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
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When planning for future promotions, one of the manufacturer’s goals is to maximize the “gain” from competitor products (so that consumers switch from a competing product to thier own) and minimize the “loss” from their own product range. If an increase in promotional sales comes at the cost of another one of the manufacturer’s products, this is called cannibalization, which means that consumers have switched from one of our products they regularly buy, to the one on promotion.
Through data analysis, we could create a cannibalization matrix, which is a table that outlines the expected cannibalization effect of certain products when placed on promotion. However, the practical challenges of constructing such a table are significant. Without applying any domain knowledge or business rules, it might be necessary to look up each individual product and calculate its cannibalizing effect on every other product in our range. For most manufacturers, which may sell hundreds or even thousands of products, this would result in a table with tens or even hundreds of thousands of values. While it’s possible to calculate this automatically, there’s no easy way to validate these values without going through them line by line.
A better approach is through the application of human knowledge along with various Artificial Intelligence methods; for example, by implementing human rules based on well-founded assumptions, such as cannibalization occurring across individual categories, we could capture 80% of the expected cannibalization effect with just 20% of the modeling effort, and then complement the cannibalization matrix by additional modifications that result from a deeper analysis of the data using AI algorithms.
To learn more about promotional planning and pricing, watch this supplementary video to Chapter 3 of The Rise of Artificial Intelligence, a recently published book authored by the Complexica team.