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:
No customer data is ever shared, re-used, or re-sold in any way. Each Complexica customer has their own AWS instance where Larry, the Digital Analyst® is trained and configured using that customer's data plus any relevant external data. Customer data is also covered under the provisions of the Confidentiality Clause of Complexica's standard terms & conditions.
This is a commonly asked question and the answer is no, because each instance of Larry, the Digital Analyst® (along with the relevant Decision Cloud® module) is configured to the specific business rules, constraints, and objectives of each individual customer. These configurations will never be exactly the same. Furthermore, the algorithmic techniques within Larry, the Digital Analyst® are trained on customer-specific data, which also differs from one organisation to the next. For these reasons and others, two competitors using their own versions of Larry, the Digital Analyst® will never get the same result, especially that these two competitors will also differ from one another in their products, brands, priorities, strategies, initiatives, and more, putting further distance between one installation of Larry and another.
Yes, Larry, the Digital Analyst® can generate decision recommendations based upon the automatic analysis of the data carried out by its underlying algorithms, rather than just providing insights or analytic reports. Hence, Larry is "forward-focused" on what the next best decision should be, rather than "rearward focused" on what happened in the past and why.
As a simple example, it's possible to define two classes of entities within Larry: Customers and Products, where customers have preferences for certain products and these preferences must be extracted from the data. The utility matrix gives (for each customer-product pair) a value that represents what is known about the degree of preference of that customer for that product (the matrix is often sparse, as most entries are unknown). Larry, the Digital Analyst® then examines the properties of products recommended to customers: For example, customer A “likes” products with characteristics X, so that Larry would recommend products with characteristic X, and analyse the similarity measures between customers and/or products (the products that are recommended to a customer are those preferred by “similar” customers, which is often referred to as collaborative filtering).