The Analysis Phase of Your Material Handling Project
The questions that are asked of the client in the Discovery Phase and the answers provided, drive what we need to ask of the data in the Analysis Phase. These specific constraints that the client is experiencing in their operations where excessive costs or inefficiencies can help guide specific inquiry to better understand the current state of operations.How does deeper questioning provide improved clarity? Discovery is the lens by which Analysis can be focused.
Free Guide: Top Order Fulfillment KPI Indicators
Models are built which show the current relationship between supply and demand. Once this is done this understanding of order and product velocities can be compounded by growth factors agreed to by the project team. By applying this predictive forecasting an understanding starts to take shape of the current challenges, potential solutions, and the appropriateness of what solutions can best address the business goals faced by the client today but the result of inaction further on down the line.
Pitfall #1: Asking the wrong questions of your data will lead towards the wrong solution.
The greatest asset in being an experienced Integrator isn’t knowing what the solution should look like before you begin but instead possessing the ability to ask the right questions of a customer’s data to uncover the true root cause of problems. Not taking the time to review key metrics including important design parameters such as order profiles, SKU affinity, labor costs, materials to be handled, rates, service levels, ship methods, etc. can result in major system design flaws which are not exposed until later when it is much more difficult – if not impossible to course correct.
Pitfall #2: Avoiding the work up front to truly understand the scope of the assignment can cause costly delays and project overruns later.
Before any analysis can commence, the data is received from a client (order history, item master, receiving history, etc) it must be validated and each data point thoroughly understood. Once imported into the modeling tool the data should be queried to generate summaries which can be presented to the client. This assures the accuracy of what is being reported coincides with real world experience of their operation. For example, when summarizing historical order demand clients have a excellent handle on the volume that was able to be shipped during peak periods and can easily identify errant information.
It is also important to identify and review each data point with each data set received. Data mining column by column, notes are taken and the purpose of each data point is carefully discussed to understand what it means within that specific customers operation. Often groups of SKU’s can have unique terminology within categories or divisions. Also, the Unit of Measure (UoM) within order history can be reported differently in the way it is summarized ( for example by line, piece, inner pack, case, etc ) and it is imperative to understand this information to properly balance the demand of each item with the right sized storage methodology based on pre-determined criteria.
Pitfalls #3: Never limit the analysis to operational and data investigation.
A firm understanding of operational touch points including current systems in place and order processing throughout the operation is imperative. A critical component of the Analysis Phase includes an assessment of the current infrastructure of information technology and how data is moved between the various subsystems in an operation. Only by understanding the various touch points and how order data flows from the ERP through the four walls of the warehouse and beyond up front can the system can be flexible to scale for the future.
By including key members from throughout the organization including Operations, Facilities, Finance, and Information Technology during the Discovery phase key contacts will be established. These team members from IT will be critical as we analyze the movement of a customer’s order through the various order processing and host systems before it gets to the warehouse.
In a broad sense understanding how often these orders can be released to the warehouse can have an impact on how large an order set can be batched or if work is dynamically added to existing batches as it dribbles in from a real time interface. Equally as important is an understanding of how late orders can continue to be accepted at the host level before being dropped to fulfillment and still need to ship the same day. This example illustrates how alignment between all facets of an organization are critical throughout the process for proper system design.
Conclusion:
Careful analysis is a key component to the overall success of a well designed and implemented material handling project. Having a vision of what the picture should look like is important before selecting the RightFIT pieces to putting them in place. Obtaining the correctly shaped pieces by way of careful analysis from a non-biased source that has all of your company’s best interests in mind is something that should not be compromised to ensure you end up with a successful RightFIT solution.
