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 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
The process of configuring Larry's algorithms to a particular problem includes several steps:
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.
Complexica has a well-defined engagement model that progresses through the following stages:
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.
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:
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:
The monthly SaaS fee is an ongoing operational expense
One of the most common issues is dealing with missing data. Incompleteness is almost impossible to fix with any data cleansing methodology, because facts cannot be inferred that were not captured when the data was initially recorded. However, there are some techniques that can assist in mitigating this issue, like turning to other available datasets (internal or external), and looking for existing fields that could be used as a proxy to estimate the missing data. Another method (often used in promotional planning) is to use agent-based simulations to augment the existing, incomplete dataset.
There is also an entire category of business and government problems for which the data simply doesn’t exist. A few examples include:
Predicting demand for a brand-new product where there’s no historical sales to reference, and no equivalent product in the marketplace that can be used as a proxy (and yet a decision needs to be made on how many units to produce)
Deciding whether to implement a new advertising strategy or product pack size, even though that type of strategy or pack size has never been tried before
Understanding the performance of new product designs that have never been created nor tested before
Evaluating new government policies for which there is no historical data or precedent, for example, devising evacuation strategies for major cities that have never been evacuated before
For such problems, data scientists turn to simulation-based techniques to gain insight into process being simulated and into the effects of alternative conditions and courses of action.