Techatalyst has partnered with one of the leading supply-chain solution companies to provide an SCM solution that offers out-of-the-box integration with SAP.
Demand forecasting helps organizations estimate their future client demand using historical data and other information such as field reports. With a realistic demand forecast businesses are able to gather valuable information about their potential and different functions within an organization can make informed decisions about pricing, business growth strategies, and market share. Without demand forecasting, businesses risk making poor decisions about their products, target markets often leading to loss in market shares & company value.
When businesses mine historical data for a product or product line and you can leverage your operational data for demand forecasting. A time series analysis can help you identify seasonal fluctuations, cyclical patterns, and key sales trends. The time series analysis approach can be exploited by businesses who have can leverage several years of data to work from and relatively stable trend patterns.
The Demand Forecasting solution uses the powerful R statistical forecasting engine incorporating the best time series techniques available anywhere worldwide, including the following algorithms:
Most businesses in the make-to-stock (standardized products) category focus on creating an execution system that is able to respond quickly to the demand generated, while minimizing stocks through the supply chain.
The key question that confront the planners is that the system only needs to trigger replenishment at the stocking location when there is an unmet demand, as the objective is to minimize inventory at the same time maximize service level.
Across a supply chain the solution sets execution triggers. The replenishment triggers are created through automated procedures that take demand /consumption and create automated replenishment recommendations. These auto-triggers are based upon the keeping automatic track of stocking locations through the supply chain – and replenishments triggered when the stocking location falls below a preset level.
All manufacturing companies focus on Production planning, a critical step, helps them to achieve maximum efficiency and utilization of resources. An effective production plan directly impacts product costs, manufacturing lead times, machine throughputs and helps improve product quality.
Many manufacturers implement MRP as a part of their ERP projects as ways to save costs and improve operational efficiency. However, MRP output is an Unconstrained Output. MRP does not take into account manufacturing constraints. It assumes that the plant has an infinite capacity where multiple work orders can be scheduled on the same line at the same time, without taking into account the real manufacturing constraints.
What you require is a Planning system that bases its plan on real-life constraints. That is where our Production Planning system helps you. Optimization with constraints will only allow the load to be placed where there is capacity.
Production scheduling for low-volume, high-mix work typical of most job/batch shops is challenging. Our Shop-floor scheduling software allows you to meet all your delivery time frames for each sales order and for every client. As your business grows substantially, scheduling manually or with spreadsheets become impossible. It’s under such situations it becomes inevitable to use a production scheduling software to handle more / complex jobs under tighter constraints than ever.
Optimization of shop-floor requires data that is accurate.
That’s because the cycle-time “guesstimates” common in job shops don’t work with a scheduling engine, while clean, accurate routings are more accurate in a repeating-job environment, where cycle times are predictable. Finite scheduling looks at work-center/ machine capacity availability, manpower capacity (shifts), and then organizes everything based on manufacturing lead times and priorities to deliver a shop-floor order schedule for the shop to work on.
