How can you improve demand forecasting with better inventory management?

There are two issues which are interrelated. 01. Demand forecasting: better forecast accuracy & less forecast bias will always ease better inventory management. For better forecast, it is important to analyze past data & service loss reasons scientifically along with quality human judgement. 02 Inventory Management: Scientific norm & replenishment system are key for good inventory management. Rather than producing final product, common inventories should be kept as raw as possible to minimize wastage.

Few things are important before any plan 1. Nature of product 2. Procurement cycle time 3. Readiness of product 4. Price fluctuation Use usual method of forecasting but keep in mind no one fit for all formula work in SCM and business.

Apart of using historical data, important is to understand the impact that extraordinary events create in the demand, such as price increase, cogs increase, exchange rate fluctuation. All the above need to be used to adjust (in plus/minus) the historical data to get the most accurate forecast demand.

In the context of Humanitarian Logistics, especially in Health Logistics, the ability to analyze and understand data is fundamental to efficiently managing the supply chain for health-related goods like vaccines, medicines, and medical equipment. Data-driven logistics can optimize the supply chain by forecasting even irregular and emergency demands, monitoring inventory levels, and ensuring the delivery of critical medical supplies to remote or affected areas, all the while adapting to emergencies and outbreaks, considering infrastructure affected by conflict.

In today's VUCA business landscape, optimizing inventory management has become critical to achieving operational efficiency & cost savings. Traditional methods of demand forecasting, relying on historical data and manual calculations, are increasingly becoming outdated and ineffective. To address the challenges posed by redundant past data, businesses are turning to AI-based predictive and prescriptive forecasting. Few tools and methodologies which can be used are Real-Time Data Analysis, Sentiment Analysis, Machine Learning Algorithm, Prescriptive Insights, Risk Mitigation Strategies & Customer Centric Approach. All these tools shall help in reducing forecasted demand variability from actuals and shall help maintain lean inventory levels.

Safety stock Estimation:Calculate safety stock levels to account for unexpected fluctuations in demand. Ensure it's based on realistic scenarios and not just worst-case assumptions.

All perspectives are relevant in manufacturing. One addition is to set inventory management policies by considering ABC/XYZ analysis, safety stock levels set by CV %, service levels, statistical safety stock levels, fluctuating lead times, and seasonality of production. In doing so, organizations can better support the Demand/Forecast plan for the set revenue goals. The key is to not to run out of inventory while accounting for demand fluctuations/managing optimized inventory levels by set policy in serving customers better.

To achieve this, businesses should start by clearly defining their inventory management objectives, such as minimizing holding costs, meeting customer demand, or supporting production needs. These objectives should be aligned with broader business goals like profitability and customer satisfaction. Next, companies should establish robust communication channels between departments like sales, procurement, and production to share data and insights. Leveraging technology and data analytics can aid in real-time tracking of inventory levels and demand patterns, enabling proactive adjustments to policies. Regularly reviewing is also a must for sustained alignment and optimal inventory management.

Improving demand forecasting through better inventory management is essential for businesses to optimize their supply chains, reduce costs, and meet customer demands efficiently. Here are several ways to achieve this: Data Analytics and Forecasting Tools: Utilize advanced data analytics and forecasting tools to analyze historical sales data, market trends, and other relevant factors. This can help you make more accurate demand predictions. Collaboration with Suppliers and Customers: Collaborate closely with your suppliers and customers to share data and insights. This can help both parties make more accurate forecasts, leading to better inventory management.

Implementing inventory optimization techniques is crucial for effective demand forecasting. It involves classifying, segmenting, replenishing, allocating, and reviewing inventory to balance costs and maximize performance. By categorizing items based on importance, demand, and profitability, and aligning replenishment with optimal quantities and frequencies, you enhance accuracy and efficiency. Allocation based on optimal distribution and location further streamlines operations. Regular review ensures adaptability and responsiveness, ultimately improving demand forecasting accuracy and overall inventory management, contributing to cost savings and customer satisfaction.

In a world where AI, algorithms and digitalisation are the new names of the game many end users are considering Additive Manufacturing (3D printing) in hopes to make savings and reduce their inventory. For this purpose and not really knowing where to start from they are contacting 3D printers. As a matter of fact I believe this is not the right approach. There are companies agnostic to any printing processes or factories, studying your full inventory and funnel it down to what should be 3D printed. It is then easy to optimize your inventory by deciding if you want these parts digitalised in your own inventory, develop local content and smart local manufacturing by knowing precisely the volume you can propose to the market etc...

That's good to use the optimization inventory technique, so that technique, I can tell about analysis ABC to determine the items on which business would concentrate these efforts, in addition, vendor managed inventory

An important aspects to inventory optimizations are to view inventory on higher level, such as country level or even region level to rebalance excess inventory and only replenish net shortages. Stocking policies basing on importance of parts to the business will reduce the logistic costs of expediting replacement due to shortages.

Every aspect mentioned in this article is imperative enough for Demand forecast and plays equal role in assisting the execution of better Inventory management. I believe monitoring the performance of the Inventory has an edge in supporting the subject call. Inventory management works on 80/20 Rule at par and so is the demand. There are various means which a Inventory manager or the team can fall back upon to monitor the graph of the Inventory, storage and delivery. Graphical, Image, Historical and Current Data representation of the Stocks and Daily review meetings discussing the current trends could be the tools on projecting forthcoming demands and storage requirements.

It's very important to monitor and evaluate your inventory performance, sticking balance between to meet the customer and avoid excessive stock. The KPI are the perfect tools to do them.

Appoint a "Inventory Conductor" who will coordinate inventory decisions based on current information to ensure perfect harmony between supply and demand.

Right product Right place Right time Alway need to meet the business requirements, help profitability & product availability will help customers satisfaction

Reading this article and comments was enlightening. While it covers demand forecasting comprehensively, one crucial variable is missing: "climate change." Logistics, vital for any project or business, is now greatly affected by climate change. A case in point is the PANAMA CANAL, managing 40% of global maritime traffic. Reduced rainfall has partially shut down the canal, causing delays and port disruptions due to "EL NINO" patterns. There's an 80% chance this will persist until February 2024. To cope, fresh water is being added despite a 20-year drought in the region. Integrating climate change into demand forecasting is now essential due to its far-reaching impact on supply chains and operations.

Setting the reorder point and maximum stock level parameters are the key factors to optimize the inventory levels as per the demand forecast. Also, prioritize and percentage allocation of the forecast to the order processing would be helpful to maintain the JIT (Just In Time).