What tools can you use to make data-driven inventory decisions?

After having used Brand specific Inventory Management tools, such as SRD and DIM, I was convinced that a specialized tool is a must, with a high RoU from both the revenue and cost saving perspectives. Recently we have implemented Syncron, still taking its first steps but already proving itself a good investment. Naturally, as with any data based application, much care has to be taken with the quality of the inputs, as well as the setting up of the parameters.

There are several tools that can be used to make data-driven inventory decisions. Some of them include: Inventory management software: Specialized software like Fishbowl, TradeGecko, or NetSuite can provide real-time data and insights on inventory levels, sales trends, order history, and more. These platforms often offer forecasting and analytics features to help make informed inventory decisions. Point of Sale (POS) systems: Modern POS systems, such as Square or Lightspeed, not only process sales transactions but also generate detailed sales reports and track inventory in real-time.

In my experience from different past companies tools like looker, tableau, rapid response or power BI could be some of the good tools that can be used for analysis and visualization purpose. I have been using rapid response for a while now and I think it has a massive capability to perform all of these and can be tuned according to company needs

Use inventory management software, data analytics platforms, and demand forecasting tools to make data-driven inventory decisions. Consider IoT sensors, RFID, and machine learning for advanced insights.

In any automobile inventory management of bought out items strong SNOP & SOG cycles help for right sizing of inventories along with customer satisfaction. This will help for FG inventory as well. Decisions can be taken based on ABC & XYZ analysis. No complicated tools needed for decisions.

TMS and Route Optimization: AI can optimize transportation routes and schedules to minimize fuel consumption, transportation costs, and delivery times. This is especially important for businesses with complex distribution networks. Supplier Management: AI can assess supplier performance and risk by monitoring factors like delivery times, quality, and compliance. It can also identify alternative suppliers in case of disruptions. Quality Control: AI-powered sensors and vision systems can monitor product quality in real-time, detecting defects and ensuring consistent quality throughout the supply chain.

I leveraged Power BI to provide a comprehensive overview of BIAS (Bias) and MAPE (Mean Absolute Percentage Error) at both the brand and SKU levels. Using Power BI's robust data visualization capabilities, I designed interactive dashboards that allowed me to assess the accuracy of our forecasting models with ease. For a more granular analysis, I drilled down to the SKU level within Power BI. This level of detail allowed me to pinpoint specific products or SKUs that may have been consistently over- or under-forecasted. By visualizing BIAS and MAPE at this level, I could fine-tune our forecasting algorithms, optimize stock levels for individual products, and enhance our overall inventory management strategy.

Inventory management, is critical in ensuring,the right product at the right time , at the right price for the customer.This optimization can come in the form of stock on hand measures, safety stock measures, reorder points , open transfer order, in transits, write offs and damages. Either managed through an efficient inventory management system,or even through excel if need be, depending on size of business and complexity of product range. Coupled with inventory management,there are other systematic couple.on systems, from a supply chainnsystem, effectively transport management, and systems, demand planning materials planning systems (sap, logitlity , dnf , demand smoothi to name a few ) which all tie into an organizations ERP solution.

An example I know for quantification and forecasting is the quantimed software. With it you can run some analysis to help you quantify and forecast properly.

Forecasting tools 1. Moving Average Method 2. Simple Exponential Smoothing 3. Seasonal Indexes 4. Trend Analysis 5. Regression Analysis 6. Neural Networks 7. ARIMA. 8. Decision Support Systems 9. Monte Carlo Simulation 10. Time Series Decomposition

Predictive Maintenance: AI can predict when equipment and machinery are likely to fail, allowing for proactive maintenance. This reduces downtime and unplanned maintenance costs. Demand Sensing: AI can continuously monitor real-time data like social media trends, weather forecasts, and economic indicators to adjust production and distribution strategies based on changing market conditions. Warehouse Automation: AI-driven robots and automation systems can optimize warehouse operations, including picking, packing, and shelving, improving efficiency and reducing labor costs. Risk Management: AI can assess and mitigate supply chain risks by monitoring geopolitical, environmental, and economic factors that may impact the supply chain.

One thing I feel is missing is measuring expected lead times into these calculations. Lead times are critical in determining min/max levels.

Most Forecasting Tools have some kind of optimization tools included, whilst usually just on a superficial way. Using AI to our advantage will help us to gain even more insights and to make better decisions. For example you can use AI to crawl existing product and company reviews that help you optimize your product (or even the price).

Inventory optimization is a crucial aspect of inventory management that involves maintaining the right amount of inventory to meet demand, minimize logistics costs, and avoid common inventory issues such as stockouts, overstocking, and backorders. There are several solutions to optimize inventory and reduce risk.

Optimization tools are a must and need mathematical models to correctly allocate your inventory needs. These tools will help you manage your inventory and prevent shortages/overages. The balance of inventory dollars greatly impacts a business's ability to increase or maintain profitability.

Customer Insights: AI can analyze customer data to understand preferences, buying patterns, and feedback. This information can inform product development and marketing strategies. Sustainability: AI can help optimize supply chains for sustainability by reducing energy consumption, carbon emissions, and waste. Real-time Visibility: AI-powered tracking and monitoring systems provide real-time visibility into the supply chain, allowing companies to respond quickly to disruptions and make data-driven decisions. Cost Reduction: Overall, AI can help reduce operational costs, improve resource allocation, and increase overall efficiency throughout the supply chain.

Automation is a huge part in Inventory Management. The first real-deal is pulling data by API from several other platforms to collect all data in one database, to then start creating mathematical models to calculate data that you need for a more accurate forecast. Automation means for example that your stock is continuously updated on all your platforms at the same time, that your delivery time to the consumer is updated based on the busyness of your warehouse etc. But whatever we do; we should not only rely on results of any automation. Even AI might not know about future marketing events, so make sure that you check any recommendations manually and that the numbers add up. In the end, you can only blame yourself, not a tool.

In conclusion, to implement AI effectively in supply chain management, companies should invest in data infrastructure, develop AI models tailored to their specific needs, and ensure integration with existing systems and processes. Additionally, ongoing monitoring and optimization of AI algorithms are crucial to maintaining their efficiency and effectiveness as supply chain dynamics evolve.

At my current role I am using simulations for my clients to show them the "What if" scenario. You don't really need a fancy tool, again - Google Sheets can do the job. If you feed it with the right information. But having the chance to look into different kind of scenarios gives you the option to consider all aspects in forecasting, not only of one forecasting model. If sales go down by 30%, what does it mean to your inventory? What costs are involved? There's more than meets the eye. Simulate alternative scenarios to see what the implications will be, inform stakeholder and if required, take a more secure approach and re-order more frequently for example, if sales are going as planned (or beyond).

Simulation in inventory management? Sorry, you would have to convince me. Simulation in automation, well that's another story. If you business is looking to spend the mega dollars on an automated warehouse without first having the plans run through simulation modelling, well.... you get what you pay for. You take a new or used car through an inspection process and you're spending 100 times (minimum) on that amount to automate your warehouse. Remember, sometimes a Kia can be just as good as a Rolls Royce (it's about what you can afford and the intended use....)

There are several tools that can be used to make data-driven inventory decisions. Inventory Management Systems: These software tools track inventory levels, sales data, and customer ordering patterns to help analyze and predict demand. They provide real-time insights into stock levels, order history, and product performance, allowing businesses to make informed decisions about inventory replenishment, reordering quantities, and managing stockouts. (ERP) Systems: ERP systems integrate various functions such as inventory management, sales, finance, and supply chain. They generate reports and analytics based on real-time data, enabling businesses to identify trends, optimize stock levels, and automate replenishment decisions.

To make data-driven inventory decisions, you can use a variety of tools. Here are some popular ones: 1. Point of Sale (POS) Systems: POS systems can provide real-time sales data, allowing you to track which products are selling well, monitor stock levels, and make informed decisions on restocking. 2. RFID Technology: Radio Frequency Identification (RFID) tags can be used to track inventory in real-time, providing accurate and up-to-date information on stock levels and enabling automated inventory tracking. Remember, the choice of tools depends on the specific needs and size of your business. It's always a good idea to evaluate different options and choose the ones that best suit your requirements.

Replenishment settings, and projected volume forecasts are the signals used by the majority of ERPs & Distribution planning platforms. In many cases, functional operators can set and forget the product master data, including lot size, lead time, preferred vendor. However, the assumptions can change with reality over time, and whilst dynacism can create agility, it can also create longer term inventory volatility & introduce risk. I would encourage planning excellence professionals to create a schedule review point for new and established products. Review forecast exceptions monthly. Planning settings including portfolios minimum twice yearly, lead time settings and safety stocks quarterly. And conduct a detailed review on sample results.

Demand Forecasting Tools: Demand forecasting tools use historical sales data, market trends, and statistical models to predict future demand. These tools help in estimating future inventory needs, preventing overstocking or stockouts, and optimizing inventory levels. Data Visualization Tools: These tools turn raw data into visual formats such as charts, graphs, and dashboards, making it easier to analyze and understand inventory-related information. They help in identifying trends, patterns, and anomalies in inventory data, and facilitate data-driven decision-making

Excel has been one of the tools that has help me in a long way in inventory management, i designed it to suit the needs of the organisation.