Common sense rules. We may not like them, but generally, they stand the test of time and should be followed. Here are 8 common sense rules related to inventory management published by Inbound Logistics back in 2007. They still hold true today. 

1. If you don' t know where you are going, no road will take you there. Enterprise resource management systems are designed to tell you about today' s inventory. With some work, you can also access information about past inventory. To manage inventory proactively, however, you must know projected inventory levels for the future.

2. Make what you can sell. An integrated Sales and Operations Plan will naturally take into account expected demand in its production plan. Inventory is not an independent variable - it is the direct result of demand and supply.

3. Sell what you can make. Too often, a disconnect exists between sales and marketing desires and the reality of production capabilities.

4. If you can' t sell it, stop making it. If demand for your product does not materialize, you need to identify that gap quickly to avoid a buildup of non-moving inventory. Numerous mechanisms can be put in place to identify such trends.

For tips 5 through 8 and more details into the other tips, click the button below to read the full article.

When fixing a downtime issue, make one change at a time.

Often, multiple changes are made at the same time, without individually checking their impact on the equipment. This makes it much harder to diagnose problems and evaluate the effectiveness of solutions.

All actions required for converting, validating, matching, and posting electronically received bank statements can be performed within a single session:

• Bank Statement Workbench (tfcmg5610m100)
• Bank Statement (tfcmg5610m000)

Alternatively, you can use the sequence of electronic bank statement sessions outlined below.

Steps to Process Electronic Bank Statements:

OPERATIONS: Update, Cancel or Remove Outbound Order Lines

FINANCE: Currency Differences Accounts

TECHNOLOGY: Advantages of Data Replication