Traditional supply chain planning and optimization tools fail to provide timely information, especially in markets requiring manufacturers to be agile, and respond to changes instantaneously. Since planning cycles are monthly or weekly, the plans fail to provide day-to-day operations with information to respond to daily variability of actual to plan. Operators rely on spreadsheets and tribal knowledge.
Companies should leverage digital technologies to deploy business-model-based planning and optimization that transcends functional boundaries, collects data from new sources (internal and external “big data”), and applies advanced analytics. Doing so enables them to facilitate cross-functional collaboration at near real-time speeds. Leveraging the convergence of digital technologies enables companies to create new platforms for connected digital collaboration. This supports processes and applications that overcome the constraints of functional silos and boost the synchronization, accuracy and timeliness of plans – within the enterprise and with customers and suppliers.
When Amazon built its applications, it made conscious strategic decisions. First, it built its own systems. Amazon was a unique business model which no off-the-shelf application supported. Second, it built its applications on Linux and open source code. Knowing that they had to provide an equally delightful delivery experience as well as a customer marketing experience, Amazon tightly integrated its order and fulfillment services with in-line optimization to ensure rapid, cost-effective response to customer demand.
While much is made of the “Amazon Effect” to business strategy, little is said about the operational impact of the effect. As companies embrace and compete in omnichannel demand-supply network management, they will look toward supply chain optimization tools that transcend planning and integrate planning and execution as iterative real-time applications. Supply chain optimization tools are generally model-based. A company’s demand-supply network is “modeled” and populated with relevant data and algorithms to enable scenarios to be simulated to suggest an optimal solution that considers all of the constraints, options and trade-offs to provide fulfillment at least total cost.
In the past, solve time for models was measured in days and weren’t practical for day-to-day operations. One could argue neither do ERPs. That said, in today’s computing environment solve times are hours and minutes and can model just about anything. Leveraging digital technologies, companies today can consider modeling their entire demand-supply network with variable data input in real time with, let’s say, recalculation several times a day – Amazon is doing it, so is Procter & Gamble.
When planning is integrated to execution and optimized frequently, operators are able base their decisions on the demand plan and the plan can consider actual demand simultaneously when re-planning. The result is lower operating cost, liberation of working capital, and increased revenue recognition.
In 2018, you can expect to see traditional supply chain planning and optimization tool providers offering solutions for demand planning and optimization. As solve time goes down and compute power goes up, the providers will leverage their algorithm capabilities to integrate multiple data streams of demand information to provide more robust causal analysis to demand planning. Look to these providers to be the bridge between functions to enable digital collaboration for improved planning accuracy.
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