Leveraging on existing Forecast Technology to Enhance Business Functions

Luidi Maia, NSAM General Manager SLSS, OneSubsea a Schlumberger Company
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Luidi Maia, NSAM General Manager SLSS, OneSubsea a Schlumberger Company

Luidi Maia, NSAM General Manager SLSS, OneSubsea a Schlumberger Company

Global large-scale enterprises are constantly challenged to optimize their business functions and truly leverage on their size and scale to deliver superior value.

With the development of technologies such as cloud-based storage, data cubes or DSR (Demand Signal Repository),IoT (Internet of Things) and DSA (Demand Signal Analytics) it has become possible to address a fundamental challenge that every business function face on a daily basis in such global organizations. It is the ability to produce fairly accurate global forecasts on a regular basis, which can then be used to understand from a global perspective, the amount of resources required in different parts of the organization and allow an agile redeployment or optimization of existing resources—be it information, inventory, assets or personnel so that the several different business functions can effectively work together and deliver superior value to their customers and shareholders.

Cloud based storage and DSRs, are solving an important challenge of business systems integration and master data consolidation. Most of these large global enterprises have grown through acquisition or fast paced organic growth so it is very common to find a myriad of ERP systems in such companies that normally don`t interface seamlessly with one another. This is true even though some of them are just different versions from the same supplier, typically SAP, Oracle, Kenandy or Microsoft Dynamics just to name a few of them. Unless the data can be stored and analyzed at the global level, it becomes very challenging to have a global perspective or generate accurate global forecasts for any aspect of the business.

  Cloud based storage and DSRs, are solving an important challenge of master data integration and enabling a global approach to cost optimization versus product availability using advanced analytics​  

DSRs as defined by Gartner, are centralized databases used to store, organize and harmonize attributes for large volumes of demand data—Point of Sales data, wholesaler data (promotional data, inventory movement,  electronic data interchange), and customer loyalty data for use by decision support technologies (category management, account team joint value creation, shopper insight analysis, demand planning forecast improvement, replenishment, etc.).

This concept can be expanded further to include data generated by IoT. As an example, spare parts replacement rate, number of operating hours from a given machine, vibration level, temperature and many more attributes or parameters that could be centrally stored and used for replacement parts forecasting, maintenance work order forecasting and maintenance crew size forecasting.

Basically, any type of internal or external demand signal can be stored into a DSR system for further analysis and forecasting usage, by the distinct parts of the organization.

The cloud-based storage technology has become the backbone for global enterprises in the big data world, given the amount of data collected and generated by modern forecasting systems. Recently global enterprises have been shifting from yearly, quarterly, monthly or weekly forecasts, to daily/hourly item-locations forecasts combinations using the demand sensing capabilities of advanced supply chain solutions, which demands dynamically scalable system supported by the cloud.

The next big innovation on demand forecasting technology is called Demand Signal Analytics. This is a platform that allows the integration of POS data with company specific and external attributes like weather forecast, economic conditions, oil price or similar causal factors into demand forecasting to predict and automate short to mid-term demand.

A typical DSA implementation would include 3 layers. The first one being the DSR which will essentially consolidate the various sources of data—internal and external coming from the ERP systems or any other source such as IoT, POS, wholesalers, social media, weather, EDI, syndicated scanner, promotional, marketing etcetera, and make such data packages available for query, download, reporting, alerts and analysis. 

The second layer is comprised by a Business Intelligence platform, which allows data visualization by data scientists or business planners to explore, analyze and produce standard reports at global, regional or business unit level for focus areas, improvement opportunities and action required in distinct parts of the organization. Those reports can then be immediately used by the several business functions within the enterprise to enhance their effectiveness.

The 3rd layer would include a combination of forecast and optimization engines which will automate demand forecast and facilitate the modification of forecast models interactively without programming. Once the forecast models are automated and vetted by business managers, it can be directly put into resources optimizers which would recommend resources allocation as per automated forecasts and optimization models.

The outcome of the optimizers recommendations can then be used by different business functions to drive efficiency within global organizations and truly use their size and global footprint as a clear competitive advantage.

The expected outcomes from global companies that are evolving or are already on the advanced stages of becoming a demand-driven organization with best in class forecast technology are inclusive but not limited to an improved forecast accuracy by truly utilizing demand sensing and demand shaping activities. The significant reduction on lost sales and process downtime due to products or parts stock outs, fast detection and proactive action over product category demand changes, better evaluation of new product information or sentiment analysis, increased trade promotion effectiveness and proper focused direction to the sales group efforts, reduced inventory and safety stock levels by redistributing localized overstock for globally needed products, minimize overspend and overproduction for products with declining shot term forecast based on live POS, IoT or, syndicated scanner data.

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