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® can be applied to a variety of business functions and application areas – such as increasing the sales effectiveness of call centres and in-field sales reps, maximising customer loyalty, capturing revenue and margin opportunities through optimised promotional planning, influencing customer buying behaviour and preferences, understanding price elasticity, optimising production plans and schedules, predicting demand, and improving trade spend outcomes – among many others. The way Larry "works" within these application areas is different, because the problem and outcomes are different. However, even within the same application area, Larry will still "work" differently from one customer implementation to another, because the process of training and tuning the algorithms involves using different training datasets and customer-specific parameters (e.g. guardrails, objectives, business rules, constraints, etc.). Hence, even though Larry is the "same" software product, it will "work" differently from one customer the next and no two deployments will ever be exactly the same.
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
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