Use Case: Precision for Supply Chain Management with Oracle Demand Management Cloud

A leader in medical technology has encountered challenges in enhancing its supply chain for various products. Despite an effective forecasting approach and the use of nodal tuning for demand forecast accuracy, improvements plateaued. This is where Analytical Factor stepped in, increasing forecast accuracy by an impressive 6 percentage points.

Forecast Accuracy Increase Drivers

1. Segmentation – Analyzing data as a single entity proved inefficient. Data was divided into two main streams. Stream #1 data includes all sales transactions contributing to the company's earnings, whereas stream #2 data includes items like samples vital for operations but not directly profitable. Analytical Factor decision to further segment data based on product behavior and geographic regions, transitioning from 2 to 10, significantly sharpened forecast accuracy for specific data groups.

• Data Characteristics-Based Segmentation - Differentiating data based on its unique traits, such as intermittent sales patterns, varying levels of fluctuation, or the pace of change (slow versus fast moving), enables the application of models and settings best suited for each specific data cluster.
• Geography-Based Segmentation - The demand patterns observed in one geographic area may not apply elsewhere, making it crucial to account for the distinct characteristics of each region. This approach enhances the precision of demand forecasting by tailoring it to the specific needs of different areas.

2. Causal Factors Identification – Through interviews with the customer’s planners and in-depth data analysis, new and existing causal factors were identified. It's important to differentiate between global causal factors, like seasonal trends that affect all product-region combinations, and local causal factors, unique to specific product-region-time scenarios such as promotions or special events. This distinction allowed for a more targeted forecasting approach by matching relevant causal factors with the newly segmented data groups.

3. Fine-tuning the Statistical Engine – Enhancing the statistical engine within the demand management system was key to boosting forecast accuracy. Analytical Factor’s fine-tuning involved refining the algorithms to closely match customers’ unique demand patterns, with special attention to seasonality, trend and product demand variability. The effectiveness of this fine-tuning was significantly amplified by incorporating local causal factors and employing precise segmentation. This approach ensured that the adjustments made to the statistical models were not only tailored to customer needs but also fully leveraged the detailed insights provided by proper segmentation and causal analysis, leading to a more accurate and comprehensive prediction of future demand.

4. Switching to Oracle Demand Management Cloud (ODMC) – The customer’s move to ODMC from an on-premises Demantra system marked a significant upgrade, particularly in forecast accuracy and supply chain management. ODMC’s key advantage lies in its built-in data segmentation capabilities, which the on-premises system lacks. This feature simplifies the application of tailored models for different data groups, enhancing forecast precision. Furthermore, ODMC makes integrating external data straightforward, easing the incorporation of new causal factors and bolstering the adaptability and efficiency of the customer’s supply chain strategies.

5. Education and Training – Customized educational sessions and on-site training empowered the customer’s planning team with a deeper understanding of the system, alongside mentoring in optimizing planning processes.

Why Choose Oracle?

The customer partnered with Analytical Factor for its expertise in elevating forecasting systems from good to great. Analytical Factor's demand forecasting and strategic segmentation skills and its specific experience with Oracle Demand Management Cloud (ODMC) and demand management systems made it the ideal choice. This collaboration aimed to enhance customer’s supply chain efficiency and operational agility. By tapping into Analytical Factor’s experience with ODMC, the customer could streamline its processes, improve inter-departmental data visibility, and maintain its competitive edge in the industry.

Partner

Oracle partner, Analytical Factor, boasts unparalleled, in-depth expertise with the Oracle Demand Management Cloud (ODMC) solution, specifically around forecasting analytics. This deep understanding, combined with our extensive data science and statistical modeling capabilities, sets us apart. We are not just familiar with ODMC functionalities – we dive deep to unlock its full potential, tailoring it to your unique business needs and maximizing forecast accuracy through advanced analytic techniques.

Elevate your supply chain management with our expertise. Contact [email protected] to explore how our innovative strategies can improve your forecasting accuracy and strategic decision-making, driving your business toward greater success.