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Five Ways Digitalized Data Improves Supply Chain Planning and Agile Responsiveness – SupplyChainBrain

Companies are discovering the power of data and analytics to transform their supply chains, but few are actually prepared to take advantage of these benefits. By following the path of data digitalization, companies can position themselves to maximize strategic advantages, enhance tactical supply chain preparedness and optimize supply chain operational efficiency. Here’s how.
Long-term strategy. For many companies, supply chain planning is focused on the sourcing strategies, production footprints and infrastructure investments needed to satisfy customers right now, leaving crucial long-term planning and strategizing processes by the wayside. Enterprises can and should do better at making informed long-term strategic decisions about their supply chains. 
In the face of new supply chain realities, companies at this very moment are contemplating reconfiguring their production footprints, or are reconsidering their sourcing strategies and distribution networks. To make efficacious investment decisions, enterprises need to be able to forecast performance requirements for their facilities and networks in three, five, and ten years — and plan accordingly.
These analyses cannot be performed successfully without detailed, digitalized supply chain data. Data derived from internal and external sources, allows companies to understand the costs and opportunities associated with long-term strategic options. In the absence of this data and analysis, companies fall victim to suboptimal decision-making processes, and in an increasingly risky environment, are left to fly by the seat of their pants.
Granular analysis and scenario testing. Many companies now use high-level data — often future-state modeling based on financial forecasts, sales reports, and transportation analyses — to make supply chain strategic decisions, and don’t bother to dive into the weeds. But building true data-driven future-state models requires the detailed data derived from transportation, distribution, sales, inventory and other databases. Analyzing this data increases the accuracy of company projections on every aspect of the supply chain — including resource plans, headcounts, equipment needs, infrastructure investments, and information technology requirements — and provides companies with the confidence that they are on the right track with their supply chain strategy and planning.
Taking this granular approach will require up-front investments to build the data models; but in the end, the data will drive the flexibility, agility, and certainty that are impossible to derive from assumptions and analyses made with high-level numbers. Digitalized data models allow companies to test scenarios to develop the most resilient plans — and to get responses quickly, without the weeks of discussions typical of the older, outmoded way of doing things.
A unified view. In many companies today, individual units and divisions are empowered to create their own supply chain plans and decisions. These diverse business units often operate their planning processes based on conflicting assumptions, producing adverse results for other business divisions and ultimately, for the enterprise as a whole. When individual units operate in siloed isolation, it’s next to impossible for these units to utilize the synergies they may have with one another.
A data-driven approach allows a unified view of supply chain planning and strategizing processes. By creating enterprise-level data models, an enterprise can ensure that all divisions are on the same page when it comes to planning and that they take advantage of synergies they would otherwise be blind to.
Forward-thinking risk management. Supply chains have experienced disruptions like never before in the last few years — much, but certainly not all, related to the COVID-19 pandemic. As a result, planners are rethinking many aspects of their supply chains, including their dependence on international trade and transportation for their supply sourcing.
This forward-thinking response — an effort to shield supply chains against future disruptions and risks — is akin to building a protective armor around supply chains. But armor, although it may fend off attacks, will inevitably also slow supply chains down, triggering a negative impact on service levels, and ultimately, on enterprise revenues and profits. 
The better approach involves applying supply chain data at the tactical level and incorporating advanced analytics for prediction. Access to analyzed data and prediction models provides companies with the visibility to anticipate what’s coming by running what-if scenarios and crafting customized solutions in advance of the disruptions and risks that they may face. Using digitalized supply chain data allows companies to better prepare themselves for a variety of contingencies, and to execute supply chain shifts swiftly and with agility.
Optimized operations. Companies that are engaged in omnichannel distribution — a strategy that requires transportation and deliveries to be managed in real time — require the proper digital tools. 
Omnichannel facilities are often complex, differing in the extent of their automation and in their product mix. The products themselves move at different velocities, often seasonally, and the channels are also often fluid, with e-commerce commanding attention one week and brick-and-mortar retail the next. 
In the face of this complexity, a digitalized supply chain approach — often in the form of a virtual “digital twin” model of a facility — can optimize product allocation, workloads, and assignments of products and human resources. This type of model can also help companies optimize transportation processes, for example, by allowing company divisions, and even different companies to join forces to increase their use of truckload transportation, relying less on the more expensive less-than-truckload segment and otherwise optimizing resource allocations and product deliveries.
Bridging the Gaps With Miebach
With all of the talk these days of the power of data and analytics, many companies operate under the misapprehension that they possess the requisite data to transform their supply chains. Unfortunately, that’s often not the case.
“What they really have are just records and infrastructure,” says Victoria Ma, head of digitalization, North America, at Miebach Consulting. “There are gaps in their capabilities to derive information that can support decision making.”
Miebach Consulting is a supply chain specialist, bringing an engineering approach to the strategies, systems, structures, and operations involved in transforming supply chains into the agile and resilient organizations they should be — ones that promote enterprise profitability and customer service.
“We help our clients bridge the gaps in developing digital supply chains,” says Bernard Tremblay, CEO of Miebach North America. “We use data to build models that allow organizations to forecast the future and to make better planning decisions at the strategic, tactical, and operational levels.”
Miebach has pioneered the use of supply chain digital twins — virtual representations supported by real-time data — in manufacturing, distribution, materials planning and transportation management. Digital twins not only report what has happened historically, but also help companies analyze causes, predict future impacts, and develop reaction plans. In the case of one Miebach customer, a European consumer-goods company, the supply chain digital twin enabled cost reductions and facilitated optimized decision-making across five product categories, 46 factories, 92 distribution centers and 24 markets.
Ma says, “Helping our clients transform records into data, data into business intelligence, and intelligence into decision making — that’s what we do.”
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