In 2023, retail firms understand the impetus of matching the elevated pace of business while also responding to supply chain collaboration challenges with agility. In this new normal, suppliers have emerged not just as providers of goods but as strategic partners.

According to The Future of Procurement, Making Collaboration Pay Off, by Oxford Economics, about 65% of practitioners say that procurement at their company is becoming more collaborative and strategic with suppliers.

Moreover, retailers who invested in improving supplier collaboration have seen a 20% increase in revenue directly or indirectly, according to the Coresight Research survey of retailers, by Precima. Among some of the direct benefits received by these firms are lower costs in the supply chain (32%), faster order fulfillment (30%), and stronger promotions strategy and calendar (28%).

Benefits of Investing in Better Supplier Collaboration

With retailer collaboration platform emerging as a primary key to unlock the next phase of growth in retail and drive synergies that are otherwise perceived as impossible within the confines of the traditional business, no retailer can ill afford to ignore it.

With that in perspective, here are the top challenges that retailers face today due to ineffective supplier collaboration.

In most retailer setups, category managers are on their own to plan for demand, assortment, and inventory. Understanding all that is happening on the ground, translating it to requirements, and directing it to the right vendor completely takes up the bandwidth of category managers – denying them the opportunity to focus on more strategic affairs.

This is one of the leading causes why category managers have not been able to catch up with the fast-paced business retail has become today, leading to elevated levels of stock outs, wastage, margin erosion, and new product introduction failures.

According to our estimate, new product introduction failure rate due to supplier challenges for most retailers is as high as 25%.

Why? Because new product introduction is a very time-consuming process that involves several iterations of communications to onboard the product (and vendor in some cases). With category managers already struggling with adapting to fast-paced demand, they have little oversight on the various stages of new product introduction – often leading to delays and failures.

Most retailers have realized the value suppliers can bring and help ease this burden. When suppliers are part of your retail planning process, they are able to help category managers make the right decisions at the right time while also benefiting from them. It is in other words a Win-Win.

Managing a vast set of products across stores has its own challenges. One of the greatest challenges is setting the right price of the product. The cost price of a product changes with tax, geographical, and transportation factors.

In the absence of a track record of historical pricing, category managers do not have a reference they can use to make pricing decisions. Most category managers are forced into frantic communication with suppliers to determine the right price. Ultimately, this lack of transparency leads to suboptimal pricing, eating into your already thinning margins.

Another challenge is with regards to promotions and rebates. Retailers today face underutilization of retail vendor collaboration funded/co-funded promotions. The main culprit here is the siloed nature of communication that happens before a promotion is run and a lack of transparency on promotions performance.

As a result, suppliers don’t proactively initiate promotions. Even if they do run the promotions, they are unable to learn and adapt to run more effective campaigns.

Nothing is more detrimental to efficient operations than not having a reliable source of truth to measure and course correct. Unfortunately, retailers still rely on external teams to extract valuable business insights. By the time they get these reports and make sense of it, proverbially speaking the train has left the station.

Data collaboration and reporting form a key pillar to supplier performance. In the absence of quality data from retailers, suppliers are reluctant to collaborate and plan better. Their focus is solely on delivering orders, rather than larger objectives such as category growth. Retailers themselves don’t have reporting capabilities to track supplier performance and evaluate it from time to time.

There is therefore a strong merit in on-demand, persona-specific access to real-time insights and reports. The implications are far reaching, spanning across functions such as sales, category, inventory, assortment, demand planning, financial planning, and more.

In today’s fast-paced retail environment, building responsive and resilient supply chains depends on how tightly knit your supplier ecosystem is. Even though we live in the world of WhatsApp and Slack in our personal lives, collaboration with suppliers has yet to catch up.

Across a typical supplier lifecycle—onboarding suppliers, information management, onboarding product, creating order, generating invoice, running promotions, and more—there are multiple barriers to execution. The biggest of these barriers is a siloed chain of communication – not being able to take action at the right time.

As a result, most processes get delayed and require greater manual oversight. This not only leads to increased costs but also affects the morale of suppliers.

Let us take an example of a simple Purchase Order (PO). PO raised by a buyer undergoes several changes in coordination with the supplier before it is converted to a Delivery Shipment Note (DSN).

These DSNs are then tallied with received goods by the warehouse manager. Invoices raised by suppliers are validated by the AP processor against POs and DSNs in coordination with the warehouse manager and the buyer.

Finally, the payment is processed to the supplier. As you can see, there are at least 5 participants in the entire process, but even today for most retailers the communication happens at best on one-on-one email, messages, and phone calls. This leads to delayed payments and excruciating audits which inadvertently affect business growth and profitability.

Also read: Expert Opinion: Is Supply Chain Collaboration The New Frontier Of Growth In Retail?

Vendor Link helps retailers digitize and streamline end-to-end inventory optimization supplier collaboration processes, build seamless communication across supplier lifecycle, and elevate data insights and collaboration to a whole new level. It’s mobile first and data-science backed use cases help retailers save on operational costs and unlock top line improvements.

Learn more about Vendor Link here.

Demand forecasting is the process of predicting how much demand your products will have over a specific period, based on historical and real-time data. It helps make the right procurement and supply decisions for the business and its customers.

As a retailer, demand forecasting must be routine for you, whether you sell 1,000 SKUs or 10 million. In fact, the higher the number of products you sell, online or offline, the more important it is that you forecast the demand for your products accurately for the upcoming months.

Demand forecasting is essential for almost every activity from production or procurement planning to sales and marketing to assortment planning.

It is a critical BAU activity for several reasons, such as:

• To balance product availability with minimal stock risk—cut down inventory optimization issues and wastage at the same time
• To ensure you are able to procure the right amount of inventory required to meet customer requirements in the near future: both online and offline
• For optimal retail inventory optimization solution and management and to avoid out-of-stock as well as excess or old stock scenarios
• To understand which products are needed in approximately what quantity at each store
• To know how much inventory your warehouses should store to meet consumer needs on your digital channels
• For capacity management—ensuring that production/supply and in-store efficiency is aligned with the projected demand
• To make supply chain management more efficient by helping you decide the inventory required for each product category and whether more or fewer suppliers would be needed at a time
• To be able to create, produce, procure, or design new products to meet customer needs better
• For planning production requirements and logistics, if you are a D2C brand that manufactures your own products
• To be able to do assortment planning the right way so that products not being sold during a particular period do not take up key shelf spaces
• To optimize cross-sell and upsell strategies around alternative and similar products
• For optimization of product promotion campaigns and advertising spends, i.e. knowing which products to promote through discounts and offers and which not to
• To reduce operational costs and increase profitability

Once upon a time, demand forecasting was siloed to individual stores, and having one individual dedicated to tracking product movements and predicting requirements was enough.

But in the past decade, with different sales channels—multiple stores (many a times in different countries), websites, and apps—it is important to have an omnichannel outlook to forecasting.

The scale of omnichannel means that the amount of data—related to both product movement and customer behavior—is massive, which is beyond the scope of a few individuals and their spreadsheets.

Traditional demand forecasting solution methods consist of two key areas:

1. Quantitative methods, which employ mathematical and statistical models to understand the trend and results. These include models such as Percentage Over Last Year, Moving Average, Linear Approximation, Exponential Smoothing, Lifecycle Modeling, Time-series Modeling, Regression Analysis, and Econometric Modeling.
   
2. Qualitative methods, which are subjective and sociological methods of collecting information and applying ideas generated from them to the problem at hand. These include Market Research, Historical Analogy, Expert Opinions, Delphi Method, Panel Consensus, and Focus Groups.

As is obvious, most traditional demand forecasting methods are manual in nature, relying on collecting information and analyzing them using spreadsheet formulae.

But when your retail data points run into millions and the variables that determine the demand for a product run into dozens, manual forecasting is simply time-consuming and prone to human error.

In addition, it is impossible to consolidate all data points and all kinds of different analytical models into a single spreadsheet or chart for a 360-degree view—inevitably, some factors get left out and siloed interpretations follow.

You might find one statistical model telling you that you need to stock up on baking essentials because it’s Thanksgiving. Another study tells you baking is falling out of fashion because people are working more and have less time for personal activities. And then, a third unknown factor of sudden bad weather drops out of nowhere. So, should you stock up on baking essentials or not, and how much?

Today’s retailers must have accurate demand forecasts in order to optimize every part of the chain of activities required to meet the day-to-day appetite for their products. The better forecasts you build, the more efficient each of your procurement, sales, and marketing processes will be.

And nothing can give you better data accuracy than machine learning-based software.

McKinsey notes that using ML and AI in demand forecasting and supply chain management can reduce errors by up to 50% and reduce lost sales and product unavailability situations by 65%. This can lower warehousing costs by up to 10% and administration costs by up to 40%.

These benefits are surely too good to pass up.

For starters, AI algorithms use a combination of the best of mathematical, statistical, and data science models. An ML-based forecasting software doesn’t simply apply past patterns within a business to predict future requirements; it evaluates each factor likely to impact demand in real time, and automatically gives you a constantly updated picture of sales, demand, and inventory.

Machine learning can process millions of data points in minutes, draw trends and insights across different dynamic conditions, and show you how each variable affects another and thereby the overall demand. It can find non-linear connections between variables, which are crucial for the best forecasting models.

Plus, these algorithms constantly learn from the data the software ingests. It is already trained on several forecasting models and historical data, and further training with real-time data strengthens its accuracy. This helps you automate the whole process and cut down on the human hours required for the task.

All this makes predicting demand through machine learning accurate, fast, and scalable, which, in turn, ensures efficiency in the entire supply-to-sales chain.

To summarize, using machine learning for demand forecasting can benefit you in the following nine ways:

1. Process more data points than a human can
2. Process data from more sources
3. Process the data quickly
4. Identify hidden trends and insights from the data
5. Identify relationships between the variables that impact demand
6. Generate accurate forecasts by factoring in several variables
7. Automate and update the forecast in real time
8. Make the forecasting system robust, scalable, and adaptable
9. Save time, money, and resources by making every step of the supply-to-sales chain effective and efficient

Related Blogs

• How the Autonomous Supply Chain Is Bringing New Efficiencies to Retail Logistics
• Grocery Replenishment Has Evolved: 9 Things Every Retailer Must Know
• 9 Best Practices in Demand Forecasting for Grocery Retailers

Now that you know the immense benefits machine learning can bring to how you forecast demand, time to look at different ML-based software and get one for your business. ADA Global’s Forecast Right is one such AI-driven forecasting solution that is also easy to use.

Sounds interesting? Explore demand forecasting or Get a demo to decide for yourself.