Creating seamless flow in a fulfillment stream requires that all departments and functions work in harmony. One way to make sure that your fulfillment stream is optimal is to invoke the principles of lean. In this approach, you focus on the fundamental lean principles of eliminating waste so that only value remains, which in turn helps achieve the harmony mentioned above.
Seven types of fulfillment wastes
All Supply Chain and manufacturing professionals are very well verse with the seven wastes in manufacturing : Overproduction, waiting, conveyance, processing, inventory, motion, and correction. Similar to manufacturing wastes, Martichenko and Grabe have identified seven wastes for Supply Chain fulfillment. Below, we will briefly review those wastes and discuss how Analytics and Technology can help us mitigate these wastes.
Waste 1: System Complexity
Elaborate scheduling systems and managers working around mismatches between the formal schedule and actual needs.
Solution: The solution here, in this age of easy availability of open source solutions and data science talent, is to build customized solutions, on top of your existing ERP modules, that provides your managers ease and visibility into data, metrics and schedules, they way business wants to see it, NOT the way the ERP interface spits out.
Waste 2: Lead Time
Too much cycle time from one step to another in the fulfillment process (excluding transportation)
Solution: The key technology foundation needed here is an end to end platform to track the entire fulfillment process ranging from order processing to delivery. Then, on top of that Technology platform, you need to build a Business Process Management (BPM )platform to help manage the process and process KPIs, in order to optimize the processes. To be candid, standard process modules built within ERP systems like SAP are too standardized to effectively track the processes.
Waste 3: Transport
Excessive conveyance among facilities and companies.
Solution: The key technology foundation discussed above in lead time will incorporate transportation as well and hence will provide visibility into transportation process. Then, on top of that Technology platform, you need to build an Analytics platform. All three forms of analytics, Descriptive, Prescriptive and Predictive need to be leveraged to reduce transportation lead time.
Waste 4: Space
In lieu of processing, space becomes a value factor and excessive space for storing inventories is waste.
Solution: A bottleneck that I see is that the current generations of WMSs have not evolved much in last two decades. While complexities of fulfillment (like Omnichannel) have increased, WMSs have not evolved much, with the exception of few band aids now and then. Building a Smart, customized WMS in this age of easily availability of technology and computing power is not an impossible tasks and companies should really look into this option to optimize their warehousing space.
Waste 5: Inventory
Inventory at any point and in any form should be considered a wase and should always be a candidate for minimization.
Solution: Eternal pain point of Supply Chains, my firm belief is that if you can streamline your transportation and warehousing waste, as discussed above, a significant portion of your Inventory issues will get resolved. Another significant driver of inventory is demand uncertainty and Machine learning based forecasting tools can help reduce forecasting errors leading to reduced inventory.
Waste 6: Packaging
Wrong type of goods in the wrong quantities resulting in damages, excessive inventories, and corrections downstream.
Solution : Packaging optimization methods can be leveraged to optimize the packaging processes. Packaging optimization generally needs to happen in tandem with load optimization to achieve full benefits.
Waste 7: Human effort
Fulfillment team members working at cross-purposes, which creates rework, confusion, and excessive motion.
Solution: The Business Process Management (BPM )platform identified in the “Lead Time” waste should help manage the human effort better as well. In addition to that, effective labor planning algorithms need to be developed, specifically for order processing, warehouse operations and fleet.