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

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 

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

Depending on the industry, a supply chain could be as straightforward as a few retail shops, warehouses, and trucks, or as complex as a sprawling network of mine sites and processing plants connected by rail and sea transport. From a higher perspective, however, all these supply chains are linked in one way or another through an intricate web of interactions.

Each of these interactions has its own challenges and complexities. Let's take the mining supply chain as an example to illustrate these challenges and complexities. Planning a mine site requires consideration of what grade of ore is required at what point in time, coupled with truck and digger availability, workforce rosters, maintenance schedules, and more—which is just the first step in the process—followed by the scheduling of trains that will transport the ore from various mine sites to the port, where the coordination of stackers and reclaimers happens to ensure that each ship is loaded on time.

After that, there is more, as the ore arrives in another country and is heated by coal (which arrived at the furnace through a similarly complex supply chain) to become steel, which in turn is molded into the car’s frame and doors and hood—components that represent just a handful of the 30,000 parts that make up the average car, each of which has their own supply chain from raw materials to finished part. On top of this, the automaker needs to predict consumer demand for its cars across different countries—a difficult problem in itself—all while the cars are being assembled and placed on ships for transport to those markets.

At every step, there is complexity, and the more steps we consider together, the more complex the problem becomes. This inherent complexity makes supply chain problems particularly well suited for the application of Artificial Intelligence. At the highest level, these are the core components of a supply chain operation: predicting demand, planning and scheduling production (whether it be the production of iron ore from a mine or the assembly of cars in a factory), and then organising logistics and distribution. Each component represents a complex business problem in itself, and together, an almost impossible challenge for humans to solve. This is where leveraging Artificial Intelligence can optimise decision-making across a variety of supply business functions, be it improving the accuracy of demand forecasts, or optimising inventory levels, or creating schedules that are optimised for various constraints and objectives.

Contact us to request a soft copy of Chapter 10 of the book The Rise of Artificial Intelligence discussing supply chain optimisation.