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:
Larry, the Digital Analyst® is Complexica’s Artificial Intelligence engine and it powers the Decision Cloud® software platform of award-winning applications. Larry was developed to unlock the inherent value in large data sets by transforming raw data into optimised recommendations, so that staff across multiple business functions can make better & faster decisions.
The primary task of Larry, the Digital Analyst® is to recommend optimised decisions by centralising and managing the interaction with internal data warehouses and external data sets, and automating the associated data loading, handling, and analytical processes. Larry consists of various smart algorithms (such as Bayes nets, artificial neural networks, rough sets, classifier systems, support-vector machines, decision trees, genetic algorithms, etc.) that are used for various data mining problems, such as:
Anomaly detection (outlier/change/deviation detection) for identifying unusual data records that might be interesting or contain data errors that require further investigation. Anomaly detection is of key importance in the process of cleaning (and understanding) data
Association rule learning (dependency modelling) for finding relationships between variables. For example, a supermarket might gather data on customer purchasing habits, and use association rule learning to determine which products are frequently bought together. This is sometimes referred to as market basket analysis
Clustering for discovering groups and structures in data that are "similar." The data is viewed as points in a multi-dimensional space, and points that are “close” in this space are assigned to the same cluster. For example, an organisation may group their customers into micro-clusters for personalised promotional campaigns
Classification for classifying new data to existing categories. For example, to classify new credit card transactions into either the "legitimate" or "fraudulent" category
Regression for finding mathematical functions that model the data with the least error. In other words, regression is a statistical process for estimating the relationships among variables (i.e. the relationship between a dependent variable and one or more independent variables). The discovered relationships aid in understanding how values of the dependent variables change when any of the independent variables are varied. A common application of regression is in the area of price elasticity
Summarization for providing a more compact representation of the data, including visualisation and report generation. For example, after clustering is carried out, the clusters themselves are summarised (e.g. by generating the centroid of the cluster and the average distance from the centroid of points in the cluster) and these cluster summaries become the summary of the entire dataset
Simulation is used to evaluate a variety of scenarios, thus addressing many important what-if questions. Simulation allows for the analysis of a complex model and understanding of how individual elements interact and affect the simulated environment
Predictive modelling is used to assess the likelihood of future outcomes. It goes beyond knowing what has happened in the past (and why) by providing the best assessment of what might happen in the future. It can be used, for example, to better understand changing market dynamics, or identifying the next best action for each customer
Optimisation is used for recommending the best possible decision (whether for promotions, production, resource allocation, etc.) by identifying the key variables, business rules and problem-specific constraints, and taking into account many potential objectives (e.g. inventory levels, DIFOT)
Complexica's AI engine, Larry, the Digital Analyst® – which encompassess all these processes, algorithms, and models – was named the 2018 Australian Innovation of the Year, the 2019 Australian Software Innovation of the Year, and the 2020 Digital Transformation of the Year. Larry, the Digital Analyst® powers Complexica's modularised software platform called Decision Cloud®.
Complexica does not custom-make software applications for each customer, as this approach would be too prohibitive from a cost, time, and risk perspective. Based on almost a decade of research and development, Larry, the Digital Analyst® and the Decision Cloud® platform are software products with pre-existing algorithms, models, workflows, user interfaces, functions & features, integrations, and security controls. Hence, from this perspective, Complexica's software applications can be categorised as enterprise-grade, commercial-off-the-shelf (COTS) products.
That said, our experience is that most COTS products are unable to handle many customer-specific requirements such as complex business rules set in time-changing data environments, non-linear relationships, and non-standard business processes and workflows, among others. For this reason, the Decision Cloud® platform allows for a "customer-specific layer" to be added on top of the COTS product which caters for unique requirements without compromising the base product (or the ability to upgrade to future versions and releases). Hence, Complexica's Decision Cloud® platform combines the best of both worlds by leveraging the cost and time efficiencies of a COTS product, and the flexibility of being able to handle unique requirements within the customer-specific layer.
In an ideal world, each customer would provide Complexica with a clean and complete data set, which is well documented and highly structured. In reality, however, this is rarely (if ever) the case. Our experience with data is that most organisations suffer from missing, incomplete, and inaccurate data (i.e. "dirty data") which needs to be evaluated during the scoping phase and addressed during the software deployment phase. Because today's data comes from increasingly disparate sources and in an ever-growing variety of forms, it usually needs to be prepared to become usable. During the data cleaning process, corrupt or inaccurate records are corrected or removed from a record set, table, or database, and incomplete, incorrect, or irrelevant parts of the data are replaced, modified, or deleted. Unsurprisingly, this process (often referred to as “data cleansing”) can be demanding, but pays dividends during the software go-live phase and during usage thereafter.
Click here to learn more about data as part of the problem-to-decision pyramid.