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## Warehouse Activity Profiling

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**Warehouse Activity Profiling**Based on Bartholdi & Hackman Chpt 5**Warehouse Activity Profiling**• The careful measurement and statistical analysis of the warehouse activity. • The process of understanding the customer orders that drive the system • Sifting through historical data for opportunities and insights that might confer advantage. WAP Summary statistics SKU data Order data Distributions Location data • “Structural” • Characterizations, e.g., • prevailing patterns/trends • relations • dominant elements**SKU-related data(distributed over a set of data-bases)**• SKU ID • text description • product family (product families are defined for each industry and suggest certain types of storage and handling) • Addresses of storage location in the warehouse (zone, aisle, section, shelf, position on the shelf) • For each location storing the SKU: • storage unit • physical dimensions of the storage unit (length, width, height, weight) • scale of the selling unit • number of selling units per storage unit • Date the SKU was introduced (for assessing growth of the corresponding activity) • Max inventory level by month or week (for assessing space needs)**Order-related data(coming from sales-transactions databases)**• Order ID • SKU ID • Customer ID • Any needs for special handling • Date/time order was picked • Quantity ordered • Quantity shipped Remark: This set of data can be really large (the corresponding datafile might exceed the 100M) => Needs processing through some specialized Database software.**Data Mining**• Handling a set of tables in a relational database management system • Table rows: Records with instances of the object/entity stored in that table (e.g., SKU’s, order lines, etc.) • Table columns: Attributes characterizing the considered entity • Typical functionality involved in data-mining • sorting the rows of a table by a certain attribute • selecting a subset of rows of a table, s.t. all isolated entities satisfy a certain property • counting distinct entries in a table meeting a certain condition • performing joins, i.e., combining the information one table with that of another table to create a new table with a different set of attributes • graphing the results • SQL: Structured Query Language**Some basic summary statistics**• Order-related • average number of SKU’s involved (work and storage complexity) • average number of orders shipped per day (volume of activity) • average number of lines (SKU’s) per order (picking complexity) • average number of units per line • seasonalities (Seasonal Indices: What percentage of a cycle corresponds to a period in the cycle - temporal distribution of the work) • Facility-related data • area of the warehouse • average number of shipments received per day(the “backend” activity) • average rate of introduction of new SKU’s (operational stability) • average number of SKU’s in the warehouse (volume and scope of operations) • distribution of the personnel to the various activities (labor-related costs and opportunities)**A closer characterization of the warehouse workload**• What drives the entire warehouse activity is the order/pick lines! • Need to understand how these lines are distributed among • SKU’s • product families • storage locations • warehouse zones • time • Activity analysis • Results are communicated as • discrete distributions • Pareto curves, i.e., cumulative distributions where the items on the horizontal axis are arranged in a decreasing order w.r.t. the corresponding value of the distribution. • other plots (e.g., bird’s eye view for characterizing location activity)**Graphing the results of the Activity Analysis**Discrete Distribution % picks 1.0 zone A B C D Pareto curve % picks 1.0 SKU’s 10K 20K**Pareto Effect and ABC Analysis**• Pareto Effect: A small percentage of the considered entities account for the largest fraction of the activity (20/80 rule) • ABC analysis: Exploit the Pareto effects in order to classify the considered entities into (typically three: A, B and C) categories, such that • the entities in the first category are the ones responsible for most of the activity, and therefore, more closely managed; • the entities in the second category account for most of the remaining part, and therefore, are moderately important; • the entities in the third category are the largest bulk responsible for only a small part of the activity, and therefore, insignificant. • Remark: ABC classification of the same set of entities will differ from activity to activity (c.f. Bartholdi & Hackman, Tables 5.1 - 5.5)**Work Patterns and their Implications**• Distribution of lines per order: What percentage of orders have a single line, two lines, etc. (Reveals possibilities for batching and/or zoning) • Distribution of picks by order-size: What fraction of picks comes from single-line orders, two-line orders, etc. (reveals whether most work is generated by small or large orders, shipping activity) • Distribution of families/zones per order: What fraction of orders involves a single family/zone, two families/zones, etc. (identifies coupling which can be exploited by the picking process) • Family pairs analysis / “order-crossings” (for zones): identify pairs of families/zones with correlated demand (this correlation should be exploited by putting items in each pair close to each other)**“Case Study”: Profiling the Activity of a Wholesales**Distributor of Office Products Problem description: http://www.isye.gatech.edu/people/faculty/John_Bartholdi/wh/book/profile/projects/projects.html Problem Solution: http://www.isye.gatech.edu/~spyros/courses/IE6202/WAP-cs.pdf