I recently completed a study of the Supply Chain Planning (SCP)software market. In doing this study, I came across solutions that could not be cleanly classified as either Supply Chain Planning or Supply Chain Execution.
First, a quick background on supply chain planning and execution solutions. SCP solutions are typically based on optimization algorithms. I will spare you a detailed definition of optimization (it might make your eyes glaze over), but in the simplest terms, think of optimization as being very advanced math. By definition, Supply Chain Planning solutions focus on a “planning horizon”-i.e., on putting together optimal and feasible plans to be executed in the coming weeks and months. The primary way companies save money using SCP solutions is through reduced inventory (e.g., reductions in raw materials, work-in-process, or finished goods inventories).
Two types of Supply Chain Execution (SCE) solutions are Warehouse Management Systems (WMS) and Manufacturing Execution Systems (MES). Both are, in some ways, the opposite of SCP solutions. While some WMS solutions have math associated with them (e.g., labor management and slotting), they are not true optimization solutions. WMS and MES operate in an “execution” time frame, focusing on what needs to get done today, in the next few hours, or even in the next few minutes, to operate the warehouse or factory efficiently. The primary payback of high-end WMS and MES comes in the form of improved labor efficiencies.
“Demand sensing” solutions are an emerging hybrid solution that are part SCP, part SCE. One such solution is offered by Terra Technology. Terra is a private company with several consumer goods and food and beverage manufacturing clients. Like SCP, their solution is based on advanced optimization. Terra’s demand sensing solution is designed to take daily orders and shipment data, combine it with the weekly shipment forecast, and then create daily SKU forecasts at the DC level. The daily forecasts extend out over 40 days and are refreshed daily. These short-term forecasts are considerably more accurate than what is generated by demand planning solutions. Further, like SCP solutions, the payback is based on reducing inventory. Procter & Gamble (P&G), for example, publicly stated that its short-term forecasts were improved by over 30 percent, allowing it to reduce DC safety stock by more than 10 percent.
This solution, however, is used in an execution time frame. The primary users of this solution are replenishment planners that plan shipments from the factory to the DC. The planners are not part of the demand planning staff. This solution is generally not used to change the demand forecast, but to improve replenishment processes that occur several times a week, an execution time horizon.
Procter & Gamble also uses a company called Transportation Warehouse Optimization (TWO) that offers another hybrid optimization/execution style solution. TWO’s solutions take the retail DC orders and delivery schedule and create a lean/pull type environment that is used to facilitate both full truckload shipments and efficient labor utilization in the warehouse.
First, advanced math (but not true optimization) is used to build mixed-SKU pallets and to efficiently build truck loads that respect weigh out, cube out, and stop sequencing constraints. In building the mixed SKU pallets, logic is utilized to insure that case picking proceeds efficiently. In other words, you don’t want your pickers moving from one side of the DC to the other to build a particular pallet. The pallet-building logic needs to respect weight/size/crush constraints, while not resulting in excessive travel time for pickers. Finally, the solution has Distributed Order Management style functionality that looks at orders daily, or in near real time, to determine which DC makes the most sense to ship from.
Thus, like supply chain planning, this type of solution contains very advanced math that could be loosely labeled “optimization.” But it can also be labeled an execution solution because it operates in an execution time frame and one of its main payback buckets is efficient labor utilization in the DC.
Transportation Management Systems (TMS) have long been labeled a supply chain execution solution, but they’re really hybrid applications that contain elements of optimization occurring in “execution” timeframes. Now, new SCE solutions that that involve distribution operations have emerged with the same characteristics.
We traditionally believed that you could not optimize across both warehousing and transportation operations simultaneously (at least not in practice). Most companies typically optimize their transportation operations first, and then “sub-optimize” their warehousing operations, instead of conducting a “holistic” optimization that jointly considers both transportation and warehousing constraints to arrive at an optimal plan. In other words, we argued that WMS integrated to TMS did not mean you were achieving true logistics optimization. That argument is starting to breaking down.
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