Maximize Warehouse Management Systems with Process Mining Technology

Transcription

1 WMS Maximize Warehouse Management Systems with Process Mining Technology

2 Short Introduction & Background Courtesy of Soft Solutions

3 Warehouse Management System Ordering & Receiving Goods to the Warehouse Operational & Business Rules WMS Document Creation Inventory Control & Quality Assurance

4 WMS Terminology Wave Loop through a set of documents and create optimized tasks for picking, put away etc., taking under consideration resources, space, volume and other relevant parameters. Task A result of a wave that has a set of instructions that enables data entry with time tracking and online error reporting. Storage Zone Physical or virtual storage space in the warehouse like front storage, back storage or a gallery.

5 Warehouse Definitions (in Priority ERP)

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7 Advantages of WMS Automation Real-time reporting on data Verification via barcode scanning Significant reduction on need to work with paper Eliminates need for data entry Eliminates need for quality control of picking Eliminates needs to rely on memory and personal knowledge of warehouse personnel Optimization Task Consolidation Consolidating orders into one picking Performing a number of actions in one task Task Interleaving - mixing tasks to reduce travel time, e.g. sending a forklift driver to put away a pallet on his way to his next pick Inventory Reserve Inventory replenishment by level or orders Storage Optimization

8 So now how does WMS work?

9 Warehouse Tasks Details (in Priority ERP)

10 No. of open tasks for each user Hand Held Device UI PDA UI Forklift PDA or Laptop UI

11 PDA Pick sample PDA Pick Example Pick recommendation Next/Previous Record Copy Record Details Pack Details Data entry Zone Show alternate Bins

12 WMS BI Dashboard

13 Analyzing Warehouse Tasks Handling with Disco Example Deliverables

14 The Planned Warehouse Task Status Flow Notes: Tasks created by a wave, might be set to start with the status To Do. There are several task types (e.g., PUT, PIK), having the same planned flow.

15 The Required Data The warehouse tasks data are exported from WMS and saved as Excel or CSV. The file contains the mandatory data and additional attributes (e.g., Task type, Customer ID) Task identifier Task Status Timestamp of status change 4 Additional Attributes

16 Import Configuration Screen in Disco Configuring the data file columns to the appropriate built-in process mining definitions. 2. Importing the data into Disco.

17 Overview of the Imported Data in Disco

18 PIK = Pick PUT = Putting away RPL = Replenish MOV = Moving The Types of the Warehouse Tasks

19 Analyzing PIK (Picking) Tasks

20 Info about the Number of Lines in PIK Tasks

21 Filtering Tasks with One Line Only

22 Map Display of the Actual Process - Frequencies The map displays the various ways PIK tasks with one line were performed. Numbers, colors and lines thickness represent the frequencies of statuses and paths accordingly.

23 Statuses & Paths Frequency Number of tasks starting the process Number of status occurrences Number of times the path was repeated* Number of times a path was taken *Such loops may indicate inefficiency and waste in the process

24 Map Display of the Actual Process - Performance The map displays the mean durations of performing PIK tasks with one line. The thickness of the arrows and the coloring of the paths visually support these numbers.

25 Display of the Mean Duration in Tasks Performance The average time of the path between statuses

26 Locating the Slow Tasks Average duration of PIK tasks with 1 line is approx. 1Hour. Tasks which take longer then 5 hours are filtered.

27 Statistical Info of the Workers involved in the Slow Tasks

28 Comparing Storekeepers Performance

29 PIK Tasks Distribution Among Storekeepers The number of tasks allocated to a storekeeper

30 Comparing Storekeepers Performance The numbers indicate the mean duration of paths between statuses

31 Performance of a Certain Storekeeper Different Task Types

32 Performance of a Certain Storekeeper Different Task Types

33 Analysis by Task Types Example Deliverables

34 Task Types Comparison - Frequencies

35 Task Types Comparison - Performance

36 Further Analysis Suggestions By Wave ID (instead of Task ID) Waves issues By wave strategy types Adding attributes from customer orders (or other ERP documents) Locating influence/dependency among task types executions Worker s performance against established labor standards Traffic in zones

37 Thank You! Dafna Levy Phone: +972 (0) Web: Disco website: