How can you identify potential supply chain issues with sales forecasting techniques?

As the Supply Chain Physical Activities start with the Micro Supplier echelon and end with the end-user the Information cycle is in reverse so the potential benefits of sales anticipation would play a significant role in Rates of Material Ordering, Production, FG Packaging and Inventory Flow in the Supply Chain, Known as Bullwhip effect issue.

Supply chain issues encompass disruptions or inefficiencies affecting the flow of goods and services, including supplier delays, transportation problems, quality issues, demand fluctuations, and excess inventory. These challenges can result in negative impacts such as lost sales, increased costs, reduced cash flow, and damage to reputation. Furthermore, supply chain issues may contribute to decreased customer satisfaction, heightened waste, and diminished profitability and brand image for retail operations.

I agree, anything that can negatively impact delivery of goods through it’s journey, be it a global pandemic, industrial action at wharfs, canal blockage or internet outage. Depending on the business and time sensitive nature, supply chain issue can put a company out of business.

I agree, anything that can negatively impact delivery of goods through it’s journey, be it a global pandemic, industrial action at wharfs, canal blockage or internet outage. In my experience it has been best to account for some level of disturbance within the supply chain as the issues are usually out of your control, but allowing for them is not.

Use sales forecasting techniques to anticipate and identify potential supply chain issues. Implement statistical models, like time-series analysis, to predict demand patterns accurately. Collaborate closely with sales teams to incorporate market insights and customer feedback into forecasting algorithms. Utilize advanced forecasting tools and machine learning algorithms to enhance accuracy. Implement scenario planning to prepare for various demand scenarios and potential disruptions. Regularly monitor forecast accuracy metrics to identify discrepancies and refine models. Establish strong communication channels between sales and supply chain teams for real-time information exchange.

les prévisions de vente aident les entreprises à anticiper la demande future, facilitant ainsi la gestion des stocks, l'optimisation des cycles de commande, la prévention des pénuries ou des excédents, la réduction de la variabilité de la demande, l'amélioration de la communication avec les partenaires de la cha?ne d'approvisionnement, et renfor?ant les capacités de prise de décision et de gestion des risques.

Sales forecasting involves predicting future demand for products or services by analyzing past data, market trends, and various factors. It aids in preventing supply chain issues by aligning inventory with expected sales, optimizing ordering cycles, and mitigating demand variability. Furthermore, it fosters improved communication with suppliers and distributors, enhances decision-making, and bolsters risk management capabilities, contributing to a more streamlined and efficient supply chain process

Sales forecast can help with supply chain issues but to be more effective it has to be demand forecast not sales forecast as there’s a big difference between sales and demand that can cause you supply chain issues as well. Sales is just concerning actual sales history but demand is the actual sales history + lost sales due to availability for example. Considering actual sales only means you will always have the same lost sales and you are not improving. Secondly is the forecast accuracy the more it’s accurate the more the supply chain is efficient and less disruptions exist. Monitoring and measuring accuracy is important to be improved and it has to be measured using the most appropriate technique.

Short term forecasting helps the planners to place orders on time which helps in improved service levels, reduced costs and optimized inventory. Long term forecasting is needed to understand where the business is going and thereby additional investments are needed to increase capacity, infrastructure and resources.

Sales forecasting for me is one of the first steps in identifying if the supply chain can meet the demand. It is critical to ensure orders are placed / production starts at the correct time to have the finished goods where they need to be at the right time. It is important to note that a sales forecast can also be wrong, and by reviewing it regularly you can quickly make decision to adjust future orders/production.

Sales forecasting techniques can be categorized under 2 categories quantitative and qualitative. Qualitative is based on personal insights of some experts in the market methods used here are like Delphi method, where group of experts provide their insights. Quantitative has 2 sub-categories intrinsic which is time series based like moving average using actual demand data. Extrinsic which is based on leading indicators for example like simple and multiple regression Most used and efficient sales forecasting technique is a hybrid consensus, like a quantitative forecast based on some AI machine learning tool generating base line forecast that is influenced by leading indicators and qualitative forecast from sales and marketing team

Métodos como modelagem estatística, análise de séries temporais e algoritmos de aprendizado de máquina oferecem abordagens mais precisas em compara??o com métodos tradicionais. Por exemplo, a utiliza??o de redes neurais pode melhorar significativamente a capacidade de prever padr?es complexos de comportamento do consumidor. Além disso, a integra??o de dados de diversas fontes, como redes sociais e feedback dos clientes, pode enriquecer as previs?es, proporcionando uma vis?o mais abrangente do mercado. Em resumo, a ado??o de técnicas avan?adas de previs?o impulsiona a tomada de decis?es estratégicas, melhorando a eficiência operacional e a adaptabilidade às demandas do mercado.

- Time Series Analysis: Utilize historical sales data to identify patterns and trends over time. - Regression Analysis: Analyze the relationship between sales and various factors like advertising expenditure, market conditions, etc. - Machine Learning Models: Implement algorithms such as linear regression, decision trees, or neural networks for predictive modeling. - Market Research: Gather information on market trends, customer preferences, and competitor activities to inform sales forecasts. - Qualitative Methods: Incorporate expert opinions, focus groups, or Delphi method for subjective insights. - Rolling Forecasts: Update forecasts regularly based on new data and adjust predictions as conditions change.

These techniques collectively empower supply chain managers to navigate dynamic market conditions, minimize risks, and optimize inventory management, marking a paradigm shift in effective supply chain forecasting. - Historical Data Analysis - Data Analytics and Machine Learning (basically AI software). - CPFR #SupplyChainManagement #SalesForecasting #DataAnalytics

Algunas técnicas de pronóstico de ventas incluyen el análisis de tendencias históricas, métodos cualitativos como encuestas y juicio de expertos, modelos de series temporales que utilizan datos pasados para predecir el futuro, y técnicas de regresión que identifican relaciones entre variables. También se emplean análisis de mercado, pronósticos basados en datos de clientes y métodos combinados que integran múltiples enfoques para mejorar la precisión. La elección de la técnica depende de factores como la disponibilidad de datos, la estabilidad del mercado y la complejidad del producto.

In order to choose the best sales forecasting technique you have to first understand the demand pattern besides quality of data, product elasticity and the market volatility. Demand patterns are trend, random variation, seasonal, cyclical and others. For example seasonal demand has to be forecasted by certain technique which is calculating seasonal index for each period and multiply it by de-seasonal demand. Sometimes you have to decompose demand patterns as it could be a trend combined by seasonal demand

Simply put it will be the one that best works for your operations and circumstances, historical sales data can be a good indicator for sales forecasting but may not take into account factors such as, improvements in your marketing content, endorsement, less competition/more competition, etc. The strongest sales forecasts should have a collaborative approach with stakeholders and deviations be measurable and accounted for. It should be dynamic, with a small buffer of tolerance.

1. **Understand Your Data:** Assess the nature and characteristics of your sales data. Time series analysis may be suitable for historical patterns, while regression analysis might be effective if there are identifiable influencing factors. 2. **Consider Complexity:** Choose a technique that aligns with the complexity of your sales environment. For simpler situations, basic forecasting models might suffice, while complex scenarios may benefit from advanced machine learning models. 3. **Data Quality and Availability:** Ensure that you have sufficient high-quality data. Some techniques may require large datasets or specific types of information. If your data is limited or of poor quality, simpler methods might be

La elección de la mejor técnica de previsión de ventas depende de la disponibilidad de datos, la estabilidad del mercado, la complejidad del producto, los recursos disponibles, la precisión requerida y la flexibilidad. Evalúa estos factores para determinar si se requieren métodos basados en datos históricos, cualitativos, modelos de series temporales o técnicas combinadas. La adaptabilidad y la precisión son clave para seleccionar la técnica más adecuada.

At Walmart Canada we prioritized the analysis of each products sell-thru rate & turnover rate, in the apparel department. Sell-through rates focus on how much was sold in a given period of time, while inventory turnover ratio (or turnover rate) measures how quickly you go through a product (or “turn” inventory). Knowing your turnover rates per product will help you order the optimum amount at the correct times of the year. Knowing your turnover rates per product will help you order the optimum amount at the correct time(s) of the year.

Improving sales forecasting performance depends on monitoring and improving forecast accuracy and analyze root causes of deviations. there are many techniques to measure forecast accuracy and they are used for different purposes, MAD is one of the most know and used techniques. MAD = average of absolute deviation, But with MAD you don't see the bias if it's forecast biased or actual. Bias = algebraic sum of errors along the period. if Bias = zero that means you have random variations smoothing out each other here you have to calculate Tracking signal error. Tracking Signal error = Algebraic Sum of errors / MAD Another technique MSE (Mean Squared Error) which is designed to chase higher performance through magnifying errors.

Monitor forecasting accuracy over a period of time and analyze the reasons for deviations in actuals versus forecast. Implement these during the next forecasting cycle. Use lead time while calculating the forecasting accuracy. For ex: if the lead time is 2.5-3 months, use the forecasting which was published 3 months ago for comparing this month's forecast with actuals.

To monitor and improve sales forecast performance: 1. Compare forecasted vs. actual sales regularly. 2. Measure forecast error using metrics like MAE or MSE. 3. Conduct root cause analysis to identify factors affecting accuracy. 4. Collaborate with sales and marketing teams for insights. 5. Incorporate external data sources for more accurate forecasts. 6. Continuously update and refine forecasting models. 7. Implement collaborative forecasting involving key stakeholders. 8. Monitor forecast performance metrics and set improvement targets. 9. Conduct regular forecast review meetings for feedback and improvements. 10. Invest in forecasting tools and technologies for automation and efficiency.

By monitoring the stock availability with the aws compared with the lead time. And if the inventory is above 3 month sale then its one of the major issue to keep those stocks

Aprendi que o passado pode se tornar uma bela ferramenta, coma fun??o de direcionar o futuro. S?o muitas as possibilidades de problemas na cadeia de fornecimento que possam atrapalhar os processos de produ??o, mas, com um estudo detalhado e monitoramento constante utilizando KPI's, é possível minimizar essas possibilidades. Os interesses de todas as organiza??es envolvidas na fabrica??o de um produto devem estar alinhados e coerentes com a necessidade do cliente final, mas claro, sem desconsiderar a QUALIDADE.

Um dos principais pontos na gest?o de suprimentos que além de controlar internamente produtos e possibilita como consequência, é a festa de estoque tanto controle como qualidade. O resultado da gest?o de estoque é uma maior qualidade de despacho, vale salientar que gest?o de ativos também é importante nesse processo.