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:

  • Expedited shipping costs due to the wrong inventory being at the wrong place at the wrong time
  • Stock-outs and compromised services levels to customers, especially on measures such as DIFOT and fill rates
  • Low inventory stock turns
  • Stock obsolescence
  • High forecast error

About Complexica

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®:

Research Partnerships

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.

Supply Chain

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.

Supply Chain

Supply Chain Management

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.

Forecasting and Demand Management

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.

Forecasting and Demand Management

Demand Management

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.

Predictive Demand Planning

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:

  • Reduce the forecast error associated with demand planning
  • Increase DIFOT and service levels to customers
  • Improve stock turns and decrease overall inventory levels
  • Improve reaction time and response to unexpected events (e.g. sudden change in demand requiring additional inventory)
  • Automate manual processes to eliminate human error and key man risk
  • Understand the impact of changes in business rules and constraints (such as lead times, capacity constraints, required inventory levels, etc.)
  • Replace home-grown spreadsheets, which are often opaque and convoluted
  • Optimise and automate the replenishment process across various time periods, product categories, lines, and individual SKUs
  • Understand customer sales trends and patterns

Frequently Asked Questions

Although real-world problems are usually comprised of smaller sub-problems (i.e. components) that interact with each other, most organizations realise that these components are related and affect one another, and what’s most desirable is a solution for the overall problem that takes into account all components. For example, scheduling production lines (e.g. maximizing the efficiency or minimizing the cost) is directly related to inventory costs, transportation costs, delivery-in-full-on-time (DIFOT) to customers, among other business metrics, and shouldn’t be considered in isolation. Moreover, optimizing one component may negatively impact another. For these reasons, organizations can unlock more value through “globally optimized” solutions that consider all the components together, simultaneously, rather than just a single component.

Many modern organizations are interested in “globally optimal solutions”—solutions that allow their organization to reap the greatest possible benefit—rather than solutions to individual components of their operation (which they have to “assemble”).

Despite the obvious observation that multi-component problems should be solved as a single problem to come up with the best overall solution, the vast majority of modern organizations don’t do this. Why? Because of how overwhelmingly complex the problem becomes when all components are assembled together, requiring not only sophisticated AI algorithms, but significant process re-engineering and change management within each component of the operation. Hence, for these reasons and others, the design and creation of such algorithms, as well as putting them into software applications that become embedded into business processes and workflows, is beyond the capability of most organizations, despite the significant benefits that global optimization can unlock.

For more information on some of the issues relating to optimisation, please refer to this chapter of the recently published book authored by the Complexica team

Larry, the Digital Analyst® is comprised of many algorithmic techniques and models that can be applied to specific business functions, such as demand forecasting, price elasticity modelling, promotional planning, omni-channel ordering, and production planning & scheduling, among others. In addition to Larry, Complexica has developed a range of software modules called Decision Cloud® to enable end-users to access and utilise Larry's algorithms within a variety of workflows (e.g. trade spend optimisation). For every new deployment where Complexica applies Larry's algorithms to a specific business function, these algorithms are trained on customer-specific data in order to enable proper use. At a high level, the learning aspects of Larry can be summarised by the following methods:

Training & deploying models

  • Self-learning feedback loops
  • Training & deploying models  

The process of configuring Larry's algorithms to a particular problem includes several steps:

Data mining

  • Learning the problem domain (relevant prior knowledge and goals)
  • Creating a target data set (data selection), including both internal and external datasets
  • Data cleaning and pre-processing
  • Data reduction and transformation (finding useful features, dimensionality/variable reduction, invariant representation)
  • Choosing functions of data mining (summarisation, classification, regression, association, clustering)
  • Choosing the data mining algorithm(s)
  • Searching for patterns and trends of interest
  • Pattern evaluation and knowledge presentation (visualisation, transformation, removing redundant patterns, etc.)
  • Establishing the key variables to be used in the models for the relevant use-case.


  • Choosing or combining Larry's algorithmic techniques for a specific business problem
  • Training the algorithmic techniques on customer data (as well as potentially external data)
  • Configuring Larry's parameters for the customer-specific use-case
  • Evaluating the model(s) performance against key metrics

Model deployment

The final step is deploying the trained model(s) into the customer-specific AWS instance and enable users to access through the relevant Complexica Decision Cloud® software module (e.g. Promotional Campaign Manager or Demand Planner)

Self-Learning Feedback Loops

Cognitive computing involves self-learning systems that use Artificial Intelligence, data mining/machine learning, visualization, and natural language processing to mimic the way the human brain works. The overall goal of cognitive analytics is to create automated IT systems that are capable of “learning from” and “adapting to” changes in the environment.

To understand the “self-learning” mechanism within Larry, the Digital Analyst®, there is a step within the modelling stage described as “Configuring Larry's parameters for the customer-specific use-case,” which works in close alignment with the next step “Evaluating the model(s) performance against key metrics”. Once live, Larry continues to evaluate the model's performance against actual results (e.g. did the promotional plan recommended by Larry actually generate the predicted uplift in sales? If not, to what extent did it fall short?). The difference detected being the “error rate," which acts as a trigger (when it exceeds an acceptable threshold) for Larry's adaptive algorithms to automatically adjust/self-tune the initially configured parameters to "learn" and improve performance in the future.

Information security is critical to maintaining the reputation and brand of any organisation in today's world, as well as its ongoing success and viability. Complexica's core security principle is to ensure the confidentiality, integrity, and availability of the data entrusted to us by our customers and business partners. To this end, Complexica has achieved ISO27001 certification, recognising its commitment to providing customers with the highest level of information security management. Following an extensive audit process, the certification was issued by TQCSI International, an accredited, third-party certification body providing auditing and certification of international management system standards with offices in more than 30 countries. For more information, please visit our information security policy page.