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

Many CPG/FMCG companies look for software solutions to digitalise their workflows and optimise their promotions. There are two main components to that process, and (despite many vendor’s claims) they are not the same, and are rarely executed by the same software:

  1. Trade Promotion Management (TPM) systems
  2. Trade Promotion Optimization (TPO) systems

TPM is usually the starting point for companies wanting to replace the home-grown spreadsheets and automate the labor-intensive processes for the following activities:

  • data loading and handling
  • customer planning
  • budgeting
  • promotion execution and monitoring
  • account settlement
  • monitoring and managing deductions 
  • tracking and validating claims

TPM systems are used by a broad range of users (e.g. Merchandisers, Category teams, Trade Marketing, Strategy and customer finance) for the transactional workflows associated with planning, managing and monitoring promotional activities in a more streamlined manner. 

TPM systems are transactional at their core and include basic analytics and a workflow for collating, reconciling, and visualising the past performance results. 

Trade promotion optimisation (TPO) are sophisticated systems capable of advanced analytics to enable users to automate the exploration of a large number of what-if scenarios, running optimisation algorithms across a promotional program, whilst considering various constraints and objectives (e.g. trading terms constraints, market share objectives, promotional guardrails, supplier constraints, category growth objectives, etc)

TPO solutions extract data (both transactional data as well as Promotional Slotting Boards) from the TPM system to perform its workflow and then ingest back into TPM optimised plans that deliver the right mix of promotions that drive category growth and sustainable share. It is important to note that as this is a process ridden with Science and complexity, no two deployments are the same as the algorithmic techniques need to be trained in each client’s data and the various constraints and objectives used by the Optimiser (e.g. trading terms constraints, market share objectives, etc.) need to be configured to each Client’s needs.

Learn more about TPM/TPO and how Complexica can help maximise trade promotion ROI. 

Complexica has a well-defined engagement model that progresses through the following stages:

Workshop

This initial exercise is conducted at no cost with the intent of developing a common understanding of the business problem as well as an organisation's people, processes, and technology as they relate to the current state and desired future state. The availability and quality of data is also explored during the workshop, with the output being a fixed-price proposal for scoping the relevant opportunity.

Scoping engagement

During the scoping stage, Complexica analyses the current-state data, processes, systems, and practises within the relevant business unit(s) and workflow(s) of an organisation. The goal of the scoping engagement is to define the applicable scope of the applicable software, along with the high-level business and technical requirements, proposed interfaces, as well as all deployment milestones, cost estimates, and business benefits. Each scoping is a fixed-price, fixed-time engagement that delivers:

  • A fixed-price software project plan, which includes configuration recommendations and a suggested approach for achieving the most advantageous Return on Investment (ROI) and Time to Value (TTV)
     
  • A detailed Statement of Work (SoW), which contains the specification for the software being deployed, including data inputs and outputs, integrations, use-cases, constraints, business rules, optimisation objectives, among others

Software Deployment Project

The software deployment project is broken up into a number of milestones that culminate in Business Verification Testing (BVT), User Acceptance Testing (UA), and ultimately, the go-live process. The initial milestone covers off any further detailed scoping, specification, and design work, while further milestones are used to deliver partial configurations of the software for customer validation before heading into the BVT and UAT. During each software project, Complexica’s science team also trains the relevant algorithmic techniques using customer data (along with any relevant external data) before testing and tuning the models. These software projects are capital expenditures from a budgetary point of view and overall cost and duration depends on the scope of the software modules being deployed.

Software as a Service (SaaS)

Once live, end-users can access the deployed software through a web interface (or native app) that is deployed into a dedicated, customer-specific AWS instance. Ongoing access and support for the software is covered by a monthly SaaS fees (Software as a Service, or “Online Service Fee”) which includes:

  • AWS hosting, backup, and DR
     
  • AWS computation load (as some prediction and optimisation problems are more computationally "expensive" than others)
     
  • Maintenance and support
     
  • Monitoring the performance of the underlying model(s)
     
  • Future product upgrades

The monthly SaaS fee is an ongoing operational expense 

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®.