What do you do if your inventory demand planning lacks logical reasoning?

When there is insufficient information to support arguments and conclusions, logical reasoning is typically lacking. The first step in resolving this issue is gathering and examining inventory management data. Sales, stock, and fast- and slow-moving products statistics will all differ in this regard. All demand planning decisions can be informed by an analysis carried out using precise and tangible facts.

Business activities cannot always be realized according to recognizable usual mechanisms and schemes. Reviewing, looking at shortcomings, restructuring and changes in plans and approaches to them are necessary. Sometimes alternative solutions give far better results than methodological approaches. Stocks are questions of the market, recognition of the need for goods, the right moment of placement, adequate prices, logistical availability. If we look at business as one big adult game, throughout history the greatest results were often made by those whose moves had nothing to do with the mainstream logic of others involved in business.

Rely on data but always keep questioning it . Question it to lead you to the KPIs you need . The more you analyze the inputs from that data the better inventory management outputs you will get . This way you will keep assessing data to include it to your inventory management strategies and transform it into the outputs you need .

Para come?ar o seu planejamento de estoque, fa?a um inventário de todos os itens que est?o no seu estoque. Você já sabe quais s?o os custos que permeiam o gerenciamento do estoque, ent?o procure documentá-los também. Além disso, anote informa??es essenciais como quantidade de estoque de cada produto, os custos da mercadoria e a data de vencimento. Essa lista deve ser constantemente atualizada, já para saber exatamente quais produtos você tem ou n?o em estoque, é necessário ter um controle de todas as entradas e saídas. Uma forma prática e gratuita de fazer isso é utilizando uma planilha do Excel. Com as configura??es certas, é possível fazer um controle bastante profissional e garantir o planejamento de estoque da sua empresa.

In my experience, data accuracy is a company wide effort. Although demand planners controls and analyze sales forecast from sales data there will always, always be data discrepancy from reality and that’s where a company can improve by driving combined efforts of cross functional teams to clean up data during an era of ERP transformation.

An important piece to the outcome is the need for complexity. Understanding the dataset and volatility of demand will help direct you to an acceptable solution. Factors that will need to be considered are: recent fluctuations in individual product demand, determining YOY trends, and considering growth or regression rate of demand. Then applying the appropriate analysis for immediate action vs spending time making a "perfect" analysis. Keep in mind the frequency of review and trending improvement.

I agree, but there is no single one size fits all method of forecasting that will work best for every company and every type of item. Exponential smoothing and regression analysis these advanced forecasting calculations are great at maintaining a steady level of inventory to minimize supply chain disruptions but it also smooths out when there is an actual significant drop in sales after q4 holidays at end of the year. From an accounting perspective, this is not good on the books toward the bottom line to be carrying excess inventory at year end when in reality sales drop more significantly than forecast due to low store foot traffic and consumer habits of spending year end with family over buying in stores at retailers.

I've found it highly beneficial to initiate a Pre-S&OP process. This involves sitting down with the sales team to discuss any noticeable outliers and biases directly affecting the forecasts. We analyze the weights and projected impacts of different forecasting models to see where they might be leading us astray. It’s also crucial to revisit any historical data revisions to understand their justification.

Collaboration is crucial in any part of a supply chain but even more so in demand planning. You do not have all the data and will never have all the data at your fingers to provide a good forecast on your own based solely off of historical data. There can be information such as knowledge from your suppliers, buyers, category managers, promotions, marketing, all the way down to your individual store’s sales person of which you can gather better insights to increase forecast accuracy.

No matter how accurate your data models are it is important to always question them and improve them . If you do not improve your current mechanisms you will depend more on subjective wishful thinking rather than on objective thinking.

Start by scrutinizing historical sales data for patterns and anomalies. Implement statistical forecasting models to predict future demand more accurately. Engage with stakeholders to understand market trends, customer preferences, and external factors influencing demand. Incorporate qualitative insights alongside quantitative analysis for a comprehensive view. Continuously refine the planning process based on feedback and real-time data updates. Collaboration between departments like sales, marketing, and operations is vital for aligning planning efforts with strategic objectives. Flexibility and adaptability are key in refining and improving demand planning processes.

The business landscape is more dynamic than ever? Unforeseen events like the COVID-19 pandemic, trade disruptions, flash sales, and shifting seasonal patterns can wreak havoc on inventory levels, leading to stockouts, excess inventory, and lost sales. The solution lies in demand planning that goes beyond traditional methods and incorporates logical reasoning.