Diagram 1 below illustrates this layered concept; Diagram 2 shows the concept in more detail, including SAP APO as a System of Record and ToolsGroup as a System of Differentiation. 5 Diagram 1 Diagram 2 Source: Gartner s Getting the Right Technology for Your Supply Chain by Time Payne, ESCL Conference, April 2012 A Fundamentally Different Approach ToolsGroup leverages a fundamentally different approach to tackle these more challenging environments called Demand Modeling. It brings a new level of automation and machine intelligence to the planning process. It goes well beyond the standard time-series forecast to build an order-line-based baseline forecast from the bottom up. It expertly handles fragmented intermittent demand, new products and seasonal changeovers. And it does it automatically, using readily available detailed data. The ToolsGroup solution is designed from one unified model of real world behavior. Instead of looking at demand as a sequence of independent processes separated by multiple inventory levels, it views it as a single demand signal spanning the entire supply chain. This demand signal contains highly detailed stochastic information preserving full variability and volatility detail, not just about the demand quantities, but also the order-line frequencies and quantities per order-line. This approach also minimizes the bullwhip effect. This is a true bottom up approach to forecasting demand from the most granular level. It avoids the need of any splitting the statistical forecast, with their inherent assumptions and approximations. It provides a means to generate a forecast for every single SKU/locations. No exclusions. No "unforecastable" items. It provides complete visibility across all products and all levels of the supply chain. We ll look at three typical scenarios where this technology has been applied: Forecasting volatile demand Demand sensing Anticipating end consumer market demand

6 Forecasting Volatile Demand A data-driven approach using more intelligent software has been shown to create a major improvement in demand visibility, forecast quality and level of forecast detail, which are all critical for reliable supply chain planning. ToolsGroup s demand modeling creates a reliable baseline demand forecast, and then automatically adjusts the baseline by identifying the effect of stimuli and demand indicators at a detailed channel level. It creates accurate Item-Location level forecasts, handles volatile and long-tail SKUs and increases planner productivity. It analyzes all the relevant variables and the complex interactions among them in a highly automated fashion. It harnesses the power of machine learning to accurately model demand in difficult forecasting scenarios such as trade promotions, new product introductions, extreme seasonality and product cannibalization. 6 A Case Study - Systagenix Let s look at a typical situation. Wound care products manufacturer Systagenix s products are distributed to 100 countries. Despite significant demand variation, they required at least 98 percent service levels, and therefore accurate forecasting. Their customers preferred products early in their shelf life, requiring lean inventories. Unfortunately Systagenix found their SAP APO system required too much timeconsuming data manipulation to generate a reasonable forecast. It also reduced the time available to work with commercial teams to refine and enhance the forecasts. Supply Chain General Manager, Alastair Mitchell, described the situation, Given the diversity of our global sales channels and APO customization that was necessary, we were dedicating significant time and expense to adapt the system to our needs and repeat the process every time we needed further changes. This wasn t really a viable option given the dynamic nature of our global business and pace of innovation. Systagenix set out to find a more user friendly, low touch tool that could deliver a stable, accurate forecast and optimized safety stock levels. Mitchell summarized, I really set out looking for a forecasting tool that would free up my team from having to engage in non-value-added transactional work. After evaluating several alternatives, in July 2013 Systagenix completed a successful seven month pilot, and then went live with the hosted SaaS version of the ToolsGroup SO99+ software. The system forecasts SKU level demand by individual market and then calculates safety stock targets at six 3PL stocking locations across the global supply chain for all 300+ SKUs. SO99+ extracts data from Systagenix s SAP ERP system to automatically calculate a demand forecast. Next, the forecasts are refined further with input from the commercial team before finally being used to calculate optimized safety stocks based on target service levels. Finally, the forecast and these dynamic safety stocks are loaded back into the ERP system, which then executes the planned replenishment actions. Explains Mitchell, SO99+ has a unique ability to factor in the demand variability at the order-line level in order to optimize our safety stocks. Despite improving service levels to 99 percent at their 3PL distribution sites, inventory levels have been reduced by up to 15 percent. On this measure alone, Systagenix calculates that the ToolsGroup investment will have paid off in the same year as implementation.

7 As for planner productivity, the monthly global forecast that used to take the two people an entire week now can be accomplished by one full-time person in a single day. This forecaster s remaining time is also used much more productively and satisfyingly, to refine the forecasts with input from the commercial team. The second forecaster now supports another part of the business. 7 Synchronizing with the Demand Signal ( Demand Sensing ) Demand Sensing allows companies to incorporate detailed short-term demand data into their forecasts to reduce forecast error by up to 50%, increase inventory accuracy by up to 20%, and optimally deploy downstream (e.g., Distribution Center) inventory. Downstream data, such as customer and channel data, is employed to identify demand trends, provide advanced warning of problems, and remove the latency between plan and what is really happening in the supply chain. The quicker deviations can be identified, the quicker and more intelligently a company can respond. For example, when one of our customers compared Ship-To demand forecast to translated POS demand signal, forecast error and bullwhip were reduced by an average of almost fifty percent (48% across the benchmark). Within the replenishment horizon, forecast accuracy increased by 13 points, from 73 to 86 percent. Demand Sensing imports fresh daily demand data, immediately senses demand signal changes compared to a detailed statistical demand pattern, and evaluates the statistical significance of the change. It analyzes partial period actual demand to perform automatic short-term forecast adjustments using probabilistic pattern recognition and predictive analytics. Advanced statistical analytics identify and rapidly react to replenishment issues or sudden changes in customer demand. ToolsGroup s Demand Sensing can analyze the demand signal from the next downstream step in your demand chain, such as at a Ship-To store or warehouse. This can also include POS data or EDI transactions, expanding the ability to sense the extended supply chain. A good example is Costa Express, who utilized machine telemetry feeds of real-time POS data from their self-serve coffee locations to drive demand, inventory and replenishment planning, significantly reducing field inventory stock and ingredient costs. For a two-minute video describing the Costa s use of downstream POS data to accelerate their business outcomes go to: multimedia/customer-videos/item/284- costa-express-interview.html#content A more complete Costa Express case study can be found at:

8 Anticipating End Consumer Market Demand For some companies, the key to a true market-driven forecast is to begin taking advantage of the wealth of market data to understand the impact of more complex demand drivers such as macroeconomic trends, social media and customer web behavior and to use that understanding to drive improved forecasts. This is a more challenging goal. First, there is the issue of obtaining, storing and modeling the data. In the scenarios described earlier, most companies already have the data they need, even if they weren t making best use of it. In this scenario, companies often must be able to access, assimilate and analyze large quantities of unstructured big data. They may even require more extended use of a demand signal repository (DSR) that integrates and cleanses many disparate demand data sources. A predictive demand analytics or machine learning engine is also required to translate the data into actionable information. These more sophisticated tools can deal with the volume, variety and velocity of required data and can handle unstructured or incomplete data. For example, Danone deployed such a system for trade promotion and media event forecasting, achieving a 20 percent reduction in forecast error, 30 percent reduction in lost sales and a 30 percent reduction in obsolescence. The complete Danone case study can be found at: 8 Unlocking Your Data to Address Your Planning Issues Supply chains are constantly striving to improve their grasp on demand and optimize their forecasting and planning practices to keep pace with the explosion of data. Systems such as SAP APO were just not designed to handle this new reality. The inability to do so limits the effectiveness of these systems and also continues to bring less than optimal solutions for the companies leveraging the systems. The solution is to turbocharge your existing system. The data is there. Find a way to take full advantage of your investments in existing systems and the ever growing abundance of valuable data. ToolsGroup SO99+ solution is Powered by SAP NetWeaver and available as SaaS.