An ongoing challenge for modern-day demand planners and inventory managers is improving the accuracy of demand forecasts. Besides the algorithmic challenge of minimising the forecast error, there is also the challenge of understanding and optimising the trade-off between working capital costs and service levels to customers. In complex operating environments where many variables impact on the actual demand realised for specific products in specific geographies, demand forecast accuracy can quickly degrade, resulting in:
Employing almost 100 staff across 4 offices, Complexica is a leading provider of Artificial Intelligence software applications that can optimise sales, marketing, and supply chain decisions. Our investors include MA Financial Group (ASX:MAF), Flinders Ports, and Microequities Asset Management (ASX: MAM). We were founded upon the research of several world-renowned computer scientists, and have commercialised a modularised software platform called Decision Cloud® that empowers staff across multiple business functions to make better decisions. Decision Cloud® is powered by our award-winning Artificial Intelligence engine Larry, the Digital Analyst®:
We maintain research partnerships with the Polish-Japanese Academy of Information Technology, the University of Adelaide, and RMIT University, and also have a Scientific Advisory Board that includes international thought leaders in the area of Artificial Intelligence. Our enterprise software employs techniques from a range of fields, including deep learning, predictive analytics, machine learning, Artificial Intelligence, simulation and big data analytics to solve complex business problems and deliver value.
Complexica can help you address these commonplace challenges through our Demand Planner module of Decision Cloud®, which provides robust and easy-to-use demand planning, forecasting, and analysis capabilities.
Demand planning is a supply chain management process that allows a company to project future demand and successfully customize the company's production, whether products or services, according to. demand planning is the supply chain management process for forecasting demand, so that products can be delivered reliably and.
Demand forecasting provides insight into your next cash flow so companies can more accurately budget supplier payments and other operating costs, while continuing to invest in company growth. Demand forecasting is the process of making estimates of future customer demand over a defined period, using historical data and other information. Demand forecasting is especially useful for products that have an expiration date, such as in the food or chemical industry. Without adequate demand forecasting, you risk losing money if you produce too many items and have to sell them at a discounted price as their expiration date approaches.
In addition, unnecessary storage costs are incurred when too many products are produced. Demand forecasting is known as the process of making future estimates in relation to customer demand for a specific period of time. In general, demand forecasting will take into account historical data and other analytical information to produce the most accurate predictions. More specifically, demand forecasting methods involve the use of predictive analysis of historical data to understand and predict customer demand to understand key economic conditions and help make supply decisions crucial to optimizing business profitability.
Demand management can also be used to reduce demand. This encourages customers to talk on the weekends rather than during the week to reduce service demand. Demand management is a planning methodology. Companies use it to forecast and plan how to meet demand for services and products.
Demand Management Improves Connections Between Operations and Marketing. The result is closer coordination of customer strategy, capacity and needs. Demand management involves forecasting and planning to meet customer demand. An example of an activity that illustrates demand management is sales forecasting, customer evaluation, and modeling.
Rather than using a pre-defined set of rigid models that are based solely on statistical methods, Complexica’s Demand Planner relies on a wide variety of predictive algorithms and techniques that are tuned to your internal sales data as well as external sources and available overlays to improve accuracy.
Based on advanced statistical models and Artificial Intelligence methods such as Deep Learning, Demand Planner can help your organisation move demand planning away from an anecdotal and “gut feel” approach, to a data-driven and market intelligence capability. In particular, we can help you:
Most demand forecasting applications are based on standard statistical models. Some use other techniques such as event-based simulations, neural networks, agent-based modelling, fuzzy logic etc. Any single technique is sub-optimal by definition (better or worse for different instances of a problem).
Complexica’s Demand Planner relies on an ensemble model – a combination of statistical techniques and adaptive AI algorithms. Statistical techniques provide “sensors” to identify new conditions, whereas AI algorithms provide the learning that allows prediction models to automatically adjust to the new conditions. Over time, the prediction model learns from different environments and can “anticipate” changes. This enables continuous improvement in forecasting accuracy.
In addition, each model is fed with both internal data (historical sales, customer forecasts, promotional and pricing information for each product, etc) as well as relevant external data to improve accuracy.
As a result, the system can provide a number of tangible benefits, including:
· Improved forecast accuracy
· Reduction in finished goods inventory
· Reduction in stockouts, leading to a corresponding increase in customer fill rates
· Less time and effort for inventory planning and replenishment, with some tasks being reduced from a few days to a few hours
Contact us to request a soft copy of Chapter 10 of the book The Rise of Artificial Intelligence discussing supply chain optimization.
Learn more about Complexica's Demand Planner here
The ability of Larry, the Digital Analyst® to learn and improve is directly tied to the variables that were initially considered in the underlying models. As long as the number of variables doesn't change within these models, then the process of re-training and learning is automated. However, sometimes the models may require extensions to the original number of variables to factor in new channels, product categories, competitors, or even force majeure events that alter economic and market conditions (such as the recent COVID pandemic). These additional variables require retraining of the original models so that Larry's self-learning process can retain integrity and continue to generate meaningful predictions and recommendations.
There are many different AI-driven approaches for optimising sales activities, with each approach varying significantly in terms of what problems it can solve and the types of use case can be handled. Broadly speaking however, these approaches can be grouped into two categories:
1. “Mass AI”: In this category, there are products with highly scalable “model-ready” algorithmic approaches that can be deployed “at the press of a button.” The use-cases revolve around well-defined areas such as lead scoring, lead prioritisation, propensity to close, pipeline coaching, but are unable to extend into more complex areas like pricing optimisation, predicting churn, automated generation of Next Best Conversations (NBCs) or Next Best Actions (NBA), and more.
2. “Targeted AI”: in this category, organisations have developed a set of algorithmic techniques for specific industries and complex areas (like pricing, churn, and more), as well as the know-how on how to apply/combine these algorithms for various use cases. Each time these algorithms are applied, there is a need for training and tuning on customer data in order to properly address each area and optimise performance.
Complexica’s Customer Opportunity Profiler (COP) sits within the second category, enabling optimised recommendations that are targeted to specific industries, processes, and use cases (as opposed to the generic nature of recommendations from AI approaches in the first category). As an example of this granularity, Complexica's Customer Opportunity Profiler (COP) can generate a detailed, sales-rep specific Next Best Conversations (NBCs) that are tuned to the exact nature of a business, it's customer base, product range, pricing structure, competitive position in the market place, and more.
Examples of NBCs across different industries include:
Complexica's approach of using Targeted AI is able to generate the most value through:
Learn more about COP here