Total replenishement lead time (MRP3 --> availability check)

本文详细介绍了物料需求计划(MRP)中总补货提前期的概念及其在物料可用性检查中的应用。对于自制物料,补货提前期考虑了所有物料清单(BOM)级别的采购或生产时间;对于外购物料,则结合采购订单处理时间和预计交货时间进行计算。

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MRP3-- > Availability check ---> Total replenishment lead time (in workdays)   

    For in-house produced material and it is taken into consideration in the availability check .  In an availability check where the system takes the replenishment lead time into consideration, it only checks whether there are sufficient incoming quantities and stocks available to cover the requirements within the replenishment lead time. Outside the replenishment lead time,the system assumes that the material is available in sufficient quantity.



if  for the material which is procured by external procurement  , procurement type: F

requested PO delivery date  + GR Processing time(MPR2-->scheduling) == > PO requirement/receipt date  (MD04)

PO document date +  planned delivery time (MPR2-->scheduling) == > realistic delivery date    

 

Total replenishment lead time (in workdays)

The total replenishment lead time is the time needed before the product is completely available again, that is, after all BOM levels have been procured or produced. It is not calculated by the system, but defined in this field as the total of the in-house production time(s) and/or the planned delivery time(s) of the longest production path.

This time is necessary if, for materials produced in-house, the replenishment lead time is to be taken into consideration in the availability check.

In an availability check where the system takes the replenishment lead time into consideration, it only checks whether there are sufficient incoming quantities and stocks available to cover the requirements within the replenishment lead time. Outside the replenishment lead time,the system assumes that the material is available in sufficient quantity.

Therefore, in this situation, it is usually necessary to depict the product's total replenishment lead time with all BOM levels as the replenishment lead time.

Use

The following options exist for materials produced in-house if the replenishment lead time is included in the availability check:

  • The Total replenishment lead time field is maintained.
  • In this case, the time maintained in this field is used for the availability check.
  • The Total replenishment lead time field is not maintained.
  • In this case, the system reads the in-house production time and, if available, the goods receipt processing time, and interprets the total of these two times as the replenishment lead time for the availability check.

Note

There is no point in maintaining the Total replenishment lead time field for materials procured externally. In external procurement, the system adds the processing time from purchasing, the planned delivery time, and the goods receipt processing time and uses this total as the replenishment lead time for an availability check with replenishment lead time.

 

转载于:https://www.cnblogs.com/jefflu2012/p/4223556.html

-------------------------------------------------------------------------- ImportError Traceback (most recent call last) Cell In[13], line 1 ----> 1 import mmdeploy.utils as utils 2 print(dir(utils)) File ~/work/舌诊训练/mmdeploy/mmdeploy/__init__.py:8 5 from .version import __version__, version_info # noqa F401 7 if importlib.util.find_spec('torch'): ----> 8 importlib.import_module('mmdeploy.pytorch') 9 else: 10 logger = get_root_logger() File /environment/miniconda3/lib/python3.10/importlib/__init__.py:126, in import_module(name, package) 124 break 125 level += 1 --> 126 return _bootstrap._gcd_import(name[level:], package, level) File ~/work/舌诊训练/mmdeploy/mmdeploy/pytorch/__init__.py:2 1 # Copyright (c) OpenMMLab. All rights reserved. ----> 2 from . import functions # noqa: F401,F403 3 from . import symbolics # noqa: F401,F403 File ~/work/舌诊训练/mmdeploy/mmdeploy/pytorch/functions/__init__.py:2 1 # Copyright (c) OpenMMLab. All rights reserved. ----> 2 from . import adaptive_pool # noqa: F401,F403 3 from . import any # noqa: F401,F403 4 from . import atan2 # noqa: F401,F403 File ~/work/舌诊训练/mmdeploy/mmdeploy/pytorch/functions/adaptive_pool.py:6 3 import torch.nn.functional as F 4 from torch.nn.modules.utils import _pair ----> 6 from mmdeploy.core import FUNCTION_REWRITER 7 from mmdeploy.utils import Backend, get_root_logger, is_dynamic_shape 10 @FUNCTION_REWRITER.register_rewriter( 11 func_name='torch.nn.functional.adaptive_avg_pool2d') 12 def adaptive_avg_pool2d__default(input, output_size): File ~/work/舌诊训练/mmdeploy/mmdeploy/core/__init__.py:2 1 # Copyright (c) OpenMMLab. All rights reserved. ----> 2 from .optimizers import * # noqa: F401,F403 3 from .rewriters import * # noqa: F401,F403 File ~/work/舌诊训练/mmdeploy/mmdeploy/core/optimizers/__init__.py:3 1 # Copyright (c) OpenMMLa
04-01
### 解决 Python 中导入 `mmdeploy.utils` 模块导致的 ImportError 错误 当尝试导入 `mmdeploy.utils` 模块时发生 `ImportError`,通常可能由以下几个原因引起: #### 1. **模块未安装** 如果 `mmdeploy` 尚未正确安装到环境中,则会引发此错误。可以通过以下命令验证其是否存在以及版本是否匹配: ```bash pip show mmdeploy ``` 如果没有找到该包或者版本不兼容,请重新安装最新版或指定版本的 `mmdeploy`[^1]: ```bash pip install mmdeploy==<version> ``` #### 2. **路径问题** 即使已安装 `mmdeploy`,但如果当前工作目录不在 Python 的搜索路径 (`sys.path`) 中,也可能触发此类错误。可以检查并调整环境变量 PATH 或者通过修改脚本中的 `PYTHONPATH` 来解决问题。 在运行程序前设置 PYTHONPATH 变量指向项目根目录: ```bash export PYTHONPATH=$PYTHONPATH:/path/to/mmdeploy_root_directory/ ``` 另外,在代码内部也可以动态添加路径: ```python import sys sys.path.append(&#39;/path/to/mmdeploy_root_directory/&#39;) from mmdeploy.utils import some_function_or_class ``` #### 3. **依赖冲突** 有时其他库的存在会影响正常加载目标模块。比如旧版本 Django 不再支持某些方法名 (如 force_text);Keras 子组件 Sequence 导入失败可能是由于框架升级所致[^2]。对于 `mmdeploy` 而言,可能存在类似的依赖关系破坏情况。建议查看官方文档确认所需最低依赖列表,并逐一更新至推荐版本。 例如,确保 OpenCV 版本满足需求: ```bash pip install opencv-python-headless>=4.5.0 ``` #### 4. **命名空间污染** 假如本地存在同名文件夹或 `.pyc` 编译残留物干扰全局查找过程,则需清理这些潜在障碍物后再试一次操作。删除不必要的自定义 utils 文件能够避免混淆][^[^34]。 最后附上一段简单的测试代码用于验证修复效果: ```python try: from mmdeploy.utils import get_codebase_config, is_available except ImportError as e: print(f"Failed to load module due to {e}") else: config = get_codebase_config() availability_status = is_available(&#39;torch&#39;) print(config, availability_status) ```
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