DIpil Das
We present key insights from a Coresight Research webinar on September 23, 2020, in which Infor VP of Solutions Strategy for Supply Chain Management, Heidi Benko, discussed the importance of supply chain visibility and resilience in the new world of retail. The webinar was hosted by Deborah Weinswig, CEO and Founder of Coresight Research.
Resilient Supply Chains: A Necessity for Retailers
Weinswig and Benko discussed the defining features of a resilient supply chain in today’s retail landscape. A resilient supply chain, Benko stated, is one that can both weather strong economic shocks and quickly respond to changes in the retail, macro-economic, and consumer behavior landscapes. The coronavirus crisis has made the importance of having strong, adaptable operations and sourcing processes more apparent than ever. Weinswig mentioned Coresight Research’s increasing focus on sustainability this year and believes that sustainability and supply chain resilience go hand-in-hand. Coresight Research has also noted that 29% of US consumers now think more about sustainability while shopping since the pandemic began.
Supply chain resilience often comes down to the ability to automate processes and adjust quickly. Benko noted that retailers are now keenly aware that they cannot afford to rely on cumbersome manual processes in the supply chain. Today, changes in the global retail climate happen almost instantaneously, whether through major world crises like the coronavirus pandemic or the simple shifts in consumer preferences. Either way, companies must ensure that their supply chain systems are as automated as possible, from supplier to last-mile fulfillment. Last-mile fulfillment has become particularly important over the past six months, as online sales have boomed and major logistics providers begin to experience capacity constraints. Benko told Weinswig that the first step Infor takes with any client is to identify where they have the most manual processes in their supply chain network and identify how to automate them.
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Deborah Weinswig and Heidi Benko discuss the growing role of machine learning based demand forecasting in retail
Source: Coresight Research [/caption] Optimizing Inventories and Fulfillment with Machine Learning-Driven Demand Forecasting Benko believes that demand forecasting is possibly the most important facet of supply chain management. Companies increasingly need to be able to sense and predict the latest demand so they can provide the right product to the right consumer via the right channel. As the boundaries between offline and online retail are blurred by in-store fulfillment and buy online, pick up in store (BOPIS) options, retailers must efficiently navigate an increasingly complex array of fulfillment channels. Previous planning systems, Benko says, were not up to this challenge. Nor were they able to incorporate the vast quantities of information that machine learning-powered forecasting systems have the ability to process—such as those provided by Infor, Benko explains. Machine learning-powered demand forecasting enables retailers to plan and execute an end-to-end supply chain strategy, all the way from production to last-mile fulfillment. Benko touted the ability of this type of forecasting to factor in a diverse array of effects ranging from the activity of social media influencers to changes in the macro-economic backdrop of various regions. Machine learning can maximize supply chain efficiency, cut costs and even drive revenue by ensuring that consumers always have the ability to seamlessly buy the products they want on the channels they favor. Collaborative Supply Chain Data Management Can Benefit All Involved Benko emphasized that although retailers have more data at their disposal than ever before, they can still benefit from working closely and openly with suppliers to share data and gain more meaningful insights. Even in the most concentrated supply chains, Benko explained that 80% of data is held outside of any one enterprise. Traditionally, large retailers have been reluctant to share data with outside parties, but Benko claimed that this era of adversarial supply chain management is ending. Benko extolled the benefits of shared data visibility across the supply chain, arguing that it is a necessity for a successfully managed and integrated end-to-end supply chain. Weinswig and Benko agreed that retailers are increasingly seeing supply chain data sharing and demand forecasting as not just an opportunity to cut costs, but an opening to drive revenue as well. Weinswig noted the growing number of retailers who have a Chief Supply Chain Officer and expects automated supply chain management to play a growing role in retail going forward.
Deborah Weinswig and Heidi Benko discuss the growing role of machine learning based demand forecasting in retail Source: Coresight Research [/caption] Optimizing Inventories and Fulfillment with Machine Learning-Driven Demand Forecasting Benko believes that demand forecasting is possibly the most important facet of supply chain management. Companies increasingly need to be able to sense and predict the latest demand so they can provide the right product to the right consumer via the right channel. As the boundaries between offline and online retail are blurred by in-store fulfillment and buy online, pick up in store (BOPIS) options, retailers must efficiently navigate an increasingly complex array of fulfillment channels. Previous planning systems, Benko says, were not up to this challenge. Nor were they able to incorporate the vast quantities of information that machine learning-powered forecasting systems have the ability to process—such as those provided by Infor, Benko explains. Machine learning-powered demand forecasting enables retailers to plan and execute an end-to-end supply chain strategy, all the way from production to last-mile fulfillment. Benko touted the ability of this type of forecasting to factor in a diverse array of effects ranging from the activity of social media influencers to changes in the macro-economic backdrop of various regions. Machine learning can maximize supply chain efficiency, cut costs and even drive revenue by ensuring that consumers always have the ability to seamlessly buy the products they want on the channels they favor. Collaborative Supply Chain Data Management Can Benefit All Involved Benko emphasized that although retailers have more data at their disposal than ever before, they can still benefit from working closely and openly with suppliers to share data and gain more meaningful insights. Even in the most concentrated supply chains, Benko explained that 80% of data is held outside of any one enterprise. Traditionally, large retailers have been reluctant to share data with outside parties, but Benko claimed that this era of adversarial supply chain management is ending. Benko extolled the benefits of shared data visibility across the supply chain, arguing that it is a necessity for a successfully managed and integrated end-to-end supply chain. Weinswig and Benko agreed that retailers are increasingly seeing supply chain data sharing and demand forecasting as not just an opportunity to cut costs, but an opening to drive revenue as well. Weinswig noted the growing number of retailers who have a Chief Supply Chain Officer and expects automated supply chain management to play a growing role in retail going forward.