Data infrastructure optimization software
Data integration and quality software
Data availability and security software
Cloud solutions

Expert Interview with Rick Chavie About Managing Big Data for Supply Chain Management

If you want to learn more about the challenges of managing supply and demand chains, then EnterWorks CEO Rick Chavie is your man.

Rick served as as SVP, Global Solution Management with hybris and SAP’s Customer Engagement and Commerce group, where he brought together digital and physical commerce and CRM assets for seamless customer experiences. And Rick not only has industry experience from leadership roles at retailers such as The Home Depot and C&A, but also technology experience from his role as the global marketing leader for NCR’s retail and hospitality business, and management consulting expertise from his partner roles at Deloitte and Accenture, where he served clients across retail, branded consumer and wholesale verticals.

We recently caught up with the Harvard MBA and Fulbright Scholar to get his insight on how supply chain managers should be using Big Data. Here’s what he had to say:

What do supply chain managers need to know about how big data can improve their work?

Among others, there are two major ways that big data can expand the reach of supply chain managers beyond traditional sources of information:

    1. Internet of Things: By adding new information sources ranging from meters on gas lines to monitors on machine usage, there is the opportunity to transform replenishment and fulfillment cycles from a human-driven cycle to automated, real-time means to execute just-in-time fulfillment. Supply chain managers are used to relying on indirect means and forecasts to predict when to initiate a replenishment order, in particular for items that need maintenance periodically. Now, with direct connections to the masses of big data made available by their IoT collection devices, it can transform the replenishment process.

 

  1. Demand-driven replenishment: Similarly, it has long been a challenge to directly observe downstream demand pull cycles, with most companies relying on push mechanisms that are increasingly based on sophisticated forecasting mechanisms. However, as large data feeds are increasingly made accessible through B2B2C platforms for managing master data and product data, the same is true of accessing customer transactions in real-time data stores that shorten the cycle of informing replenishers what is happening on the sales or dealer floor. In the past, the supply chain manager may have known that a store has a certain amount of sales and inventory at the end of a day; now, they can know these things by customer, by location within store and by time of day. This visibility, when combined with the much higher “resolution” of insights available through ecommerce transactions can truly enable omnichannel excellence in supply chain execution.

 

Have supply chain managers learned yet to take full advantage of big data?

Few companies have mapped their systems to take full advantage of such data. The first order of business is taking on the challenge of having base data stores for product, logistics, location and other supply chain information related to the brand. Once the right taxonomies and filters are incorporated in such base data that is shared across the supply chain, it can then be enriched with other big data elements that are ever more granular in nature. But first, the initial data model must be established with proper data governance and with the capability of having a robust dynamic data modeling capability that can be refreshed continually as new data feeds and channels come on line.

What are some of the obstacles that supply chain managers must overcome when leveraging big data to improve their processes?

Beyond having the right data modeling capability and team members, the other challenge is external in the form of supply chain partners and demand enablement constituents. Across the lifecycle of a product, there is also the lifecycle of related data and content streams that must meet required standards for the right attributes, product specifications and descriptions, images and other customer-facing information such as videos and “how-to” product knowledge that can accompany the product to the marketplace. Such information is a collaborative effort across the manufacturer brand, the wholesale distributor and the retail/commerce outlet, in the form of B2B2C structure of connecting business to business to customer for both product supply chain, as it converges with the content value chain to inform both supply side and demand side needs.

What are some of the best tools available for supply chain managers to work with big data?

Certainly the base case is that supply chain managers need a product lifecycle management (PLM) system that feeds and informs a Product Information Management (PIM) system under the umbrella of Master Data Management (MDM) platform that extends in related domains such as locations, brands, customer, etc. to put the product flows into the right context for executing against demand patterns. Then, from the unstructured data side where people look to have other big data sources complement such typically internal information, there needs to be collaboration tools such as Portals for exchanging information, syndication capabilities for distributing information to a broad set of downstream retailers/commerce entities, and a platform for managing such external data such as using Hadoop or Cassandra to make major external data stores accessible for use by supply chain managers.

How can big data help with demand management, supplier management, customer management and other aspects of managing a supply chain?

A key aspect of big data is enabling the kind of fulfillment personalization and personalized logistics that is becoming pervasive in all types of commerce transactions. Whether it is fulfilling a drop ship Direct-to-Customer (D2C) or putting inventory directly to shelf through direct store delivery (DSD) mechanisms, demand management and fulfillment informed by big data and real-time foresting capabilities is more robust than ever in responding to demand efficiently. The level of customer service – in the form of in-stock and “ship complete” compliance correspondingly is enhanced. Whether in a B2B setting or B2C, supply chain managers can have an increasing influence not only on minimizing cost of supply chain and inventory levels, but also can affect and lift sales through more timely responses to demand.

What are some of the reasons that supply chain managers should consider taking on big data?

Such managers need to tackle big data – whether for a region, a category or a major customer segment – to avoid falling behind the wave of innovation that is occurring. In addition, the upside is clear: It will be more profitable to do so!

Connect with EnterWorks on LinkedIn, Twitter and Facebook.

0 comments

Leave a Comment

Related Posts