Supply Chain Planning & Execution (SCP)

Software that turns the sales forecast, the purchasing schedule and the production plan into one live plan, run in the cloud so that several plants, warehouses and, where you choose, suppliers see the same picture. It balances customer demand against real capacity, current stock and confirmed supplier deliveries, proposes what to buy and when, and checks a delivery date before sales commits to it. MSF SCP runs as shared SaaS, as a dedicated single-tenant environment or on-premise.

APS optimizes the production schedule inside the factory; SCP extends that plan across suppliers, purchasing and outbound logistics.

Yes. Supplier portals and EDI/API integrations keep supplier confirmations and lead times synchronized automatically.

Absolutely. SCP is designed to balance demand and supply across several plants and warehouses.

Yes. What SCP needs regardless of its neighbours is a maintained item master with lead times and order rules, BOMs, a stock position you can trust, and open purchase and production orders, and those have to come from an ERP or an equivalent system of record. Without MES, progress and consumption arrive as confirmations posted at the end of a shift or an order, so the plan reacts late rather than not at all.

A purchase proposal is arithmetic on lead time, minimum order quantity, order multiple, stock on hand and the BOM. If supplier lead times are still the ones typed in when the part was created, the proposals will be confidently wrong. These errors do not stay hidden: the first planning runs turn a wrong lead time or stock figure into a visibly wrong date or quantity. Cleaning that up before go-live is far cheaper than after.

It calculates and proposes them; it does not decide what risk you are willing to carry. A safety stock figure comes out of demand variability, supplier lead-time variability and the service level you choose, and the third is a commercial decision someone has to make per item group. Set it against measured variability rather than habit, then review the exceptions, because that is where stock actually comes out.

Not by itself. What it gives you is one forecast that is visible, versioned and compared against what actually sold, which is usually what people mean when they ask. Statistical models help where demand has history and a pattern; they do nothing for a new product or an order won on a tender. The improvement comes from someone owning the forecast and reviewing the error every cycle.

It can, but decide against what. ATP checks free stock and confirmed inbound; CTP also checks whether capacity and components exist to make the order in time. With APS in place, CTP resolves against the sequenced schedule; without it, against declared capacity buckets, which is directionally useful but will not catch a sequencing or changeover conflict. Promises fail on stale supplier confirmations and optimistic capacity, so agree with sales what the system may promise unaided.

You need one named owner, whose job becomes exception handling rather than rebuilding spreadsheets. The work is concrete: reviewing alerts, releasing or rejecting proposals on your planning cycle, and keeping lead times and order parameters honest. Effort scales with the number of planned items and the exceptions your parameters generate, and it is heaviest in the first cycles after go-live while those parameters are corrected. If nobody owns it, the alert list grows until people stop reading it.

Not unless you want it to. Most plants freeze a near horizon, route changes inside it to a planner as exceptions, and let the system reoptimize beyond it. Where that line sits is a matter of policy, so agree the frozen period and who is allowed to break it during implementation. Planners go back to their spreadsheets because of plan nervousness, and computing time is not the reason.