The Next Evolution of Supply Chain Planning

10 minutes

Adopt a fresh perspective

There’s never been a better time to take stock of your company’s demand planning process and supply chain goals. Because unforeseen variables will inevitably continue to drastically impact consumption and disrupt global supply chain planning, organizations should proactively put into place the tools and processes that will enable them to remain agile and competitive for years to come.

With some of the turbulence of the pandemic era in the rearview, it’s clear that certain nice-to-haves have become need-to-haves, including automation and artificial intelligence (AI), which can bring about incredible efficiencies across your entire supply chain.

These technologies can free CPG brands from inaccurate forecasts, time-sucking manual processes, and wasted capital that could otherwise be allocated to growth initiatives. Automation and AI can keep companies nimble, giving them the agility they need to quickly pivot their Integrated Business Planning (IBP) strategies and feel more confident in their decision-making process during times of disruption.

Today, the most effective IBP processes will rely on technologies that have a direct and quantifiable impact on the efficacy of your financial, operational, and supply chain management strategies—all in all, technologies that maximize your supply chain optimization. This begins with your outlook.

Adopting a fresh perspective means implementing a digital brain that transforms and optimizes your supply chain performance. An effective supply chain planning solution that enables lightning-quick data analysis, AI-powered recommendations, and actionable insights allows for confident strategy-shifting at the click of a button.

In this guide, we’ll show you the factors that matter most when reassessing, selecting, and bringing to life the right changes at your company. Welcome to the next evolution of supply chain planning.

Now, let’s dig in.

Say ‘hello’ to alignment and ‘goodbye’ to silos

Data between business units should be easily accessible and centralized, not varied and siloed.

Key executives and managers across various departments of the business (sales, supply, finance, operations) should have immediate access to an unfettered, unified view of data—full supply chain visibility and a single source of truth, if you will—thereby ensuring that all stakeholders in the IBP process are working from the same view.

Automating this process makes cross-functional team dynamics stronger and more efficient, and pushes the decision-making process in the right direction, faster.

More data the merrier—as long as it’s high-quality

There should be no barrier between the data a company possesses and its ability to transform that information into actionable intelligence. In the past, sometimes because of a gap in technological infrastructure, organizations may have been hindered by the amount of data they could capably handle (to say nothing of their ability to analyze it).

Nowadays, building a deep datapool should not be cause for worry for executives involved in the supply chain planning process. Rather, CPG companies should possess the technological infrastructure needed to ingest, analyze, and actively understand scores of datasets—or partner with a firm that does.

All brands possess large amounts of datasets—some of them incredibly vast. But quantity is not a mark of richness or usability. Simply put, demand forecasts are only as good as the data used to calculate them. As they say, “garbage in, garbage out.”

In short, it's all about quality when it comes to the data you use for demand forecasting. Four hundred decillion incomplete or mismatched data points is exactly that. So when data is “dirty,” it must be cleaned. And the more data there is, the higher the likelihood of errors existing in the data pool, which in turn can negatively impact the data's usability.

Implementing a More Holistic Supply Chain Management Strategy [Unioncrate]

Eliminating data errors with automation

It bears repeating: Every bit of data that’s used in a forecasting model should be “clean.” Clean data refers to all internal and external information—SKU or item numbers, warehouse addresses, historical shipments, inventory levels, point-of-sale data, and more—that’s organized, timely, precise, complete,consistent, and generally error-free.

It would take one human being (let alone a team of them) a very, very long time—a lifetime, even!—to sift through a company’s data and check, double-check, and triple-check the viability of every single detail.It’s a human impossibility, and this is before any sort of analysis is completed or insights are drawn.

The data-cleaning process

Clean, rich data sets the stage for Machine Learning, which in turn drives actionable insights. As a result, CPG companies should possess substantial data-computing capabilities that can scientifically clean and process raw daily sales data.

They can automate this process by applying cleaning algorithms (data mapping) to identify and correct error patterns, such as human entry mistakes or those made by a third-party vendor whose data standards are not at the level you need them to be.

Data that could improve demand forecasts

Historical sales alone (shipments at the SKU level by customer, as well as ship-to and ship-from locations) cannot totally account for rapidly changing consumer behavior. Instead, the most powerfully capable prescriptive analytics should ingest a wide variety of datasets beyond internal data—for a more complete and accurate picture of the future.

As a result, forecasting models (for sales, distribution, inventory management, and more) should constantly analyze not only supply data but also consumption-level data and other information that could help brands improve forecast accuracy and gain a better understanding of the risks and opportunities that may lay ahead. Examples of this data includes:

1. Point-of-sale (POS)

This data at the chain and store levels, illustrates when, where, and how a customer made their purchase. It helps to ensure that sales are progressing as planned and makes unexpected, short-term demand spikes fully visible and accountable.

2. Seasonality

Hot chocolate on a cold winter day? Yes, please. Eggnog at Christmas? Always. Hot chocolate in the middle of July? Perhaps not. Seaweed snacks at the Super Bowl? Maybe! Trend-related data associated with time and cultural rituals is the stuff of (patterned) gold.

3. Category trends

Demand patterns for various essential goods, or health and ingredient trends.Yesterday’s charcoal toothpaste is today’s plant-based protein, for instance. This information can help brands not only forecast demand by region more accurately, but also pivot their own new product development and distribution strategies.


4. Brand’s competitive marketing, and trade investment

The performance of your brand’s marketing mix, such as promotions, advertising, and public relations can contain important information related to sales volume and velocity. This includes the effectiveness of trade promotions with supply chain partners across your supply chain network (retailers, wholesalers, distributors), and at the store level.

5. Search engine data

In 2020, we saw a stratospheric rise in e-commerce. Consumers are taking advantage of curbside pickup and increasingly making purchases from their phones. Digital retail services, such as Instacart and Shopify, bolstered consumers and businesses alike. Patterns in search queries related to the world of CPG are telling. This can cover products, brands, retailers, e-commerce, and other terms and phrases that provide insight into consumption trends.

Another data source that could potentially impact customer demand is:

Cross-channel social media mentions, trends, and sentimentality indices

Consumers are constantly on social media platforms and can interact with brands directly by posting their own content or by reacting to a brand they follow. Though its impact on demand may seem small most of the time, social media can offer useful insights into trending products and seasonal shifts, as well as the competition.It’s still ideal to have the ability to measure it—leave no stone unturned.

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Identifying dynamic relationships across datasets

Now that we’ve reviewed the types of datasets that could improve forecasts, let’s go over how Machine Learning models can be trained to constantly seek out and identify dynamic relationships, both short and long-term, between datasets. Some examples include:

1. Historical sales & seasonality data

There’s an immense wealth of data for both your supply plan and demand plan contained in raw day-to-day sales patterns, including the relationship between seasonal trends and long-term growth.

2. Historical inventory data

This information can include quantity on hand, quantity on order, weeks of supply, service levels from your supplier, safety stock, and more. For example, the analysis of quantities ordered vs. quantities shipped is vital to understanding sales that could have occurred vs. sales that actually occurred—due to insufficient inventory and botched supply management.

3. Product transitions, maturity data

Products can transform over time, whether it’s a packaging refresh, ingredient change, name change, or some other stage in its maturation. As a product is refreshed, it will have more than one unique identifier attached to it during its lifetime. Whether a SKU has one or many UPCs, AI makes it easier to keep a product’s history connected, especially when analyzing relationships between various product units.

4. Marketing investments and trade promotions data

It's vital to establish the numerical relationship between promos and changes in sales volume and velocity (lift/drop). This includes understanding the dynamics between the type of promo (TPR, shipper displays, coupons, e.g.)and promotion-related data that can demonstrate the effectiveness or impact of a promo:velocity change(s), ROI, and ROAS, for example. Trade promotions can be big drivers of sales,and it’s vital to incorporate this relational data into your forecasting models.

5. Obsolescence and discontinued product data

Data detailing product sunsets and loss of distribution is vital to understanding the demand of comparable products. Running conversely with product (or component) obsolescence is the pace of innovation and speed to market for many CPG products and product categories. Gaining a clear basis of understanding as to the reason a product is discontinued—such as innovation, new claims, a product enhancement, or poor performance—is essential to better predictions.

All of these dynamic relationships feed into supply chain analytics, which help shape more effective supply chain plans. And, with the right supply chain management solution, this data can be pulled from your ERP system.

Now let's lift the hood a bit and work towards a deeper understanding of AI, plus how it can drive more advanced planning and, therefore, better business decisions.

Uniting AI + Human Intelligence for Truly Agile Planning [Unioncrate]

What does it mean to use AI? How does it work?

At a time when data exists in near-infinite amounts, artificial intelligence is a vital tool for organizations in the CPG industry and beyond. But there’s more to AI than just the ingestion and analysis of data.

Behind the scenes of automation, Machine Learning models are constantly calculating and finding dynamic relationships between datasets, such as those mentioned above. Data engineers tweak and iterate models in search of locating clear-cut demand drivers based on consumer behavior and market signals, and some models give weight to top-selling SKUs (WMAPE).

These models form a neural network whose speed, dynamism, and computing capabilities ascertain complex relationships between datasets, and the more data that’s incorporated, the more the models learn and the more accurate they can become. These models make it possible to ingest and analyze immense amounts of data in a speedy, organized fashion. (This level of efficiency is a human impossibility.)

Eventually, artificial intelligence (AI) actively makes sense of all the learnings and becomes the internal digital brain of your supply chain process, providing accurate, granular forecasts and actionable recommendations.

Directly impacting your business

The value of AI and automation extends well beyond their ability to quickly analyze multitudes of data.If recent events are any indication, companies must be able to rapidly recover from an outlier event, which can quickly throw off their commercial plans and disrupt supply chain operations.

Chart of what the supply chain planning process looks like today [Unioncrate]

Brands need flexibility, agility, and actionable insights. As a result, CPG companies should double down on the technologies that can take their supply chain planning process to the next level. Having anAI-powered IBP process enables brands to develop a digital brain inside their supply chain operations—one that facilitates cross-functional collaboration through unified data, accurate demand forecasts, actionable insights, and many other quantifiable efficiencies.

Chart of the supply chain planning of the future [Unioncrate]

Company-wide impact of integrated planning

Accurate sales forecasts help VPs, senior managers, and other executives involved in the IBP process to align sales forecasts with departmental capabilities/restraints, and implement strategies to hit those goals. Accurate sales numbers also help executives communicate expectations to key company stakeholders.

Accurate distribution forecasts enable distribution teams to help sales teams. When sales teams are provided with visibility into customer inventory levels, they’re able to incrementally upsell the buying and strategic planning teams. The best distribution forecasts come with a customer scorecard, which enables distribution teams to predict their entire distribution footprint and adjust distribution schemes accordingly. They can optimize logistics such as service levels, lead times, and DC splits.

Accurate inventory predictions help manufacturing operations teams optimize their supply plan while enabling procurement teams to negotiate better prices, MOQs (minimum order quantities), and delivery dates with suppliers. They also help teams manage working capital, in part by negotiating volume cost breaks for raw materials,components, and finished goods. When inventory levels are optimized, stockouts, dead stock, and spoilage can potentially be avoided, and warehousing and logistics costs can be lowered.

Accurate manufacturing forecasts help to maximize the efficiency of line capacity and CAPEX (capital expenditure), reduce obsolescence, strengthen negotiations with suppliers, and more. Top-tier manufacturing forecasts enable plant managers to create an efficient production schedule across all locations, and determine potential bottlenecks (in real time) that could impact their ability to complete their production plan and reach target production metrics, including throughput, capacity, and utilization rates.

There is no time like the future

It’s no secret that the CPG industry is experiencing a once-in-a-generation shift, and it can be difficult to think about how to pivot and where to start that process.

So as you and your supply chain planner navigate these new and often unprecedented market realities, it’s important to have a fresh perspective. We hope that this guide will continue to provide answers and illuminate an actionable path forward for years to come. We believe that the path to becoming a supply chain leader is paved by automation and AI with a human touch, which, altogether, can enable incredible agility in the IBP process as well as very real growth.

And that won’t go out of style—now or in the foreseeable future.

About Unioncrate

Unioncrate is an AI-powered Supply Chain Planning Platform that gives CPG brands the technology they need to compete and win in a rapidly changing consumer landscape. Our automated demand and supply forecasts deliver unmatched accuracy, collaborative visibility, and actionable intelligence, simplifying a manual-heavy process and slashing hours from your week.

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