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Article
Supply Chain Solutions--Foundation for the Omnichannel Revolution

Highlights of what we learned at NRF's annual conference from providers of supply chain planning and optimization, IoT and RFID, omnichannel and order management, global logistics, PLM, and Geographic Information Systems.


Full Article Below -
Untitled Document

After talking to many supply chain solution providers at the NRF ‘Big Show,’ some recurring themes emerged: quicker and easier end-to-end integration and implementation, big data and machine learning, rising importance of product attribute data, IoT & RFID, low/no training ease-of-use, and the expansion of solution provider’s footprints blurring the boundaries between solution categories. Here are some of the highlights.


To jump to a particular company, click the link below:



Logility, Built-in Integration Advantage

Logility has a rich history as a best-of-breed vendor offering a deep suite of supply chain optimization and retail planning capabilities, including demand management, inventory optimization, transportation and logistics, supply optimization, and S&OP/Integrated Business Planning, as well as a rich set of analytics and visualization. They have a strong set of retail-specific functionality for merchandising assortment planning, allocation and replenishment. An important recent development is their acquisition of AdapChain, an integration platform that is purpose-built for pulling in and pushing out supply-chain-related data from/to other systems (ERP, CRM, sourcing and procurement, other supply chain platforms, etc.) and external sources (e.g. data from suppliers, third party data providers such as weather, various indices, etc.). Since integration is typically the most difficult and time consuming part of implementation, this acquisition enables faster implementations, as well as the ability to evolve and maintain integration as the systems around the Logility suite continually change. For more on this approach, see Agile Integration.

Also at NRF, Logility announced their implementation of integrated planning and execution with Nebraska Furniture Mart. Nebraska Furniture Mart carries over 650,000 SKUs in furniture, appliances, electronics, and flooring. They use Logility to integrate with suppliers, shippers, and customers, providing much better visibility into the flow of products, both inbound and outbound. This has helped them to make reliable ATP (Available-to-Promise) commitments, fulfilling from different places in the network, including drop ship from the manufacturers. Their furniture business involves kitting of an enormous variety of combinations of components. This creates challenges in getting the right proportions of the right components stocked at the right places, to account for differences in regional and seasonal preferences (different fabrics, wood types, etc.). Logility gives visibility into those preference variations, enabling more accurate stocking against demand.

Logility is strong in fashion forecasting. An example is Groupe Dynamite, for whom accurate size profiling is critical. They need to account for differences in seasonality across the globe. For example, winter in northeastern U.S. is very different than winter in Middle Eastern countries. Groupe Dynamite also needs to account for cultural and taste differences, such as skirt lengths being longer in the Mideast. Using Logility, Groupe Dynamite reduced size variance (supply vs. demand) at the store level by over 3%, thereby selling more items at full price rather than at markdown. They also have moved to pull-based replenishment, holding back 50% of inventory at the DCs and using the store’s demand signals to determine when and where to send the remaining inventory. That helps reduce markdowns and costly inventory transfers from one store to another. In fact, 80% of Group Dynamite’s replenishment decisions are made automatically by the Logility platform. That frees up planners’ time from doing everyday replenishment, allowing them to focus their time on maximizing performance of promotions and new product introductions.

I asked why customers chose Logility over competitors. They said their customers consistently tell them it was the usability; a comprehensive solution that they can adopt quickly and use successfully, out-of-the-box. Usability and rapid implementation equate to faster time-to-value realization.

JDA’s Retail.me, Machine Learning Approach to Persona Clustering

I got an update on JDA’s Retail.me offering, which uses machine learning to analyze actual customer buying behavior (such as what each customer buys, where and when they buy, what and where they return, etc.). The machine-learning algorithms discover groups or clusters of customers that have similar buying behavior. This contrasts with the traditional approach of using customer demographics to define customer personas and clusters. The behavior-based approach creates clusters of people that actually have the same tastes and buying patterns, providing a more accurate prediction of future buying and returns behavior, compared with assuming that everyone with similar demographics has the same tastes and behavior.

I asked how they get data on actual customer shopping behavior. They said it involves bringing together data from multiple sources, such as customer loyalty data, POS data, product attribute data, promotions data, and other data. They said that is not easy, but is essential to really know your customers. Retailers have started working on getting more consistent and richer product attribute data. This makes getting started a little less daunting.

JDA said the Retail.me vision is resonating—three large international fashion apparel retailers signed up last year, and they are aiming for two or three times as many this year. They work collaboratively with the merchants at JDA’s clients to figure out which attributes will be the most relevant and feasible. They said the persona clusters generated by the system successfully captured merchants’ intents, so was considered a success. A big part of the appeal here is ease of use and a very graphical user interface that communicates a lot of information intuitively. A retailer’s journey implementing Retail.me can start with existing processes left largely as is, using the machine learning to better understand which products are selling at which stores, based on product attributes. Then over time, behavior-based clusters and other advanced practices can be put in place.

RELEX Solutions, Optimization with a Strong Grocery Footprint

RELEX may be more familiar to European retailers than to those in North America. The company was founded in Helsinki in 2005, with a focus on using in-memory technology for rapid demand forecasting, inventory optimization, and replenishment automation. Last year, they acquired Galleria, expanding their footprint into assortment planning, category optimization, and micro and macro space optimization.

RELEX has a strong presence in grocery, especially fresh lines within grocery. One of their strengths is single number, integrated forecasting. They project stores’ future needs and order patterns, and use that to calculate demand backwards into the supply chain, determine when and what to replenish, in some cases all the way back to the supplier. For some of their fresh food customers, they support a FEFO (First-Expired, First-Out) distribution paradigm, using the products’ expiration dates to decide which items to ship next.

RELEX said that part of their philosophy is ‘no black boxes,’ i.e. if the system comes up with a number, no matter how sophisticated the science, the user should be able to go back and see why and how it came up with the number. They also provide ‘what-if’ functionality, so planners can test out different scenarios and see all the effects before ‘pulling the trigger’ to implement changes. RELEX currently has nearly 200 customers (retail, wholesale, and manufacturing) in 20 countries. They have had some recent wins in both the U.S. and Canada, and are making investments to expand further in North America. They told me they have 100% customer referenceability.

Checkpoint, RFID End-to-End, From Source to Store

Checkpoint is perhaps best known as a long-time leader in EAS (Electronic Article Surveillance). However, they have a considerably broader portfolio of products and services, including RFID and labeling hardware and software that extends from the retailers’ stores all the way back to the source manufacturer. Checkpoint was recently acquired by CCL Industries, the world’s largest labeling company. Checkpoint was already the second largest provider of apparel labels in the world, so the combined company is a labeling powerhouse. The acquisition opens up other industries to Checkpoint as well. It provides Checkpoint with financial stability and a parent company investing in growth.

Part of Checkpoint’s strength is their capabilities for source-tagging and their end-to-end footprint across the supply chain. Macys, Marks and Spencer, and other retailers who sell primarily supplier-branded products (i.e. not private label) are pushing or mandating their suppliers to tag items with RFID at the source. These suppliers, most of whom would not otherwise implement RFID, are now thinking about how to leverage the investment that they are being forced to make—how to get some value beyond just complying with their customers’ mandates. Checkpoint has developed solutions that help suppliers get this value from their RFID investments, such as the ability to do automated 100% audit of all shipments, to eliminate shipping errors and the resulting chargebacks, using little or no additional labor. In fact, some suppliers can reduce or eliminate the labor they currently use to manually count or rescan shipments, to double-check accuracy. Checkpoint offers different solutions for different situations and volumes. For high volume operations, they have a highly accurate, high-speed tunnel reader. For low volume, they provide handheld or countertop readers. Checkpoint told me they are starting to see interest by some suppliers in doing shipment verification to get some value out of their RFID investment.

GT Nexus, Networked Platform for Global Supply Chains, Adding Ground Transportation Functionality

GT Nexus continues their integration into Infor, after being acquired in 2015. Infor’s other supply chain solutions (Supply Chain Planning and Execution) have been brought together into one Infor supply chain organization, with GT Nexus as the centerpiece of their supply chain suite. GT Nexus is one of the only true networked platforms for global trade and logistics. They partner with Integration Point for GTM (Global Trade Management) to provide capabilities such as denied party screening, trade content, FTA (free trade agreement) opportunities, Free Trade Zone, and customs clearance. About $500B of trade is managed on the platform, which connects 350 major corporations to over 28,000 suppliers, 13,000 carriers, and 40 financial institutions. There are many advantages to a networked approach, including pre-connected suppliers and carriers, and a single shared ‘network-of-record’ between trading partners (a.k.a. a single-version-of-the-truth). This greatly reduces the latency of information sharing, as well as disputes about what actually happened. Another example of the advantages of a networked platform is what GT Nexus is doing in sustainability with some well-respected retail brands. For example, one program enables the retailers to share common audit questions with others on the network so they don’t have to reinvent the wheel, and (eventually) share audit results (within security constraints, of course). This will help reduce ‘audit fatigue’ and time wasted by suppliers and the brands responding to nearly identical audit surveys.

GT Nexus has a long history in supply chain finance, where they are one of the few companies to provide pre-invoice and pre-shipment finance (see Reinventing Supply Chain Finance). One of their partners in this area is Seabury TFX, who uses the historical trading partner performance data on GT Nexus to provide expedited trade financing at lower rates.

GT Nexus was founded almost 20 years ago, with a focus on the end-to-end international shipments, primarily in ocean and air shipping. Over the past couple of years, they have been investing in building out ground transportation capabilities as well, adding multi-modal coverage to existing capabilities such as sourcing (RFQs, contract management and compliance), rating (centralized, standardized rates across modes), planning and optimizing loads, tendering loads, and a next generation version of freight payment & audit (2H 2017). One of GT Nexus’ differentiators is the underlying architectural support for the aggregation and coordination of transport planning, execution and visibility data across multi-leg and multi-mode complex shipments. This allows a customer to use different TMS systems (from GT Nexus, or JDA, SAP, MercuryGate,1 Descartes, or others) to manage ground or air transportation in different regions or business units and then feed them all into the GT Nexus umbrella system for oversight of the end-to-end move. Ultimately, GT Nexus will provide multi-mode planning and optimization scenarios from international origin to domestic end customer. This allows earlier and more accurate total cost estimations, as well as greater visibility in one place.

GT Nexus is in the process of integrating with Infor’s various legacy ERP and newer cloud platforms. For example, they are in the process of integrating GT Nexus with Infor M3 (formerly Lawson) and LN (formerly Baan) to automatically take in purchase orders (POs) from those platforms and then have suppliers collaborate item-by-item on whether or not they can meet the requested quantities and dates. Suppliers are able to give partial commits; like ‘I can ship this many on the requested date, and this many on this later date.’ GT Nexus lets the buying customer set up rules to automatically accept the supplier’s proposed shipment dates if they fall within certain thresholds. If the supplier’s proposed dates fall outside the thresholds, the system provides for back-and-forth person-to-person order collaboration between buyer and supplier to come to agreement on the quantities and dates of shipments. This collaboration happens directly within the M3 or LN user interface, using Infor Ming.le on the side panel of the actual transaction (PO) being discussed. Once agreed, GT Nexus automatically updates the M3 or LN ERP system. GT Nexus is working on the next phase of integration, automatically pushing ASNs into these ERP systems and integrating with their accounts payables and accounts receivables functions.

Infor’s Big Retail Suite Investment

I also spoke with Infor about their Infor Retail suite. As we wrote about last year, Infor is investing impressive amounts to create a built-from-scratch, full-suite retail solution set built on an advanced modern UX2 running in the cloud. There are now 900 employees in their Retail group, up from 500 last year. Infor has a retail suite that spans across merchandising planning, supply chain collaboration to the store with converged commerce. This large investment reflects Infor’s conviction that existing systems are not meeting retailers’ needs. For example, most older systems only allow a very limited number of product attributes (as few as four in some systems). Today’s machine learning algorithms for retail are thirsty for a rich set of product attributes, needed to make smarter forecasts, persona definitions, assortment and allocation planning, and other recommendations. Those algorithms are starved of data by existing systems. Infor’s system allows unlimited numbers of attributes, including custom attributes. Attributes are in fact central to the design of their system. Infor’s strategy is to build out a foundation of item-level attributes, with the goal of making an external PIM (Product Information Management) system unnecessary.3

Most older systems are limited in the volumes and types of data they generate or can consume—they are usually not set up for big data, such as incorporating IoT streaming data. Infor told me they are designing their system to consume big data, as more and more external data (social media, weather, granular supply chain tracking, etc.) and higher volumes of internal data (i.e. the integrated store) becomes available and relevant to retail solutions. Infor is building more connectivity between their various modules and other systems, to provide granular inventory visibility all the way from stores, back to the DC, transportation, suppliers, and third parties through the network they acquired with GT Nexus, and integrating with other systems (e.g. Manhattan or other WMS), to provide a single supply-chain-wide view of inventory.

The first release of Infor’s retail suite was last year. They have Whole Foods and Crate & Barrel as lighthouse customers. They are actively working with fashion retailers, so they can demonstrate the ability to serve a spectrum of retail formats and needs. Infor is definitely a player to watch in this space.

Mojix Makes Its Move in Blockchain

Mojix started in the business of making UHF RFID systems with a unique architecture for providing real-time locating capabilities across a wide local area with their STAR RFID reader. In 2015, they acquired TierConnect and TierConnect’s IoT Platform. That same year, they released ViZix, their cloud-based IoT Platform, designed to take in data from any sensor, ingesting and processing high volumes of data through its CEP (Complex Event processing) engine. ViZix is built on distributed, high-availability, high-scalability, open source, NoSQL databases.

At NRF, Mojix announced TurboAntenna, a phased-array RFID antenna that provides better locating and more robust reading of dense fields of static RFID tags in reflective environments as found in retail. The patented TurboAntenna works by adjusting the array to randomize the pattern of hot spots and cold spots,4 to ensure that no tags get lost in nulls (cold spots). They also announced their Retail Task Management App, which uses RFID data to alert store associates when various tasks need to be done, such as a shelf that needs to be replenished, or items put in the wrong location. If the back store is outfitted with locating readers, it could also tell the associate where to find those items.

A buzz-worthy part of their NRF announcements was Mojix implementing a new application on its ViZix platform called BlockLogistics that supports smart contracts, using Microsoft’s Ethereum-based Blockchain-as-a-Service, running on Azure. The idea is that Mojix will get contract and PO information from ERP systems (SAP and Oracle) and use that to create smart contracts—code that executes in a blockchain, which is a distributed ledger shared by all parties involved in the transaction. The blockchain provides a common, shared ledger in which to immutably record transactions and events, as they happen in the supply chain. In many cases, this record of events could be based on physical evidence presented and digitally signed by authenticated RFID readers (for example ‘items 123 passed through dock door XYZ at such and such date/time’), as well as automated execution of encoded contracts based on specific events happening.

The use of RFID can now link physical movement of goods into the electronic transactions, in a way that all authorized parties have the same precise, granular, irrefutable visibility. Here is my speculation on how this might work: The manufacturer/supplier could create an ASN that is not only tied back to the PO, but includes EPC codes of all items in the shipment, generated by actually reading those tags as they are packed (probably before being palletized, and loaded onto the truck … though a ‘double-check’ read at the dock door might be a good idea, even if all tags couldn’t be read). If the PO is stored in the blockchain, then a smart contract associated with the ASN might decrement items from the PO, or at least record that these items were shipped against that PO. In any case, that ASN could be recorded in the blockchain (probably sent as EDI, in parallel for now). When the retailer or other customer receives the goods, all of the tags would be read and the smart contract might kick off an evaluated receipts settlement payment. In the hopefully rare case where tags read at receipt don’t match the EPC numbers in the ASN in the blockchain, some sort of exception code could be run sending out an alert for resolution, potentially semi-automating claims resolution. This way there is a shared single-version-of-the-truth between buyer, seller, carriers, 3PLs, banks, and others involved in the transaction. Again, this paragraph is my speculation on how this all might work—I will be getting a briefing from Mojix in the near future on the actual specifics. Mojix plans on trialing this technology with at least one retailer this year.

Distributed Ledger Technology such as blockchain could also be used to track provenance of items where transparency is important. It could provide an immutable, digitally signed documentary trail on an item level, from source throughout each chain-of-custody handoff. Everledger is already doing this with diamonds. Mojix could provide a blockchain-based provenance tracking platform more broadly across many different types of products. For more on emerging and high profile uses of blockchain, see Blockchain Gets Real.

First Insight Teams with PTC for Optimized Line Planning

As we’ve written in the past (see Pricing - It's Only a Game), First Insight uses gamification to solicit input from consumers on preference and pricing for different items (especially fashion items) and variations that a brand owner or retailer is considering offering. Within the gamified survey, along with hypothetical future items (typically constituting the majority of the items), the brand owner/retailer will include a few existing (already sold) items, for which the price elasticity and actual demand history are already known. This provides a way to rank participants by their ability to predict, giving greater weight to those respondents that are better at predicting how the actual items performed.

At NRF, First Insight met with us, together with some folks from PTC, to show their machine-learning-based Optimized Line Planning (OLP) functionality, which can integrate into PTC’s Flex PLM. The OLP enables designers to make more informed decisions by showing them which combinations of product attributes resonate the most with different customer segments. A series of questions allows the tool to segment customers into different personas or buying tastes and habits. The example that First Insight gave me was people who buy Yoga clothes; some buy those items as a fashion statement to wear around town, some use them as active wear for on-the-go activities (like biking), and others just want something comfortable for lounging around the house. Each of these segments has different tastes and different things they look for in yoga pants. By looking at overall revenue numbers and CRM data, you can see which segments are bigger and/or growing faster. If a designer needs to design 10 SKUs for the next season, OLP can recommend how many should go to each segment and which combinations of attributes each are looking for. This is done by taking historical product sales from prior seasons, plus the consumer testing from First Insight, and running these through the machine learning algorithms in PTC's ThingWorx platform. It then provides assortment recommendations and pricing guidelines that optimizes revenue.

First Insight told me their clients are seeing measurable results. Some customers are realizing 25% reductions in markdowns, 30-70% improvements in new product success rates, and 3-9% increases in profit margins across all categories and geographics.

PTC’s FlexPLM Expanded Footprint

PTC is known for their evolution from originally being a CAD company (founded in 1985), then expanding into PLM, ALM, Service Supply Chain, and more recently IoT. In 2005, PTC acquired Aptavis, from which came FlexPLM, PTC’s PLM solution for Retail Fashion and Apparel. Their customers include broadline and apparel and footwear retailers, such as Target, Walmart, Home Depot, Dick’s Sporting Goods, Chico’s, TJX, Patagonia, lululemon, Marks & Spencer, and Brooks Brothers; and well-known brands such as Polo Ralph Lauren, Levi’s, Lacoste, Nike, Adidas, Newell Rubbermaid, and Timberland.

Recently PTC has been investing heavily in FlexPLM. Its capabilities are broad, including line planning, design and development, BOM management, costing, vendor planning, collaboration, and sourcing. Over that past year, PTC has defined a series of transformational ‘journeys’ for their customers. These are roadmaps for digital transformation. PTC has defined a journey for retail that addresses some of the industry’s trends, such as the move away from delivering products once each season to more of a continuous product delivery cadence (new products introduced every month). This provides consumers with another reason to return to the store to see what’s new. Another trend, particularly in athletic footwear, is personalization, initially for personalized aesthetics (color, materials), but eventually for person-specific performance maximization as well.

PLM is no longer a siloed function. Hence, FlexPLM supports collaborative development; connecting design and development to planning, sourcing, and costing; bringing in the voice of the customer (see their partnership with First Insight above); vendor participation, where trusted suppliers can participate to varying degrees in design collaboration and decisions; and near-real time, precise supply chain transparency via IoT connected factories (including outsourced manufacturers). In addition to the Optimized Line Planning described above, they support digital concept management, to speed up the development cycle, through digital ideation and concept development, leveraging 2D and 3D modeling to simulate fit and visualization, thereby shortening or eliminating the first prototype, and in some cases, being able to jump directly to the final sample.

Bamboo Rose, PLM + Marketplace

Bamboo Rose (formerly TradeStone)5 provides PLM, Sourcing, Procure-to-Pay and B2B Marketplace solutions for retailers and brand owners of apparel, footwear, and hard goods, including marquee clients such as Saks, Talbots, Home Depot, Lowe’s, Walgreens Boots Alliance, Guitar Center, Hudson’s Bay, Ascena, Belk, and Family Dollar. Over time they have built up a cloud-based community of retailers/buyers, wholesalers, and sellers/manufacturers called Bamboo Rose Marketplace (launched in 2012). The platform is designed to compress communications and development cycles. They told me one of their largest customers cut six weeks out of the development cycle and reduced sampling by 80% (in part by leveraging 3D visualization). In today’s fast fashion, short product lifecycle world, the old ways of emails, long sourcing trips to Asia, and multiple sample cycles no longer work. Bamboo Rose solutions compress design, source, and procure-to-pay cycles to keep up with the pace of markets today.

Esri, Geospatial Powerhouse

Esri has been the dominant provider of GIS (Geographic Information Systems) across all industries for a long time. Though best known (within retail) for providing tools and analytics for retailers to plan out new store placement and real estate strategy, they also have tools to help with assortment planning, supply chain optimization, home delivery, and instore mapping. Esri showed me a screen of theirs, with potential store locations that gave all kinds of analytics about the demographics at different driving distances from the store, logistical considerations for getting trucks in and out, and more (rich, multiple, connected layers of information). Another visualization used customer loyalty program data to show that not everyone shops near where they live, or even where they commute. This retailer discovered that many shoppers will go quite a bit out of their way to visit particular stores.

Esri partners with AccuWeather to provide weather awareness, both for understanding the impact on expected consumer demand from big weather events, as well as the timing for rolling out seasonal products, depending on if the long-term forecast is below, average, or above normal temperature, more or less precipitation than normal, and so forth. By having granular, geospatial-related data, such as demographics, weather, and traffic patterns, they increase the precision of retailers’ assortments and inventory deployment, and the timing of launches, thereby squeezing more productivity out of each store.

SAP, Broad and Deep Retail Portfolio

SAP has a broad portfolio of solutions for retail, including marketing & merchandising (marketing analytics, merchandise and assortment planning, pricing and promotion planning and execution), sourcing & procurement (sourcing, analytics, merchandise buying), private label/PLM, supply chain (analytics, demand and replenishment, logistics and fulfillment), and omnichannel commerce (customer analytics, distributed order management, POS, ecommerce, mobile commerce, personalization). Last year, SAP announced they will invest €2B in IoT by 2020. They are already rolling out IoT capabilities in their Leonardo framework. This framework is highly modular, to enable customers the flexibility to roll out the pieces they need, in the sequence they need, leveraging existing or third party components as needed. Within retail, SAP’s IoT investments will have impacts both in the supply chain and in the store. Their goal is to have a digital/connected supply chain with IoT-enabled stores. In parallel, SAP has been making investments to add more supply chain capabilities to their Ariba sourcing and procurement platform and the Ariba network.

Radial, Technology-based 3PL Serving Mid-Sized Retailers

Radial is a 3PL6 provider of omnichannel services and technology including distributed order management, inventory management, fulfillment and transportation, store fulfillment, dropship, customer service, and payments and fraud solutions. Unlike the other solution providers described above (who are primarily technology-only companies), Radial provides physical logistics services, enabled by their technology stack. Radial owns 27 warehouses totaling more than 13.4 million square feet. Their sweet spot is mid-tier retailers and brands, many of whom don’t have a large IT staff or the scale to run their own distribution centers and transportation operations, or who need to augment their own facilities in markets where they don’t have the densities to justify the investment. They told me speed of implementation is one of their strengths. They have a wizard-based launch tool to get started in a day or two, get test and production environments up within a week, and roll out a full implementation in four to six months. Radial told me one of their differentiators as a 3PL was their ability to scale to handle very high peak season volumes smoothly.

Radial’s tools are designed to allow clients to keep using their own existing systems and easily integrate Radial’s technology to implement (or expand) the missing functions they need. They also allow retailers to innovate on top of the technology Radial provides. For example, a retailer can build their own unique customer experience/UI on top of Radial’s instore pickup (BOPIS, aka click-and-collect) functionality … or integrate that functionality into the retailer’s existing mobile apps for shoppers and store associates.

Radial can also handle all the complexities of payment processing for their clients, including difficult to manage areas such as tax and fraud detection. They reduce or eliminate the payment collection risks for their clients, indemnifying 100% of orders they process; the retailer will never be charged for any fraudulent order. Radial told me they have fewer false positives (where the system believes a valid order is fraudulent) than other payment providers. Thereby, they reject fewer valid orders, maximizing sales for their clients. This kind of precise, risk-free, sales-maximizing fraud management is becoming increasingly important for online sales—as EMV moves into the stores, it has pushed more criminals to go after online retail as an easier target than the store.

Dynamic Times in Retail Supply Chain

What we’ve described here is just a small portion of what is happening in retail supply chain. There are so many innovations. The dramatic ongoing shift to omnichannel, and all of the added complexity it brings, continues unabated. This makes integrated and comprehensive supply chain functionality ever more important for retail.

__________________________________________

1 GT Nexus partners with MercuryGate for those who want tools to manage their own private fleet in the mix. -- Return to article text above

2 Infor refers to their user interface/experience as a ‘beautiful UX’. UI and UX have become a competitive battleground for all enterprise solution providers, as (to varying degrees) they understand that value is only realized to extent that users actually are able to use the software (that’s obvious of course, but engineers tend to forget about us mere mortals when designing their creations). Infor has however gone above and beyond what most of their competitors have done by creating a large in-house creative agency called Hook & Loop focused on helping all Infor’s software product groups imbibe simplicity, usability, and aesthetics into Infor’s various software products. I’ve seen Hook & Loops work; its impressive and justifies Infor’s bragging about having a ‘beautiful UX’. -- Return to article text above

3 External PIMs generally emerged to fill in the gap to handle richer information because ERP systems were not designed with the ability to store and process rich, diverse, structured and unstructured product information. -- Return to article text above

4 Any RFID reader will typically experience hot spots and cold spots, to varying degrees depending on the environment and other factors. Due to reflections, interference, and other interactions with the RF waves emitted by a reader, some locations within its field will have more energy (hot spots) and some less energy (cold spots) compared to the average energy at that distance from the reader. There can be ‘nulls’ which are cold spots where the energy is near zero. If the tag happens to be in a cold spot, it may be read less reliably or not at all. Conversely, if a tag is in a hot spot, it may be read at greater than usual distances. Thus, by varying the location of these hot and cold spots, the chances that all tags will be read is increased. -- Return to article text above

5 TradeStone Software was renamed Bamboo Rose last year. -- Return to article text above

6 3PL = Third Party Logistics Provider -- Return to article text above


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