A deliverable refers to tailored strategic outcomes in the implementation plan that consultants present to their clients to improve their value offering. Once the first version of the deliverable is completed, change control should be implemented. Using the configuration management tools and procedures to support the control of multiple versions of deliverables.
Dashboard Listing Decision Priorities
As we have seen, only supply chain decisions can be truly evaluated from a quantitative perspective. Therefore, one of the key business deliverables of quantitative supply chain initiatives is the dashboard, which integrates the decisions obtained as the final numerical result of the entire data pipeline. Such a dashboard can be as simple as a table, which lists the specific quantities to be reordered for each product soon. If there is a minimum order quantity or any other order restriction, the recommended quantity may be zero most of the time, and this situation will continue until the appropriate threshold is met.
For simplicity, we assume that these numerical results are collected in the dashboard. A dashboard is a specific form of the user interface. However, the dashboard itself is just an option, and it may or may not be relevant. The software that drives quantitative supply chain initiatives should be highly flexible, that is, procedurally flexible, and provide multiple ways to package these results into various data formats.
White Boxing Of Numerical Results
In the supply chain and other areas, systems are marked black boxes, and such systems generate output that cannot be explained by the practitioners who interact with these systems. Quantitative supply chain focuses on automated data pipelines, and also faces the risk that the delivered system will be classified as a “black box” by the supply chain team. The financial impact of supply chain decisions is very important to the company. Although the new system can improve this situation, it can also cause disaster. Although it is highly desirable to achieve automation, this does not mean that the supply chain team does not need to have a thorough understanding of the content provided by the data pipeline that supports the quantitative supply chain.
Although small-scale decision-making also needs attention because it is one of the few methods of quantitative performance evaluation, the supply chain may also need to be adjusted in larger and more disruptive ways to make performance more consistent platforms. This is why it’s important to engage with a business growth specialist to guide big operations for your company. For example, buying more carefully selected inventory units will slightly improve service levels. But at a certain point, such as when the warehouse is full, no additional units can be purchased. In this case, a larger warehouse should be considered.
To assess the impact of increasing this limit, we can remove the warehouse capacity limit from the calculations and evaluate the overall financial advantage of operating with any large warehouse. Then, supply chain managers can focus on financial indicators related to the friction cost of the warehouse capacity itself, and then decide when to consider increasing the storage capacity.
Normally, the operation of the supply chain is based on many restrictions that cannot be revised every day. These restrictions may include working capital, storage capacity, transportation delays, production throughput, etc.
These types of dashboards are called strategic dashboards. This method is different from the traditional supply chain approach, which emphasizes temporary measures when the supply chain is about to reach its operating limit.
Probabilistic demand forecasting is indispensable for inventory optimization. When the forecast value is consistent with the total demand in the delivery cycle, it can be said that the demand forecast is complete in the delivery cycle, this is completely different from the traditional forecasting perspective, in which the regular forecast (usually daily, weekly or Forecast once a month), and the lead time is unknowable. The forecasting engines provide complete probabilistic demand forecasts and take probabilistic lead times as input. There is a forecasting engine that provides native support for various statistical patterns in business data, such as seasonality, trends, and product life cycles. Abnormal demand situations such as stock-outs or promotions will also be considered. These forecasting systems express the expected probability of each unit in future demand. Important to note that a sales pipeline management team will help your company forecast demand and sales.
New Product Forecast
From the perspective of forecasting, new products refer to products that have not yet been sold. This involves a very special forecasting problem because, by definition, new products do not have any relevant historical data. The forecasting engine supports the start of new product demand forecasting parameters. In the case that the historical start date is known, it is recommended to also provide this information to the forecasting engine, because whether it is for new or old products, it helps to improve the accuracy of the forecast.
The expected input of the parameter is the date of each item. This data is used to indicate the first day when the requirements of the project become effective. For items that have been sold, this date is in the past, for items that are not yet on the market, this date is in the future.