Improving Inventory Demand Forecasting by Using the Sales Pipeline: A Case Study
Bibliographic record
Abstract
EXECUTIVE SUMMARY | The inventory demand forecasting is a critical function in a manufacturing organization as it impacts the major financial metrics like inventory valuation, gross margin and net margin. The currently used demand planning processes often fail to incorporate input from the Sales pipeline. Failure to do so provides challenges of how to increase the lead times for the supply chain process. This can be accomplished by introducing input criteria from the Sales pipeline. This case study is an attempt to provide such mechanism.Advanced Manufacturing Inc. (AMI) is a midsized business located in Southern Ontario. It has around 35 employees and has been in business for nearly six years. Its products are engineering items that are sold to clients in Canada, the United States, and Europe. To increase the value proposition of its products, the company often acts as a reseller of third party products. It procures specialized equipment from external vendors, integrates them with AMI products, and offers the integrated unit as a final product. The organization is well structured with specific departments responsible for fulfilling their re-spective roles in the value chain. The Sales team is responsible for securing the business, and when a Purchase Order is received from the client, the sale is deemed final. After securing an order, a job is created for it. The job is passed on to the Project Management team, which then allocates resources and coordinates with the Supply Chain, Production, Engineering, Quality Assurance, Finance, and Shipping teams. Once this project management cycle is complete, the product is shipped to clients. Inquiries that arise from clients after receiving the product are handled by the Client Response team. The organization has established a reputation for high-level product quality. Most of the client inquiries are software related. Most of the jobs are defect free, and that is what has enhanced the client loyalty. This has been proven by clients who place repeat orders with AMI, and act as advocates for the organization in their business community. As a result of the successful workflow and the efficiency of the Sales department, the organization is doing well, and has future plans of expansion.AN EMERGING PROBLEMAs discussed earlier, AMI secures contracts and executes them within the time period specified in the Purchase Order. The time period required to execute the order is determined by the Applications Engineering team. It is indicated on the quote, which is a part of the response to the Request for Proposal (RFP)/ Request for Quotation (RFQ). Depending on the internal resources available for the execution of the job, the time required for a job to be executed is quoted in the form, and that ranges from 6 to 8 weeks from the receipt of a Purchase Order.It is this project-execution time period that is creating challenges for the Supply Chain team. As discussed earlier, AMI acts as a reseller to increase the value proposition of its products. This means that it has to procure specific engineering items from external vendors. This requires negotiating prices, shipping to AMI at the correct phase of the project, and establishing terms of payment that are convenient to the company. In the case of AMI, most of the projects are unique. The Supply Chain team has to contact a larger number of external vendors and negotiate terms with them. Since they are ordering items on a projectby-project basis, they are unable to provide a guaranteed volume to the vendor and secure a price break. Also, the price of the same item can go up by as much as 10% to 15% between two successive orders that may be 60 days apart. The combined effect of price uncertainty and inability to secure a price break results in lower gross margins on certain projects. What is alarming is the fact that projects with higher revenue are showing a greater drop in gross margins due to the proportionately higher amount of resale items. …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".