Author Archive

Ajith Nayar

As a marketer, I’ve keenly watched the retail and consumer trends for a couple decades. But never has it been more exciting than now. Because everything we used to know about shopping is changing, and fast. As consumers, it’s great to be at the center of this technology-led evolution, because it’s unfolding in our everyday lives. I’m happy to share my views on related trends and issues. You’ll see me writing on the digitally empowered consumer, shopper behavior and marketing, consumerization of retail, internet of things, analytics technologies, cloud computing and digital marketing. Presently, I’m Director of Marketing at Manthan, a cloud analytics and big data solutions provider, focused on consumer industries. I’d love to hear and learn from your thoughts and experiences too, so please write to me at Google+ Profile

Augmented Retail Analytics – Supporting Human Intelligence With Superhuman AI Capabilities

The impressive growth of artificial intelligence and machine learning technologies in the past few years, have fueled the beliefs that AI might become capable enough to challenge or replace human intelligence altogether. But that is not going to be a reality anytime soon, at least not in the retail sector. Although, we have seen many innovative use cases, AI/ML still falls short when it comes to making decisions where there are many variables and contexts involved. Rather than waiting for AI/ML technologies to reach the stage of perfection, retailers can take advantage of AI and ML technologies to ‘augment’ human capabilities. Identified as “the next wave of disruption in the data and analytics market”, augmented intelligence technology is all ripe to give sweet business results today.

Forrester defines augmented intelligence as:

“The use of AI to improve a human’s ability to do their job combining machine learning technologies for processing and analyzing data at scale; technologies for automating and orchestrating standard processes; and human input, decision making, and action.”

The idea is simple – the concept of augmented intelligence is not to replace humans, but to support human intelligence, meet their shortcomings, speed-up the repetitive processes, and enable them to take quicker and smarter decisions. Let us look at some of the key advantages of augmented intelligence: 

  • Augmented analytics is better than either AI or human intelligence alone: Rajen Sheth, Senior Director, Google Cloud AI, rightly says that, "AI is most useful when you get it into the hands of a subject-matter expert." The past decade was all about superior data visualization in retail, but still the task of analyzing that data according to the business-context, exploring relationships, scrutinizing the complex variables and combinations, was far from easy. Today, machine-powered data analytics and prescriptions, combined with contextual business knowledge possessed by humans can open doors for superior decision-making.   
  • Augmented analytics optimizes productivity: Till now data consumers were burdened with several repetitive, time-consuming tasks that required little intelligence. With the advent of AI, those tasks can be automated and can be done in almost real-time, thereby increasing human productivity. Augmented analytics can search for and bring to surface vital insights or anomalies in the business processes, analyze all the data in your retail business, study all the in-store surveillance cameras, capture social media information, gather customer insights, correlate it with historical data and churn out insights at a superhuman speed. AI brings the power of speed, scale and efficiency to the hands of every business user.
  • Augmented intelligence can deliver more value today: Businesses working on developing full-blown AI solutions know quite well that it is a time-intensive venture, and although there are significant strides, there is still a long time-to-value. On the other hand, augmented intelligence systems can be easily implemented on top of your current retail technology stack. Rather than working hard on doing away with human intelligence altogether, businesses can work on developing automated systems for data preparation, machine-based algorithms, deep learning, advanced analytics and insight discovery to aid decision-making at all the levels of a business.  
  • Augmented intelligence can democratize retail analytics in the true sense: Augmented intelligence is getting paired with natural language processing. With the rise of such user-friendly interfaces for conversational analytics, anybody within the organization can take advantage of analytics, even if they don’t have any analytical skills. It not only brings analytics to the lowest level of users, but also makes the high-value data scientists more productive. Analytics always faced adoption challenges in retail, but now it can be used by anybody. For instance, with the use of augmented analytics and NLP, any in-store sales person can simply ask, “What are the top things I can do to improve the sales today?” and get a well-analyzed and complete answer from the system.

The award for “Technology In The Supporting Role” goes to augmented intelligence

Augmented analytics brings out the best of both AI and human intelligence. Deep learning and machine learning based algorithms can generate context-based correlations and insights in real-time and prescribe the best paths to take for specific business outcomes. Humans can then take their contextual knowledge and other variables into consideration and arrive at a decision. AI can then be used to execute those decisions at a superhuman speed. Together they can dwarf all human capabilities.

With the convergence of AI and Analytics the retail business can be transformed into an intelligent, self-aware one that can sense business and user context, auto discover problems or opportunities, auto-generate the insights, recommend the best decision or actions, and even execute them. The analytics system becomes smart, context-aware and automated, which takes away the burden of analytics exploration from users and focuses them directly on the business areas that need attention and action.

Manthan is at the forefront of this development

Realizing the disruptive power of these technologies, Manthan Systems has been focusing heavily on AI and machine learning techniques to automate data management, algorithmic processing, and insight generation. Our AI-powered retail analytics platform offers a conversational analytics interface which allows users to talk to the system in natural language. Users can not only run descriptive analytics use cases, but also complex predictive and prescriptive ones. For these reasons, Manthan’s innovative retail analytics solutions have found special mention in categories including, “AI in Retail” and “Algorithmic Retailing”, in Gartner’s Hype Cycle for Retail Technologies, 2018.

Manthan’s augmented analytics solutions leverage the power of AI to offer superior business outcomes. Meet us at NRF 2019, Retail’s Big Show to take a peek into the future of retail.

Customer Centric Algorithmic Merchandising, The Next Game Changer In Retail

The year 2019 would be all about algorithmic retailing as it encompasses the combined power of advanced analytics and artificial intelligence to transform retailing as we know it. Retailers have been already using various advanced analytics technologies till now, but with usable AI coming into the picture along with machine learning algorithms, smart data discovery, context-aware computing, and deep learning technologies – the process of retail decision-making as well as the mindset of retailers are in for a drastic change. One of the areas that are predicted to gain prominence in 2019 is algorithmic merchandising.

According to Gartner’s Hype Cycle for Retail Technologies, 2018, “algorithmic optimization improves and automates the decision-making required to support seven merchandising processes — assortment, space, replenishment and allocation, price, promotion, markdown, and size and pack — to adjust assortments by store, cluster or channel; determine buy quantities; optimize space allocation and planograms; and maximize product allocation and availability.” Predicted to be highly beneficial for retail, algorithmic merchandising will enable retailers to attain higher sales and margins. The technology seamlessly supports complex analytics that customer-centricity requires, enabling smarter decisions at any level of the retail organization.

Let’s dwell on how algorithmic merchandising can have a huge impact during the different stages of seasonal or cyclical retail businesses.

The Pre-Season Stage

During this crucial planning, stage retailers perform a lot of demand forecasting which consequently drive the allocation, production, and sales plans. During this phase analytics systems are used to forecast how products will fare in the market. Retailers have never completely relied on or trusted the data-driven demand forecasting model as they know that there are multiple other variables that affect demand. Even if analytical tools are used, a lot of gut and experience-based micro-decisions are made to generate demand forecasts. For instance, in fashion retail, new products are designed every season based on several factors like style, fabric, color, cuts, patterns, fit, finish, and texture that eventually influence shopping decisions.

In such a scenario AI and machine-powered analytics systems offer greater control over the use of these variables in modeling demand and therefore promise higher accuracy on the demand forecast. AI-augmented analytics takes into consideration the business context, real-time data, external influencers like weather, promotions, social media reviews, the performance of similar products, and a lot more to forecast demand. Accurate demand forecasting in-turn determines how well the inventory gets managed, how the pricing decisions get made, what kind of customer-engagement strategies get implemented, and much more.

The In-Season Stage

During this phase, the retailer’s focus is to execute according to the sales plan and generate maximum profits from the inventory. Inability to closely monitor how products are selling across the stores and controlling their inventory movement to match the plan typically results in out-of-stock situations or excess stocks that have to be heavily marked-down and cleared-off at the end of the season. With thousands of products and styles moving across hundreds of stores, businesses today rely on algorithmic processes. These can detect patterns and bring to light the under-performing products that need a temporary price reduction tactic early on in the season to maximize their revenue during the rest of the season.

These algorithmic anomalies also uncover other opportunities in the business, like changing store merchandising or running behaviorally targeted campaigns to lift sales. In this phase, retailers can leverage algorithmic retailing for inventory optimization, price recommendation, assortment tuning, new product sales optimization, customer-centric offer recommendations, personalized promotions, and a lot more. All these results in optimized sales and profits.

The End of Season Stage

After the season is over, products that have not sold well despite in-season optimization efforts have to be marked down in a specific promotional/clearance window. AI and machine learning techniques are useful in processing answers to questions like – “What percentage of markdown price is ideal for each product to clear-off its inventory?” Or “Which products have most chances of a sale in which store locations?”

Algorithmic markdown analytics continuously optimizes markdown price for the highest return on inventory in each store cluster and store location. It analyses which products need to be discounted, what should be the amount of the discount, the price elasticity, competition from other retailers, ongoing promotions, other marketing techniques, shelf placements, and so on. AI-augmented algorithms can build several decision-trees at the same time on a variety of sub-groups and then combine them all to present a predictive solution. They can also interface with pricing systems to automatically (or through a workflow) implement recommended price changes across the store network, thereby, making it easier to implement the pricing decisions.

Manthan’s Algorithmic Merchandising Solutions

Realizing the disruptive power of these technologies, Manthan has been focusing heavily on AI and machine learning techniques to automate data management, algorithm processing, and insight generation in order to improve analytics consumption across the retail organization. We apply these technologies in retail merchandising applications to automatically sense the merchandising user’s decision context, machine-generate insights, make AI-augmented recommendations, and execute the decisions.

Our AI-powered analytics platform also offers a conversational analytics interface which allows users to talk to the system in the natural language. Users can not only run descriptive analytics uses cases, but also complex predictive and prescriptive ones. For these reasons, Manthan’s innovative retail analytics solutions have found special mention in categories including, “AI in Retail”, “Algorithmic Retailing” and “Algorithmic Merchandise Optimization”, in Gartner’s Hype Cycle for Retail Technologies, 2018.

Manthan’s customer-centric Merchandising Analytics Solution leverages the power of AI to offer superior business outcomes. Meet us at NRF NYC 2019 (National Retail Federation), Retail’s Big Show and Expo to take a peek into the future of retail.

Automation and Augmentation of Retail Data and Analytics | NRF 2019

For over a decade, retailers have been trying to democratize analytics across their business units, departments and people, but it always seemed like a distant dream. With the use of artificial intelligence, it has become possible to smartly blend analytics into the very fabric of your retail business. Retailers can leverage AI-augmented retail data analytics that is context-aware, has all the insights needed for prescribing the right actions, and can even implement your decisions.

As data explodes, the human ability to manually explore data, to find out issues or opportunities, and to device tactics to address them, is becoming a thing of the past. Today’s systems have the capability to sense what is happening within your store locations as well as departments of the entire retail business, develop focused insights about any operation, predict the upcoming challenges or opportunities, prescribe the best way to move forward, and then execute the decision. Such an analytics model, that presents an augmented AI experience to retail staff at any level as well as to the customers, is what is needed in the near future.

It’s time to make a transition to an algorithmic business

Now that most retailers have transformed themselves digitally, the next big step is to transition into an algorithmic business; one that uses AI to make the analytics system more aware and intelligent, and therefore auto-processes many of the tasks humans do. Machine learning based systems can learn what a user typically analyzes, what patterns in data they typically solve, and what business processes or goals they usually drive.

Gartner’s Hype Cycle for Retail Technologies 2018 predicts that, “In the next couple of years significant benefits will emerge as retailers absorb the impact of AI as part of an algorithmic retailing strategy. Algorithmic retailing takes a broader view of use of mathematical algorithms, deep learning, smart data discovery and other advanced analytic capabilities to make major contributions to the effectiveness of the retailers’ decision-making processes.”

With augmented AI solution, users can specify the expected outcomes. For instance, if they would like to improve sales of a specific product line while protecting margins by at least X%. Earlier, they would typically use the enterprise BI and Analytics tools, set data filters as they seem fit, use the dashboards to drill down additional details in order to get the specific insights to support their decision – and doing all this required a certain level of expertise.

But today, the AI-based system can take in your final goal as an input, then automatically processes data, recognize opportunities among the various tactics businesses use to improve sales by modeling the outcome against variables like product, price, availability, and customer preferences to recommend the best action to execute. What’s more; once you decide upon a suitable action, the solution would implement that decision for you. With many business contexts already modeled into the analytics system, it can function as a perpetual prescriptive engine for every process in retail and CPG – and for every individual in the retail organization.

How can it help retailers?

In today’s hyper-complex retail environment, retailers come across a wide range of crossroads every day, where they need to choose which path is perfect for their business. The data and information are all there, but what is still lacking is an ability to understand business context, identify anomalies that need attention, correlate all that data, predict outcomes, and prescribe the best decision or action.

Today’s AI-augmented retail data analytics solutions are autonomous, omnichannel entities that can help retailers execute repetitive, data intensive tasks at speed and scale. It can help retailers with resolving business challenges and taking decisions pertaining to all areas of retail, including: new product forecasting, inventory management, in-store engagement, real-time personalization, assortment optimization, promotions, campaign management, store operations, price markdowns, loyalty management, and a lot more.

This AI-based retail data analytics solution knows what your customers want, knows your manpower needs and inventory levels, can sense winning product combinations, categories, and promotions, and keeps getting smarter over time as more and more decisions are implemented.

How does it work?

There are three vital pillars needed for retailers to build an AI augmented environment that would democratize analytics across their business. These include: machine-driven data management and algorithmic insight generation, AI-augmented analytics process automation, and simplified user-interaction through conversational analytics.

The system must have an AI powered platform that ingests and unifies data and prepares it for analysis based on the context. It should offer a friendly user interface that humanizes interaction through natural language – so the users, just need to talk to it and the intuitive analytical system would start churning recommendations immediately. Once a question gets asked, it understands the context, processes the data and uses advanced algorithms to machine-analyze and prescribe the best action for a business outcome by considering decision contexts and simulating potential impact.

Welcome to the future – A Store That Knows

The stage is all set for AI to make a transformation in the way retailers operate. Algorithmic retailing will enable retailers to optimize sales for specific segments and categories while providing unrivaled operational efficiencies by automating repetitive and complex data-driven tasks. AI could be implemented for optimizing the entire retail business so that it operates at peak efficiency levels.

Gartner’s Hype Cycle for Retail Technologies 2018 estimates that, “up to 50% of retailers have adopted some form of algorithmic optimization application, and expect algorithmic retailing will grow from its current penetration relatively quickly.”

A Store That Knows, is a concept devised by Manthan Systems, and will soon become a widespread reality in retail. Manthan is at the leading edge of this evolution and is today enabling the underlying technology capabilities for several retailers. Manthan is pioneering innovations in analytics by using the power of AI to enhance decision-making across various dimensions in the technology stack, including data management, insight generation and analytics consumption.

Gartner’s 2018 Hype Cycle for Retail Technologies. What you need to know.

Gartner’s Hype Cycle for Retail Technologies 2018 is out, with trends for technology leaders. This year, the Hype Cycle has identified democratized AI as a key trend – products and solutions that “blur the lines between human and machine”.

We are pleased that Manthan has found mention in 5 categories in the 2018 Hype Cycle.

  1. AI in Retail (Rating: Transformational)
  2. Algorithmic Retailing (Transformational)
  3. Cognitive Expert Advisors (High)
  4. Customer-Centric Merchandising and Marketing (High)
  5. Algorithmic Merchandise Optimization (High)

These ratings are a testament of invention and innovation in Artificial Intelligence, Advanced Analytics, and Cloud at Manthan. And a vision, to create context-aware, AI-powered analytics products that bring the true power of AI to every role in your business.

Manthan’s efforts have been focussed on bringing analytics-driven decision-making to the real user. To design sophisticated analytics products in a manner that everyone can use them. Which we believe, is critical to the businesses dealing with the real-time, connected customer.

We architected our products by re-interpreting 4 critical areas – Analytics Consumption, Algorithmic Processing, Solution Engineering and Data Management.

1. Re-interpreting Analytics Consumption

Our primary goal, as Gartner puts it, is to “blur the lines between human and machine”. This is what led to the creation of Maya – the world’s first AI-powered conversational agent for business analytics.

With this, analytics becomes easy to consume. In a simple, conversational format that remembers context and processes information in real time, based on the user’s intent and his flow of analysis. Maya is integrated with mobile, desktop and personal assistant devices and can be invoked anytime. It offers both general and role-based models for analytics consumers.

Maya makes use of machine learning, deep learning, advanced analytics, cloud computing, natural language processing (NLP) and generation (NLG), intent analysis and context-aware computing. But all you need to do is ask.

2. Re-interpreting Algorithmic Processing

Today’s digital business is generating millions of data points, across sources, every day. Taking traditional hierarchical approaches to analyze data is just not physically viable. Your solution should be able to conduct auto-discoveries, root-cause analyses, and auto-recommend best outcomes, based on simulations.

Manthan’s analytics platform algorithmically processes anomalies, outliers, and exceptions to recommend actions that can achieve clear, smart goals.

3. Re-interpreting Solution Engineering

As analytics processing becomes complex, analytics experience needs to become intuitive. Solutions need to be designed for the real decision makers and should embed real business contexts.

Manthan’s solutions are designed to bring together advanced analytics and algorithmic capabilities for specific use cases across retail. Our solutions also come with the ability to scale and to incorporate new use cases.

4. Re-interpreting Data Management

The digital business generates much more data than the traditional one, with new data sources emerging all the time. While some of this data drives repeatable use cases built around standard business processes, you also cannot lose sight of new use cases that elevate the customer experience.

Manthan offers a full-featured data management platform that can deliver production-grade enterprise analytics in a governed model. But at the same time, it also drives rapid experimentation and innovation with an architecture that can ingest and mash new data sources at scale in a data lake architecture. This supports on-demand data-processing, giving businesses real-time decision-making abilities.

We have what you need.

We have re-interpreted analytics delivery with AI. And an elastic cloud infrastructure and server-less computing capabilities provide the necessary performance and agility you need from a new age data a platform that can deliver real-time decision-making.

Tomorrow’s technology does not require a screen in front of you. Or you in front of a screen. It will walk with you, whispering real-time recommendations in your ear, based on intense, granular analysis.

That’s truly democratized AI. And that’s what we have for you, today.

Fair Price & Importance of Customer Location

The location has always been an important factor in retail. It is gaining an all new meaning with retailers detecting customer location inside the store and offering range of services to make their trip more enjoyable and engaging. Largest supermarket in Singapore is working on an innovative indoor positioning system which can detect customer location within a store. Indoor positioning will enable the retailer to enhance the shopping experience with wayfinding capabilities and in-store promotions delivered in real time on customer mobile phones.  Customers can receive optimized in-store directions to products saved in their shopping list, receive contextual & location-based personalized notifications making customers aware of best details exclusively available for them. For long, online retailers have been at an advantage of knowing their customer’s journey and being able to offer personalized experience along their path to purchase.  In-store positioning promises to bring Brick and mortar retailers at par with online retailers. The technology seamlessly integrates mobile apps and physical stores to deliver a personalized experience to customers currently restricted to only the online channels. For a detailed understanding of this deployment, download this case study For a quick overview download this Datasheet If you are interested in a 15 min. discovery call with Manthan, do write to us at

Whole Foods- Grocerant

“It’s 4 PM: your customers are just beginning to think about what’s for dinner. 81% of American consumers are unsure about what’s for dinner,” says Grocerant researcher Steven Johnson. This is driving the evolution of the Grocerant Store experience, blurring the lines between restaurants and grocery shopping.

Why is this important from a grocer’s point of view? Research shows that there is a strong correlation between brand perception and how grocers like Whole Foods address the essential need for food by various products and services (therefore creating new experiences). Providing consistent experience around eating healthy, eating fresh, using organic ingredients etc. extends a brand’s connect with a consumer segment. By widening the definition of the need category you are serving, more opportunities to improve customer experience opens up.

But all of this really starts with deep insights from your data and an ability to transform them into customer experiences. You need an understanding of customer behavior that is much deeper than you probably have right now, to develop innovative customer-centric strategies.  You can use analytics to understand how to shape and develop customer needs, attitudes and preferences to food, health, life situations, and influence behavior and motivation to shape new and rewarding experiences. And with micro-targeting and personalized engagement capabilities you can shape customer decisions at any stage in their journey.

NPD’s foodservice market research found that in-store dining and take-out prepared foods from grocers has grown 30% over the past eight years, accounting for $10 billion of consumer spending in 2015. Millennials are a growing segment of ultra-conscious grocerant shoppers, and having your finger on the pulse of this segment means you really need to unlock more insights on what drives their choices.

Fashion and apparel brands have now taken a bite out of this trend. Retailers like Tommy Bahama, Nordstrom, Macy’s, Brooks Brothers all have or are planning restaurants selling freshly prepared food in-store.

When you put customer experiences at the center of your strategy, be prepared to experiment, innovate and disrupt your operating model, and get the right customer analytics and engagement technologies to enable your strategies. Getting it right could create significant and lasting value for your brand.

If you are looking for ideas to improve your store experience, Manthan can help you. Manthan’s Retail Analytics solutions are built exclusively for retail and tailored to fit unique retail decision-making needs. With the experience of serving 100s of customers across 22 countries, across Fashion & Apparel, Food & Grocery, Specialty and Mass Merchandise, you are bound to get what you are looking for.

For a quick overview download this data sheet:

If you are interested in a 15 min. discovery call with Manthan, do write to us at

Optimize Supplier Collaboration

Super six checklist to Optimize Supplier Collaboration

Supplier Collaboration Excellence: Achieving Walmart Quality

Decision makers at Walmart have a simple test to check the value of each innovation the retail giant considers. Each new process or decision at Walmart goes through three key questions:
  • Will it help to reduce price?
  • Will it improve customer service?
  • Will it help improve supplier collaboration?
It is no secret that the retail behemoth from Bentonville has been able to stand by its founder Sam Walton’s initial promise of quality products at everyday low prices thanks to smart inventory management, superior communication with suppliers, visibility across the entire Walmart supply chain and excellent supplier collaboration strategies on their powerful retail-supplier platform. Today, the Walmart supply chain consisting of over 2,400 stores and 100 distribution centers connect seamlessly with its 14,000 strong network of global suppliers over a technology platform that enables end-to-end supply chain visibility and superior decision support. The platform also gives Walmart’s suppliers access to its entire database with up-to-date, store-by-store information on sales and inventory of their products, decision support, what-if scenarios, sell through and pricing.

Supplier Collaboration: The Super Six Check-list

When it comes to Supplier Collaboration every retailer aims for Walmart quality. But how many are successful in their collaboration efforts? Why do many retailers lose significant time and efforts on collaboration projects and technologies that meet with failure? Despite investments in top of the line technologies, why do so few retailers establish effective supply chain visibility? A key reason behind this is the narrow view of supplier collaboration that equates it to a set of collaborative and event-based practices as against the building of long term, collaborative relationships with suppliers. Here is a quick super six checklist which will help you identify if your collaboration initiatives are on track with the best in the business.

1. Are delayed product additions and vendor listings driving you up the wall?

Over 20,000 newly launched products are added to the Mintel Global New Products Database every month. This roughly translates to an average addition of over 650 products a day and hundreds of new vendors registered each month. For many retailers, this means undue delays, complex product assortments, frustrations and often chaos in the supply chain. But managing dynamic product assortments, new product additions and vendor listings does not necessarily have to be such a painful process. A comprehensive supplier portal with collaborative capabilities enables you to launch products and enroll new vendors electronically, and handle expanding product assortments at rapid turnaround. These let suppliers submit information and images of new products with high degree of accuracy to be launched while allowing retailers to on-board new vendors in a speedy, structured process. Merchandising teams at the retailer’s end can quickly evaluate new vendor listings and new product proposals, shorten review cycles, quickly expand product assortments and fast-track decision-making.

2. Do your suppliers support you through every stage of killer promotions?

The retail industry is rife with stories of retailers planning elaborate promotions and festive offers. But many of these end up getting a lukewarm response in the market because of poor supplier support. A truly collaborative culture will foster joint promotion planning among retailers and suppliers, enabled by sharing of product sales and inventory data under highly safe and secure environments resulting in timely markdowns and the appropriate pricing strategies. With access to timely information on marketing plans and promotion schemes, suppliers are better equipped to support you through your killer promotions.

3. Do your suppliers share insights to help boost your sales as much as theirs?

Your suppliers have their finger on the pulse of the industry and are in the know about market trends, customer preferences, moves by competition and significant shifts in the market. Effective supplier collaboration works as a two-way street for improvement with suppliers being as much of a contributor of information to your business. They bring insights and customer data at their disposal to help you increase sales, improve product assortment decisions and boost foot falls to your stores.

4. Do your merchandise, sales and store operations teams have supply chain visibility?

In today’s retail supply chains, trying to get correct information on sales, product assortments, or shipments can be as baffling as groping in the dark. A merchandise manager trying to get a grip on where a consignment is in the delivery chain, may end up having to make a series of back and forth calls, e-mails and faxes. Suppliers trying to gain information on product sales and stock positions may be an equally daunting task. The Walmart supply chain has set a bench mark with regard to retail supply chain visibility providing its network of suppliers as well as distributors and logistics providers with real time access to data on product assortments, stocks, sales and shipments. Effective supplier collaboration offers superior supply chain visibility with safe and secure access to product master data, purchase orders, invoices, data on goods Available to Promise (ATP), Advanced Shipping Notices (ASN) as well as information on product safety, traceability and recalls all on an easy-to-use single portal accessible at the click of a mouse to all stakeholders.

5. Are your suppliers using funds in smart ways that boost their business as well as yours?

Getting an early buy-in and support from suppliers into your business and marketing cycles and product assortment decisions, is a very critical aspect of any retailer-supplier collaboration and is where most retailers lag behind. The most powerful and effective way of doing this is to involve suppliers at a pervasive level of planning, demand forecasting and marketing for optimized results at both sides. With their close involvement in your processes, you can get early commitment of funds from suppliers towards promotions, combination offers, gondola hire and dedicated staff deployments at your stores – with a view to improving your sales, customer service and margins as much as their own.

6. Have you put in place vendor scorecards to help suppliers assess whether they are performing to plan?

Have your collaboration efforts been primarily focused on your business with total disregard for your suppliers growth? If yes, perhaps you need to relook your strategy. Advanced supplier collaboration techniques suggest embracing comprehensive and in-depth vendor score-cards. This scorecard should capture critical metrics and Key Performance Indicators (KPIs) of all suppliers, helping to track, measure and monitor supplier performance and focus on areas for improvement. Based on these score cards, you can make comparisons across similar supplier community and offer each supplier recommendations on improving refill rate, margin percentage, sales volumes and delivery schedules as well as infrastructure and process improvements.

Advanced Supplier Collaboration Portals:

Retail chains and large format department stores focused on building true collaboration can rely on the super six check-list to ensure that their collaboration efforts are on track with the best in the business and achieve Walmart quality. Integrated web-based portals with comprehensive supplier collaborative capabilities facilitate seamless collaboration, supply chain visibility and real-time information exchange between retailers, suppliers and all other touch-points in the retail supply chain.  
Supplier / Vendor Scorecards

Strategic supplier partnership through Supplier Scorecards

As you swing your SUV out into the highway for a long road trip, it is customary to check your car’s dashboard for critical indicators – fuel, speed, airbag and oil pressure  details you will need to control for a smooth journey. The dashboard not only alerts you of things that go wrong during the drive, it also offers early-warning signals such as check engine or fasten seat-belt to draw your attention to potential problems. Vendor scorecards, much like your car dashboard, provide you with critical information on your suppliers businesses and alert you to problems in your supply chain. They assimilate information from different parts of your retail enterprise and present it to you in a compact and easy-to-understand manner, highlighting where your supply chain is doing things right and where it is falling short. Designed comprehensively and used effectively, vendor scorecards can help retailers measure, monitor and manage supplier performance, foster long term supplier relationships and align supply chains to business goals.  

Supplier Performance critical for achieving Supply Chain Excellence

Until recently, retail industry practitioners regarded supply chain efficiencies as an effective means to control costs in the retail business. It helped retailers to reduce operating costs, manage lean and efficient value chains, reduce waste, optimize resources, reduce inventory and improve margins. However, today more and more retailers are looking at supply chains as a means to build competitive advantage. This makes it necessary for them to foster strategic, long-term partnerships with suppliers. Retailers like Seven-Eleven,, Dell, Zara, Marks and Spencer, Walmart and Li & Fung have been leveraging their supply chains to support business expansion, increase profits, enhance customer service & loyalty and improve market share, revenues and shareholder value. Strong buyer-supplier co-ordination, pervasive collaboration at every level in the supply chain and Joint Business Plans are an essential requirement to building supply chains that reinforce your edge in the market.

Vendor Scorecards  An Essential Tool-kit for Supplier Performance Management

Most retail businesses, until recently, relied on retailer compliance requirements to monitor and manage supplier operations and supplier performance. Suppliers who fail to adhere to these compliance requirements or deviate from them are levied a deduction or chargeback on their invoice by way of a penalty for non-compliance. In some instances, vendors may be fined upward of 10% of the purchase order’s value. As is evident, such an approach may track how well a supplier is adhering to short-term, tactical compliance requirements but fail to provides a complete picture of supplier performance nor foster collaborative supplier relationships. Scorecards serve as a performance measurement tool that summarizes a supplier’s Key Performance Indicators (KPIs) and metrics and provides a dashboard of supplier performance across critical business parameters. Scorecards help to transfer retailer-supplier relations to a more structured and systematic ground, offering you concrete metrics to base supplier reviews on, develop Joint Business Plans and collaborate for mutual long-term benefit.

Vendor Scorecards in the Retail Industry

Vendor score-cards are a relatively new concept in the retail industry. According to a Gartner report, not all retailers have scorecards, and not all scorecards cross all categories or supply chain processes. Only 55% of volume flowing from a supplier to a retailer is measured by a scorecard. Likewise, only 20% of scorecards shared by retailers with suppliers get past the CP sales account teams to drive change in supply chain behavior. Early versions of supplier scorecards were largely focused on operational metrics and did not scale up to meet the needs of Joint Business Planning or driving long-term supply chain goals and partnerships. Commonly vendor scorecards in the retail industry track year-on-year data for sales, fill rate, in stock performance and days and weeks of inventory on hand. They present a snapshot of this year’s (TY) data against last year’s (LY) and actuals against planned figures. Some other traditional metrics include margins, inventory turns, and gross margin return on investment (GMROI). Scorecards also track qualitative metrics such as whether the goods delivered matched quality standards, orders are complete, on time, if there are any damaged or un-saleable goods and whether ASN labeling, invoice, receipts and documentation is complete and accurate. A close look will reveal that most of these metrics are focused on short-term, tactical aspects of the business and are designed from a retailer-driven perspective. The end goal of these scorecards does not go beyond vendor compliance and definitely do not strive for collaborative culture in the supply chain.

Vendor score cards: Building Strategic Supplier Partnerships

Designed and used appropriately, scorecards can be the glue that drives and binds Joint Business Plans – an essential requirement for strategic and collaborative supplier partnerships. They can form the basis for managing closer ties between suppliers and retailers; support intensive co-ordination and foster superior collaboration at every level in the supply chain. For this, vendor scorecards have to be designed with a shared focus on retailer and supplier goals – the retail shopper. Retailers need to recognize that traditional metrics must be supplemented with a range of indicators focused on customer centered processes and in alignment with strategic supplier relations.
  • Sales
  • Fill rates
  • On-time delivery
  • Quality of goods and services
  • Service capability and performance
  • Price-competitiveness
  • Compliance with contract terms
  • Response
  • Lead time
  • Technical capability
  • Environmental, health, and safety performance
Vendor scorecards should include partnership metrics that track the supplier’s alignment with the retailer’s business goals and the suppliers level of cost-competitiveness and innovation. These metrics should also focus on joint supplier-retailer initiatives and improvement, collaborative education and training, and improvements to technology. A range of customer-facing metrics can help to measure the impact of the supplier’s performance on the retailer’s customers and monitor the effect of processes and operations keeping the shopper in the centre-stage. These include metrics such as perfect order, stock outs, shelf-level fill rates and customer experience index. Scorecards need to also focus on merchandising and supply chain-related metrics that track the supplier’s responsiveness and support for promotions and seasonal events. Finally, no score card is complete with the standard compliance metrics that track a supplier’s capability to meet performance criteria, support retailer operations and adheres to agreements and guidelines. An effective scorecard will lead to more strategic supplier relationships, and it will reinforce those processes and attributes that are most important for the retailer. Retailers increasingly are recognizing that comprehensive scorecards can provide a clear competitive advantage in building the right kind of supply chains.  
Grocery Retail Strategies

Top 3 Grocery Retail Strategies using Better Shopper Insights

Advanced analytics helps to track, understand and leverage every stage in the shopping path on grocery channels A regular shopper at Sainsbury receives coupons on her favorite products and most purchased items – from spaghetti sauce to cheddar cheese – at the checkout. These personalized coupons, offered while swiping her Nectar loyalty card at the checkout, is part of Sainsbury’s strategy to engage better with its best customers and reward them for their loyalty. And to keep them coming back for more. ‘Coupon at Till’, the innovative scheme, may appear simple enough on the surface but involves data integration, data mining of volumes of Sainsbury’s customer data and advanced analytics at the backend to match the right offers with the right customers – all in real time.

Advanced Analytics for Customer Insight – Recipe of Winning Grocers

With fluctuating commodity prices, razor thin margins, and an array of big-box, non-grocery players entering the fray with in-store food brands and customized grocery lines, food and grocery retail industry has become a tough business. As competition for the food dollar intensifies, advanced analytics is proving to be an essential tool in the grocery retailers’ strategy to sustain business and build competitive advantage. More recently, advanced customer analytics tools are being implemented by a range of large to mid-sized grocery chains and supermarkets to understand and analyze shopper behavior effectively and to apply these insights towards ‘shopper marketing’ – where the focus is on influencing shoppers’ decisions at every stage in the grocery shopping path. Here are three top analytics-driven strategies that grocers are adopting for business benefit:

Strategy number one – Customer-driven Offers and Promotions

The verdict is out on mass-mailed marketing campaigns: they are no longer working. The industry is recognizing that shoppers receiving frequent mailers from the same supermarkets are more likely to roll their shopping carts over to the competition. Kroger’s Customer First strategy believes in tying customer information with smart coupon offers. Kroger customers get in their inbox, mailers which are of relevance and interest to them. The idea is to offer them ‘what they actually buy in the store, and not trying to sell things they have never bought.’ To action this, Kroger applies advanced analytics tools to volumes of customer data and tailors offers to customers based on their actual shopping behavior and history. Today regular customers at Kroger redeem nearly half the coupons the retailer sends out, which is far higher than the average trend of 1 % to 3 % redemption. Leading retailer Target applied advanced customer analytics to perfect a “pregnancy prediction” model. By correlating customers’ shopping patterns with their market basket, the retailer was able to successfully identify customers that who were pregnant. Acting on this data, the company offered coupons and promotions targeted at customers at various stages of their pregnancy. Between 2002 and 2010, Target’s sales in the Mother and Baby product categories went up and revenues grew from $44 billion to $67 billion.

Strategy number two – Improving Aisle Layouts and Planograms with Market Basket Analysis

Market Basket Analysis, an advanced analytical tool, helps grocers to discover hidden affinities between products and identify the ‘occasion’ that drives a shopping trip. Did a shopper come in to pick a few items that he ran out of (convenience shopping) or is he stocking up for the month (replenishment shopping)? Is he throwing a party, going on a trip or cooking an exotic meal? These insights are being used by grocers towards designing better customer experiences that meet the specific needs of various such shopper occasions. For instance, ‘get in and out’ zones at the front of the supermarket, express, and self-service check-out and ‘drive through’ facilities enable quick pick-up and billing of specific items by convenience shoppers. On the other hand, many supermarkets are stocking additional product lines – stationary, CDs and pharmacy – so that they can avoid replenishment shoppers from ‘splitting’ their baskets across other stores and become the kind of one-stop destination that these shoppers are on the lookout for.

Strategy three – Shelf Life Management and Replenishment Planning

With in-house departments for dairy, meat, fresh produce, seafood, bakery and deli, no other retail segment is in need of hourly inventory planning as much as food and grocery. A closer understanding of shopper behavior and demand patterns through the use of advanced customer analytics and forecasting tools helps UK Grocers, Waitrose, maintain optimized stocks, achieve faster turn-rates and reduce wastage on their perishables. Analytics tools help grocery retailers monitor and analyze demand signals in these products including activity cycles through the day, peak hours for sales and store traffic, local events, seasonality, holidays, social trends and local buying habits. The latest retail analytics solutions drill down into region-wise as well as store and SKU-level details for all this data and offer grocers actionable decision-support to leverage this data.

Grocery Analytics Solutions: Getting to Know your Shopper Better

In a rapidly changing grocery retail industry landscape, advanced analytics for grocers offer unique value, being built on a retail data model with a deep understanding of the nuances and processes of the food and grocery retail industry business. The actionable analytical capabilities in these solutions help every level in grocery operation from store associates to the CEO understand which products are most price-elastic, how customers react to combo-offers and whether their promotions performed to plan. From increasing shopper spend per visit to creating spot-on cross-selling campaigns, actionable intelligence can empower leading grocery retailers to build competitive advantage and grow their market share.  
Suppliers downstream data

Top 3 insights that suppliers gain from downstream data

Coffee’s good for you one day; it’s chamomile tea, the next! Shoppers who wanted zero trans-fat foods and no-fat cookies on a shopping trip, come back looking for old-fashioned butter cookies and chocolate wafers, the next time. From baked snacks to herbal teas, vegan to organic, there’s no telling what the shopper in the aisle will demand.

Rapidly changing customer tastes are just one of the factors keeping CPG manufacturers on their toes. Operating in a fiercely competitive marketplace, they are fighting for scarce shelf-space and customer wallet share. They need to respond quickly to competitor promotions and pricing, comply with stringent retailer demands
– all the while striving to deliver new product innovations that stand out amidst the clutter.

The new mantra for supplier collaboration: Retail data-sharing

After initial reservations about it, retailers have now been providing their top suppliers with their POS, sales and stock data as a means of initiating supplier collaboration with them. The tangible benefits of such shared downstream data insights became evident with Walmart’s success – its Retail Link downstream data sharing platform is the pioneer in this area. Soon others like Carrefour, Metro, Tesco and Sainsbury followed suit, with proprietary extranets that passed on retailer data to their suppliers – laying down a strong foundation for supplier collaboration.

However, until recently suppliers themselves were not quite sure how this retailer data could benefit them. That is changing now. Applying the latest analytics tools and techniques, they are gaining invaluable insights and retailer-supplier collaboration from downstream data to drive their business. Many CPG manufacturers like P&G, Nestle and Unilever are using retailer data to forge a stronger foothold in retail outlets, consolidate market-share and increase margins. In many cases, the insights gained are enabling suppliers to adopt a consultative role with retailers, offering them valuable inputs on category management, as well as assortment, planogram and promotion decisions.

Perfecting in-store fill rates

Demand driven replenishment

In the traditional approach to replenishment, retailers simply provided suppliers with information on warehouse withdrawals (products pulled from Distribution Centres (DCs) to stores). There was no visibility offered into retail store-level data or current and expected consumer demand.

As a result, suppliers were largely reacting to customer orders. This often led to a mismatch in store fill rates or excess inventory at Distribution Centres (DCs) – not a great situation, either way, for manufacturers.

Del Monte’s replenishment model is an exception to this. The company made a go at shelf-level collaboration with its retail customers by drilling into their store level POS, inventory and sales data. The $3.4 billion producer of branded food and pet products relies on current store level demand data and enterprise volume forecasts to fine-tune its replenishment from factory to retailer DCs. The strategy paid off. Del Monte achieved 99% improvement in store fill rates, reduced safety stock and freed a substantial part of its working capital. It offers unmatched service to retail customers and topped the charts as the best supply chain vendor according to many industry bodies.

Soft drinks major PepsiCo has moved to a consumer driven supply chain with Food Lion. The soft drinks major is proactively fine-tuning its replenishment based on current and expected consumer demand, inventory and sales at Food Lion stores as opposed to simply reacting to goods pulled from Food Lion warehouses.

Leaner, fresher stocks – that never run out

Better inventory management

Managing the right inventory volumes at different levels of the supply chain is a key concern for all CPG manufacturers and suppliers. If they do not have visibility into retailer inventory data at stores, Distribution Centres and ‘in transit’ they stand the risk of Out of Stock (OOS) situations. More often, however, they are over cautious and end up with excess ‘safety stock’, resulting in high labour costs, warehouse expenses and working capital blocked in high inventory.

Harnessing granular retailer data on inventory can help suppliers and manufacturers to map stores to DCs, match deployed stocks to shelf gaps and reduce safety stocks. In segments such as Food & Beverages and Grocery retail, where shelf-life is critical to the quality and saleability of products, this is an even greater need.

Kellogg’s uses downstream data from Food Lion’s ‘Vendor Pulse’ platform, including item stratification and inventory tracking reports, to streamline stocks at every point in its supply chain. By integrating the stock data at Food Lion into its inventory processes, Kellogg’s reduced excess inventory at its Distribution Centres by 17% and cut inventory older than 60 days from 15% to 6% in three months.

Cross-selling through the market basket

Effective Category Management

Manufacturers with a wide array of product categories and brands have to fight for scarce shelf space and facings. Needless to say, even the best brands may end up losing the fight for customers’ share of mind – or to their shopping baskets.

Leading food manufacturer Kraft solves this challenge by tying in retailer data inputs to its in-store marketing strategies.

Some of Kraft’s successful campaigns based on retailer basket views include pairing its Philadelphia Cooking Creme with a retailer’s meat department promotions and installing Parmesan Cheese racks on frozen-pizza doors at another retailer’s outlets. Kraft also used shopper trends and market basket data to innovatively display its salad dressing, pre-cooked bacon and cheeses alongside retailers’ fresh produce, making it the first choice for shoppers picking up ingredients for salads.

Going by total basket size (revenue, margin and number of items), store traffic, share of wallet and shopper loyalty trends, Kraft has been extremely successful in planogram and category management, earning it top honours in Kantar Retail’s Category Leadership Benchmarking Study.

More retailers are demanding proactive supplier collaboration through insights from store-item-day level data shared on a real time basis. Suppliers are also seeing vast improvements across their business processes including planogram management, joint business plans, collaborative product development, promotion planning, private label product management, traceability and product recall functions as well as supplier performance reviews and service levels in stores.

Mining retailer’s downstream data: Analytics for better insights

Retailer data is of little use to suppliers, unless put through the analytics scanner. Many smaller suppliers, particularly in grocery and mass merchandise segments, lack the expertise and resources for this level of analytics to be performed on POS data. To tackle this, large retailers share an array of ready reports and dashboards with them – focused on the main KPIs and metrics relevant to suppliers. On the flip side, many large CPG manufacturers are becoming ‘data czars’ – investing time and resources into building analytics capabilities. They are moving beyond the role of mere ‘order takers’ to becoming true ‘business partners’, advising their retail counterparts on a wide range of business decisions for mutual benefit.

Are you using your data mines to start a dialogue with your suppliers and drive supplier collaboration? Do they actively seek insights and share strategies based on the downstream data you share with them? 

Retailer-supplier cross-channel data sharing

Top 3 Benefits of Retailer-Supplier Cross-Channel Data Sharing

While collecting and mining data for useful insights is an important part of the retail environment today, many retailers and suppliers have discovered the advantages of sharing this data with each other. We have previously discussed the benefits of retailers sharing downstream data and predictive analytics with suppliers – but what about cross-channel data? Traditionally, shopping revolved around the concept of customers visiting brick-and-mortar stores. Today, shoppers no longer restrict themselves to shopping through a single channel – it’s all about developing the shopping experience over different online, offline and mobile channels resulting in a truly cross-channel shopping environment. IDC data shows that multi-channel customers spend 15% to 30% more than single-channel customers, and additional data reveals that omnichannel customers spend 15% to 30% above multi-channel customers. So it’s no surprise that retailers and suppliers are “laser-focused” on understanding customers better to improve the shopping experience – and sharing cross-channel data is the key to revving up the next-gen retail engine!
Today, shoppers no longer restrict themselves to shopping through a single channel – it’s all about developing the shopping experience over different online, offline and mobile channels resulting in a truly cross-channel shopping environment
Cross-channel data is deeply connected to the concept of building a loyal customer base. Customers don’t consciously think about channels when they shop – more often than not, the customer is more interested in the brand than the channel. This signals that cross-channel shopping is the means by which retailers need to enhance the overall shopping experience, thereby using it to increase loyalty and brand value. So are retailers and suppliers sharing cross-channel and loyalty data? What benefits can they get out of this collaboration?

Improved and optimized allocation of resources

The CGT/RIS Retailer Supplier Shared Data Study 2012 reveals that more than one-third of the retailers surveyed have reported significant ROI from sharing data and that many are now investing in more sophisticated technology to enable this sharing. Such collaboration is leading to advanced supply-chain solutions that consolidate and leverage cross-channel data. This results in both retailers and suppliers having better visibility into the different channels to optimize the allocation of resources between them, all the while working towards the consumer experience while streamlining operations, optimizing inventory and increasing profitability. This differs from traditional supply-chain mechanisms which work independently and in a siloed fashion, providing no ‘big picture’ to connect activities like merchandising, assortment planning, pricing, replenishment, sourcing and logistics through a unified plan of action across different channels.
It might make strategic sense for a supplier or manufacturer to use the shared cross channel data and play a larger role in shipping their products directly to consumers in small packages instead of sending truckloads to a regional retail distribution center
For example, the logistics area of the supply chain can be revolutionized by retailers sharing cross-channel data with suppliers especially because it makes up three-quarters of overall supply chain costs. Therefore, it might make strategic sense for a supplier or manufacturer to use the shared cross channel data and play a larger role in shipping their products directly to consumers in small packages instead of sending truckloads to a regional retail distribution center — and then making retailers responsible for delivering products across all their channels, including in-store and at-home deliveries.

Smoother running loyalty programs

A large part of running a successful loyalty program revolves around a retailer’s ability to accelerate customer engagement and experience using insights from their loyalty program. Suppliers also stand to gain when retailers share this loyalty data with them, especially when consumers also expect loyalty offers to be consistent across all channels — right from an offer creation, delivery, and redemption standpoint. However, the CGT/RIS Retailer Supplier Shared Data Study 2012 reveals that loyalty card data sharing is down again this year, as only 27% of suppliers reported receiving it from retailers. What is causing this decline? Retailers are hesitant about releasing what they consider proprietary data.
Suppliers also stand to gain when retailers share this loyalty data with them, especially when consumers also expect loyalty offers to be consistent across all channels — right from an offer creation, delivery, and redemption standpoint.
According to Aberdeen’s January 2012 Omni-Channel Retail Experience, 42% of survey respondents stated that a “top omnichannel pressure is the consumer expectation of a similar experience regardless of channel. Inconsistent branding or disjointed offers can lead to consumer confusion (about the brand or offer), disenchantment, and eventually, desertion”. The solution to this is for retailers to adopt a cross-channel, centralized loyalty platform which encompasses consumer insights, offer creation, offer redemption, and performance metrics reporting. This can aggregate information across all channels of operation, with a single view of the customer and their purchase and interaction behavior. These insights can then be used to maintain tailored loyalty offers depending on the type of customer the retailer is targeting: lapsed, high spending threshold, high discount percent, etc So how do suppliers stand to benefit from this shared, cross-channel loyalty data? The example of Sainsbury’s offering its suppliers free access to loyalty card data and analysis shows that it recognizes that suppliers gain greater insight into customer requirements. Sainsbury’s uses data from its “Nectar” loyalty card program to provide suppliers in-depth analysis of customer purchasing trends. “By providing suppliers with this information, we enable them to develop products that put the customer even closer to the heart of our business and theirs,” said Andrew Ground, Sainsbury’s customer marketing director.  
Supplier Collaboration For C-Stores

Fantastic Four Benefits From Supplier Collaboration For C-Stores

  “United we stand, divided we fall”- this Aesopian dictum perhaps encapsulates best the need for a seamless collaboration between convenience stores and their suppliers. The new breed of picky and quality-conscious customers is putting the screws on C-stores to think beyond homespun partnerships with suppliers. Only an intensive collaboration has the power to orchestrate symphony across the supply chain and achieve sustainable competitive advantage. To neutralize the possibility of fraying around the edges in today’s greatly fragmented market environment, convenience stores have to be shopper-centric in terms of product offerings. Collaboration surpassing mere tactical arrangements is not a short-lived affair, rather an agreeably long-haul journey that eventually jack-up revenues and shrink expenses through shipshape processes and better designs. Though synchronization of mature level pops-up certain pesky issues about operations, data security, and logistics, nevertheless benefits of collaboration fight-off the chill winds of the underlying hiccups. Here are the fantastic four benefits:
  • Focused and customized merchandising, pricing and promotions: Adhering to a comprehensive collaboration model enables both the stakeholders of the game to exchange business critical insights in real-time. Twenty-four seven access to point-of-sale data is a rich seam to mine; it has the potential to sidestep stock outs and deferred procurement cycles. Monetization opportunities increase as collaboration ensures leaner planogram, accurate demand projection, and brisk stock replenishment. Furthermore, the practice of standardized promotions and aimless pricing can be done away with. Instead, more focused and customized pricing and discounting decisions can be hammered out.
  • Faster invoicing and payments: Members of the C-store supply chain have to deal with a deluge of invoices at any given point of time, so maintaining probity of the invoice raising process is pivotal. Preparation of faulty invoices put unnecessary financial burden on convenience stores as well as vendors. Automated invoice processing system installed as a result of deep collaboration optimizes costs and saves time by filtering-out any invalid entries.
  • Product lifecycle management: Convenience stores are known for scant staff. When there are too many activities to be handled by a couple of people, the products might get sidelined. Like animate beings, products visible on the shelves of C-stores pass through four distinct phases; development, growth, maturity, and decline. With effective supplier collaboration, vendors can keep their eyes on each stage because that will have reassuring impact on sales. Suppliers can act faster to the changes and customer preferences.
  • Common goals: The biggest boon of collaboration is that it is a tool to tap complementary expertise for developing a mutually profitable business proposition. An integrated analytics platform grounded on a perfectly aligned outlook on customers drastically reduces vendor and item costs. By forging uninterrupted alliance throughout the vendor network C-stores attune their organizational goals to suppliers’ objectives.
A well-coordinated and agile supply chain is the backbone of convenience store operations. Building a strong interdependent relationship with suppliers opens up a full basket of opportunities. Expectations of sophisticated end-users can be satisfactorily addressed by joint innovations and responsiveness. C-stores that still downplay the importance of effective collaboration are living in ivory towers and jeopardizing their own existence.