Demand planning is a critical cross-functional process, which helps to align and forecast customer demand for a product or business. It aids to optimize inventory management, avoid supply chain disruptions, and minimize supply chain inefficiencies. It also helps to increase overall customer service level, customer satisfaction, efficiency, and profitability. Demand planning compliments sales and operation strategy by providing direction and forward-looking guidance. Demand planning, including demand sensing, should be a continuous process to support effective decision making at all levels.
Topics: Demand Planning
Supply chain is considered part of every broad and little aspect of business (including raw material acquisition, product distribution, and customer service) and the Supply Chain function has added tremendous value and strengthened operational efficiency. It has also embraced a lot of uncertainty, including demand fluctuations, to support continuous improvement and deliver return on asset employed. Unfortunately, the last two years have put a lot of strain on the overall supply chain, and this has re-ignited discussion around scenario planning in supply chain, especially related to demand variability.
Businesses are dealing with very uncertain times in supply chain management, as every aspect is under a microscope. For supply chain executives dealing with these challenges and the resulting disruption, it has become important to manage unpredictable demand efficiently and optimize inventory in the network to support these demands. Over last 2 years, the pandemic has truly challenged traditional processes and shown the importance of optimal inventory across multiple echelons of the supply chain, as effective inventory management is critical to support organizational growth, impacting both top and bottom lines. The strategic understanding and execution of optimal inventory levels not only helps manage unpredictable demand, but also helps mitigate risk, manage disruptions, and support timely and effective decision making. To address these issues, a strategy that allows for improved performance is called multi-echelon inventory policy, where inventory levels of each echelon support enhanced inventory management coordination.
The world of business is always changing. To keep up, companies must embrace new technologies and methods to streamline their processes. One area that can be greatly improved with automation is revenue forecasting. Automated revenue forecasting can reduce the amount of time spent on data gathering and analysis, leading to more accurate predictions and a better bottom line. However, automated forecasting with machine learning (ML) can take things one step further by incorporating past data, trends, and other factors that may not be easily observed or quantified. This leads to even more accurate predictions and a better understanding of the business landscape.
Topics: Predictive Revenue Forecasting
Inclusive cultures strive to make people – all people - feel valued and proud of who they are. Statistically, diversity in the workplace indicates that inclusive companies are more likely to create a workforce that reflects a variety of backgrounds, experiences and needs. Often though, for a number of reasons, there is a demographic that is excluded from these efforts and that is those with disabilities.
It’s not easy being a CIO these days. Not only are you expected to keep the technology infrastructure purring along smoothly (e.g. “let’s all just work from home!”) and safely (e.g. “we can’t afford to be hacked!”) in the face of unrelenting external pressures, but you must also be an active participant in satisfying the digital-transformation needs of the business.
Forecasting is a discipline that reduces companies' exposure to risk. The process of forecasting alerts us to upcoming shortages in supply or demand, allowing us to take precautionary measures before we find ourselves in a negative cash-flow scenario. Forecasting is truly a critical business function within any organization. Automating your forecasting process can help you maximize efficiency while reducing costs and improving accuracy. This article will outline how you can use a variety of tools and approaches to automate various aspects of your forecasting process.
It is no secret that analytics and planning projects are based on data. Typically projects involve wrangling some historical datasets, each with their own peculiarities, and then adding in some projection or prediction. Because data is essential, it is also a common stumbling block as business users consider beginning a new analytics and planning project.