DATA METHOD: ACTIVITY PROFILING

Activity measurement

Now the processes, activities, context and the layout are defined, the behaviour of the items is still left to analyse. The item activity is important to understand since the customer orders drive the entire system. According to Bartholdi III & Hackman (2016) to get an accurate insight in what is actually happening activity profiling is a conventional method. With activity profiling it is possible to display the economics of the warehouse by means of creating a statistical analysis of the warehouse activities that determines the workload within the overall facility.

Three types of views can be distinguished: The customer order profiles, the item activity profiles and the facility layout profile (Process Group, 2013). In the first view the order pattern of the customer is analysed with the objective to understand the distribution mix that influences the picking system. In the second view the dynamics of the SKUs are studied to gain insight in the behaviour and flow of the items, an insight that can support the slotting and allocation policy. In the last view the general facility characteristics are discussed.

Three main steps can be recognized regarding the executing of the activity profiling method. These three steps are:

1. Collect data: the information that is gathered will be used as input for the core activity profiling process.

2. Apply data-mining & interpretation: this process is the research for statistical relationships in datasets and understanding the presented patterns.

3. Visualize the results: the output, i.e., the results from the data-mining, has to be given a certain meaning and subsequently has to be made visually in a way that they are understandable for each of the stakeholders.

In the following sections we will expand the three main steps.

Input

To be able to execute an accurate analysis, data from several sources is required. Bartholdi III, et al. (2016) determine three main data types to enable profiling, which can be found in Table 2-3. A number of things are important to bear in mind when collecting data (Bartholdi III, et al. 2016):

1. General definition agreement: ensure all stakeholders agree on the meaning of the used terms for each data characteristics.

2. Correct interpretation: ensure the meaning of each data field is the same as what we expect it to be (e.g. are we looking at the print date or the date of collecting).

3. Distinguish financial and operational data: ensure the data represent the stream we want to analyse.

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Table 2-3. Overview of data sources and required data fields for activity profiling (Source: Bartholdi III, et al. 2016)

Item Master Location Master

SKU Data

o SKU ID

o Text description

o Product Family

o Item dimensions

o Storage location: e.g. zone, aisle, section, shelf, position

o Max inventory level per time unit

o Average inventory per time unit

Layout and location addresses

o Unit Load

o Physical dimensions

o Scale of selling unit

o Number of selling units per storage unit Order Master Oder History Data o Order ID o SKU ID o Customer ID

o Special handling needed

o Date/time ordering

o Quantity ordered

o Quantity shipped Data-mining & interpretation

Once the required data is collected, the next step is to execute data-mining to discover hidden patterns and knowledge. For this particular process software is needed. Various software packages are available and each of them with its own strengths, however according to Bartholdi III, et al. (2016) the following features should be present at least: row sortation, subsets selection, table entries counting, row joints within and between tables, result graphing and visualizations.

During this phase a number of aspects require some attention (Bartholdi III, et al. 2016):

1. Expect discrepancies in the data: Due to the several sources that provide data, some discrepancies will occur. Ensure an adequate solution for those cases is prepared.

2. Cross check the data: ask several people the same question to ensure the interpretation of the data and the results found are correct.

3. Beware of small numbers: slow-movers can create strange patterns in the results.

4. Beware of sampling bias: estimations and assumptions influences the actual results.

Table 2-4 gives an overview of possible statistical summary values that can be analysed during this phase.

Table 2-4. Example of possible statistical summary values (Source: Bartholdi III, et al,., 2016; The Progressgroup, 2013; Gray, Karmarkar, & Seidmann, 1992)

Dimension Statistic summary values Customer order

behaviour

o Average # of shipments per day

o Average # of lines per order

o Average number of units per line

o Seasonality occurrence trends

o Customer order frequency distribution per time unit

SKU activity o Total number of SKUs involved

o Fraction of total SKUs ordered

o Pick frequency: number of picks per e.g., item/family/type/aisle etc.

o Distribution of family per order: Fraction of orders involving 1, 2,.. n families

o Family pair analysis: Frequently family pair orders

o ABC distribution in orders

Facility o Warehouse zones & layout: available storage facilities

o Average # of SKUs in the warehouse

o Average # of shipments received per day

o Average introduction of new SKUs per time unit

o Average # of SKUs storage location per SKU Output

When the data is analysed the results have to be displayed in a way that it is understandable to all concerned, i.e., we have to tell stories about the data. With the aid of data visualization a large data set can be displayed from various point of views while presenting and comparing information at several levels of detail. There are two major benefits of presenting the data in a visual manner. Primarily visual representations are very intuitive and easily to adapt, furthermore it is even applicable when little is known about the data or when the data discrepancy is high due to inhomogeneous and noisy data (Hearst & Heer, 2005). When visualizing the data it is important to show the data as clear as possible with a minimum of textual explanation. Clarity can be obtained by simplifying the information through reducing clutter and defining a scope with the aid of sampling, filtering and clustering. In this way, the data is most accessible to all.

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Equipment Selection Method – Step C

When insight is obtained in the processes, the equipment that make these activities possible have to be analysed and selected during step C. To be able to take an informed and well-considered decision, a number of different types of information is required. First of all a step-by-step method to go through the selection process is necessary. Subsequently, for this method a balanced selection tool must be chosen to support the actual moment of selection during the process. And last but not least, information on the latest equipment is required as input for the overall selection process. In the next coming paragraphs we will discuss the required information types. But first we will create insight in the concept of material handling equipment and storage systems.