Introducing Service Level Predictor from Peak

Service Level Predictor is a new capability within Peak’s Inventory AI product that makes this a reality. No more poring over spreadsheets reacting to last minute raw material shortages, production issues or supply challenges. Service Level Predictor will surface upcoming service issues before they take place, giving you ample time to prepare and prevent them. 

What does Service Level Predictor do?

This module predicts the service level for every product, in every location, over a future horizon. It uses demand, stock and other crucial data alongside intelligent AI models that identify when service levels are expected to drop below agreed target levels, highlighting these risks early and giving you plenty of time to prevent these issues. It extends beyond a basic forecast by modeling the service risk associated with each product and the real world impact each issue presents in terms of missed demand.

What are the benefits of Service Level Predictor?

What sets the Service Level Predictor apart is its dedicated focus on forecasting future service levels by accounting for volatility — an aspect many traditional inventory management software and ERP systems with forecasting modules lack. This unique offering elevates your internal processes to balance customers and costs.

• Proactive service level management: Identify at-risk products and customers well in advance
• Improved S&OP collaboration: Provide a unified source of truth for service level expectations across various teams
• Informed decision making: Adjust production plans, negotiate promotions and engage in early discussions with customers to prevent issues before they occur
• Stronger customer relationships: Boost service levels, customer satisfaction by proactively addressing potential issues and maintaining high service levels. Become their supplier of choice
• Reduce avoidable costs: Minimize wastage, late delivery penalties and other costs

Service Level Predictor enables organizations to move beyond outdated and inefficient planning methods. Increase collaboration and empower your business to predict, plan and perfect your customer service.

See it in action. Contact us today to discover how Peak’s Service Level Predictor can help you.

How difficult is it for supply chain leaders to keep up with the modern-day pace of change?

Supply chain leaders have a difficult job at the best of times, but the pace of change seems to have accelerated significantly over the past decade. With Brexit, international conflicts, a global pandemic and significant environmental and economic disruption, ironically it seems that change is the only constant.

These factors have presented new considerations for many of us, but they’ve had a significant impact on the costs and regulations faced by supply chain teams. Leaders already face internal and external pressures, limiting systems and operations, as well as conflicting KPIs.

Trying to make complex decisions to balance metrics such as cost and service is one thing. Doing this on outdated systems that are no longer fit for purpose is another, especially with the skills shortage seen in many sectors such as construction. Throwing rapid change into the mix doesn’t make things any easier.

But, it’s not all bad. Supply chain leaders can transform the heart of a business, for the benefit of the organization and the customer. They’re often key members of the organization with the most experience responding to change, being uniquely placed to have a huge impact.

Is it feasible for leaders to make their supply chains more agile? If so, how?

Words like resilience and agility are often thrown around when we talk about supply chains and, the truth is, they’re both very feasible things to achieve. Plus, organizations don’t need to undertake a huge transformation project to get there. In my experience, the key to positive progress is to take small, meaningful steps led by data.

There are two key levers that leaders can pull to drive agility. The most important one is technology. The only way to consider such complex decision making on a daily basis without hiring 10x team members is by relying on innovative technologies that can help you come to optimal decisions faster. There are a huge number of vendors in the market, but the most important thing to consider is whether it will adapt to your business. This is where AI is your best friend — it can save time, reduce costs and improve service in a flexible way.

The second lever is change management. As we’ve established, change is a challenge. Taking people on a journey is crucial, so investing in technology and taking meaningful steps alongside your team towards being more data-driven, agile and resilient, is far more important than immediately going all-in with an enormous rip-and-replace investment.

How can companies meet demand in a cost-efficient way?

Optimal inventory management holds the key to the balance of cost and service. Too much inventory and you have expensive wastage, lost margin and capital tied up that could be invested elsewhere. Too little inventory and you risk shorting customers and losing revenue. However, getting this balance right is easier said than done.

The foundation for success is an intelligent demand forecast. Not a static model at category level re-run at set intervals, but a granular SKU-level forecast that enables rapid response to changes in demand. Every SKU, location and customer shows unique behavior. As humans, trying to consider the millions of permutations between these variables is simply impossible. We end up dealing with the most valuable and challenging items, with no time for those in between. This isn’t optimal or sustainable.

However, even an intelligent SKU-level forecast isn’t enough. It’s not just about your forecast accuracy, but actually using it to make optimal decisions. It should be used to set healthy safety stock and inventory levels, ensuring you’re always hitting that perfect balance at the most granular level. This is what AI can enable you to do at scale.

How can firms harness the power of AI to optimize product inventory and pricing?

AI is uniquely placed to optimize inventory and pricing decisions for organizations. It can consider millions of complex data points that humans don’t have the time or resources to consider, making recommendations that save teams hundreds of hours. This time can then be spent on strategic planning and exception management, rather than trying to wrangle data across multiple systems and spreadsheets.

It’s also important to consider that every business is unique. Unique SKUs, customers, systems and data. This means that your AI should be unique, too. It must be flexible and work with your organization’s processes, constraints and requirements to drive substantial ROI over the short and long term.

Peak enables every business to win, sitting on top of existing ERP systems with its own AI to optimize inventories and pricing. You don’t need a large, in-house tech team. You don’t need to rip out current systems. We simplify AI adoption, removing the barriers and complexity to make great outcomes and rapid results accessible to all.

This article was originally published as a contribution to Supply Chain Digital.

Having to adjust course is no easy feat. This is especially true as we continue to get hit by an increasing number of legislative impacts such as Brexit, sustainability requirements and other regulations — all of which are making things even more complex.

The core fundamentals are still just as important, such as efficient stock holding, maximizing service and operating efficiently. But with interest rates and the cost of capital at a high, this is becoming more of a knife-edge. Businesses can’t afford to have capital tied up in overstocked products, but they can’t afford to miss sales either. And, to add another complexity into the mix, they don’t want to pass too much of the cost on to their customers.

Meeting demand is constantly taking on a new meaning: how do I meet demand in the most cost-efficient way?

We must turn to technology to help us. Accessing optimal demand predictions, leveraging better inventory and pricing strategies and more agile ways of working are a must — they will help you steer that ship faster than ever before and, crucially, ahead of competitors.

Tackling the tightrope: data is key

Supply chain leaders are walking an increasingly challenging tightrope, and they have had to learn how to do it in real time. To keep output well-balanced, trade-offs between cost, speed and service must be factored in; this is at the same time as the landscape shifts, where uncertainty and higher customer expectations go hand in hand.

The agility of any supply chain is dependent on the strength and reliability of its data. That’s why how it’s captured is crucial, both in terms of accuracy and speed. Without this data providing a full overview of the supply chain, leaders are walking the tightrope blindfolded and imprecise data could send them further off course.

Agility over easy-going: give it time

Enhancing flexibility, responsiveness and adaptability are the key focuses for future-facing supply chains. But this can’t be achieved overnight. The entire business, at every level, should be on board with the fact that true supply chain optimization takes time.

Yes, embracing tech will encourage flexibility and provide detailed analysis, but instant success should not be expected. It’s essential that the supply chain team is committed to utilizing the newfound tech in as many ways as possible.

AI-powered optimization: achieving optimal inventory levels and pricing

Knowing how much stock to hold is an ever-present challenge for supply chain leaders. If demand suddenly changes, sales can be lost or capital gets tied up in excess stock. But the use of AI is providing companies with in-depth, accurate insight to achieve optimal inventory levels. AI can monitor stock in real time. It can balance fluctuating demand with factors like sales history, product availability and location. As a result, it can ensure the right product is in the right place at the right time.

The technology can also show teams the optimum price for products throughout their lifecycle. It can provide recommendations that balance customer demand and business objectives in a way that maintains margins and drives profit.

Predicting uncertainty? The power of accurate forecasting

Uncertainty will always be rife in the world of supply chain. Nevertheless, AI’s insights can ensure quicker decision making when your supply chain feels the ripples of a global event. AI can improve demand forecast accuracy and agility, helping organizations maintain good service and inventory levels while minimizing waste.

While not easy, agility in the supply chain should always be strived for; it powers a balance between operational efficiency and the flexibility needed to react swiftly when issues arise.

The bottom line

As supply chain leaders battle ongoing uncertainty, having accurate data and an agile team is crucial to effectively meet changing demand in a cost-efficient manner. It enables managers to walk an increasingly challenging tightrope of balancing internal strategy with external trends.

To implement such a strategy, embracing AI is key. Supply chain managers can capture and access real-time insights on their inventory levels, allowing them to maintain the right amount of stock and offer it at the optimum price. Crucially, this means that if global events impact demand, organizations can remain operationally resilient and steer their ship far quicker than would otherwise be possible.

This article was originally published as a contribution to SupplyChain Strategy.

1. Dealing with resource constraints

The departure of many production planners from the manufacturing industry means that resources are stretched more than ever, with the remaining workforce dealing with increased responsibilities and added pressure. Although this creates its fair share of challenges for businesses, it also brings opportunities in the form of AI.

For example, the UK housing market could change significantly in 2025 after a government pledge to build more homes. However, instability in the market and a shortage of the planners needed to manufacture the building materials required to build houses and flats poses a challenge. 

By leveraging AI in areas like production planning, reordering and general inventory management, supply chain teams can work faster and more effectively, bridging the gap created by reduced resources and driving stronger outcomes.

2. Focusing on the bigger picture

This one is a common misconception around using AI, particularly when it comes to its implementation in areas like planning and manufacturing. People often assume that production planners need extremely granular focus when leveraging AI in their planning processes — but this can actually be detrimental and hold teams back. 

Higher-level, strategic planning allows for greater agility and adaptability, which are key traits for success in 2025. Expect to see a shift in focus towards prioritizing flexibility, as supply chain leaders look to increase their ability to respond quickly to changing demands and fluctuating market conditions.

3. Generative AI for efficiency

After dominating the headlines since its explosion, excitement around generative AI will begin to settle in 2025 and start to feel more normalized in the workplace. Attention will move away from the idea that generative AI can do everything for you, and instead turn towards more practical applications that can make a real impact in terms of business efficiency and performance.

We expect to see more businesses leveraging this technology to break down complex data, introducing automation into some of their more manual tasks and also asking questions and gaining insights and recommendations on next steps.

In a supply chain context, generative AI’s primary role will be to optimize efficiencies and increase output, helping businesses achieve more with less.

4. A new era for production planners

There’s no denying that AI will continue to reshape the role of production planners in the manufacturing space, so it’s of critical importance that planners familiarize themselves with the capabilities of this technology and become AI-literate. 

With fewer supply chain staff available in the market, leaning on technology is going to become more important than ever. Production planners must embrace AI as a powerful supporting tool in the workplace — and those that resist this shift risk being left behind as the industry evolves.

Same problem, far greater variance

Economic downturns, geopolitical tensions, extreme weather events and changing consumer habits have always played their part in global challenges faced by supply chain managers. What’s more, a reliance on historically manual processes and spreadsheets has made it incredibly challenging to gauge what optimal inventory levels look like and how best to balance costs.

Today, unprecedented modern events have laid bare supply chains’ vulnerabilities, meaning solutions need to be found at pace. Not only caused by political or economic levers, even superstars impact global supply chains. Last year, Google search data showed a notable spike in the search term ‘metallic cowboy boots’ as Beyoncé’s Renaissance world tour kicked off and fans grappled to purchase their own show outfits. If you also consider vastly changing customer needs driven by economic uncertainty, supply chain managers are dealing with a host of obstacles; demand can change at the flick of a switch.

Disruption and uncertainty can always be expected, but striving for near-perfect inventory levels — including more SKUs and faster delivery — is almost impossible without AI.

Overstock vs. out of stock: a balancing act

The key challenge for any supply chain manager is finding the balance between holding too much stock (especially when demand is low) and too little (especially when demand is high).

The former means they risk obsolescence, needing more warehouse space to house additional stock and potentially wasting vast amounts of products that go past their ‘sellability’. It can result in the business having to shift that stock at discounted rates and puts pressure on nailing every single sale. The latter of course means a host of missed sales opportunities, not only impacting revenue and operational costs but also brand reputation. The consequences can be critical to a business.

Forward-thinking companies are looking to AI to optimize their inventories. The more they can optimize, the less sales lost and the less capital tied up in excess stock. For example, by using AI, they can assess inventory levels in real time and instantly make decisions to balance factors like product availability and life cycles with operational costs, a process that used to take days or even weeks.

And for the early adopters, the proof is already in the pudding. Using AI, McKinsey research showed these adopters lowered their logistics costs by 15% and improved service levels by 65% “compared with slower moving competitors”. But if the results are attractive, why are more companies not jumping aboard the AI train?

Optimizing team processes

It’s the unfortunate truth that supply chain teams are struggling for resource. In fact, a recent survey found that 76% of supply chain and logistics leaders are experiencing significant shortages in their supply chain workforce.

Part of this is due to recruitment challenges, which plague the industry. Enticing tech companies and roles attract talented tech workers. Supply chain is missing out on key talent. Organizations need to shine a light on these supply chain roles and companies to ensure there is a healthy flow of talent into the sector.

On top of recruitment challenges, to combat these pesky supply chain resource shortages, organizations need to optimize their team processes. But, what does this look like?

When AI is introduced into the fray, teams can regain time in both the short- and long-term, giving them much-needed time back to plan and strategize. AI-powered insights also elevate this strategic planning, empowering supply chain teams with a quantity and quality of data they might not have accessed previously.

An approach fit for the modern world

You can never truly predict future customer demand. But if you know how much variance there usually is between forecasted demand and the true level, you can implement a supply chain strategy and inventory level best suited to deal with this fluctuation.

It’s not an exaggeration to say that without using AI in the coming years, companies could be losing millions in margin compared to their AI-powered competitors. But if they can start to embed AI into their supply chain operations now, they can form a flexible and modern approach to tackle a problem as old as commerce.

This article was originally published as a contribution to SupplyChain Strategy.

The meaning of sustainability has changed

Our minds usually jump to the environment when we talk about sustainability, but its meaning has changed over the years as global priorities have shifted. It wasn’t until the 20th century that it began to represent what we now recognize as the accepted definition: creating lasting and efficient methods through which to live our lives in order to maintain our world for future generations. Be that environmentally, economically and socially.

Why is it so important?

Our planet is ever-changing. This has been highlighted this year, as we watched the COVID-19 pandemic sweep across the world. Countless images began surfacing on social media showing unusual wildlife encounters, and whilst it may be unlikely that Venice has been invaded by dolphins, there has been a huge and relatively quiet impact on the sustainability sector and the environment. Carbon emissions have shown a rapid decline, there has been an uplift in interest in green technologies such as solar power, and the environmental sector is being used across the globe to boost the economy – just this month it was announced that investment would be made in a large green space in the heart of Manchester, just down the road from Peak HQ. Of course, this has all been the product of a tragic global event, and we all long for the day we are all able to get back to the safety and comfort of our normal lives. 

But if there’s anything we can learn from this difficult time it’s that we, as both individuals and as key parts of larger businesses, are responsible for the world around us, and that going forward we can have more of an impact than we may realize.

The future of sustainability – what can AI do?

We’re living in an era in which we must develop and utilize technology to ensure we’re leaving our world in a better place than when we came into it. Through collaboration and innovation, we can use technology to ensure we’re all doing our bit to tackle these problems in a dynamic and agile way. 

Here are three of the key areas we’re seeing AI help in terms of driving sustainability:

1. Navigating complex sustainability goals…

Businesses are aligning themselves with the UN’s Sustainable Development Goals to do their bit for the environment. However, actually achieving these goals can be difficult, especially without the technology in place to help.

This is where AI comes into play. By looking holistically at business processes and unifying previously siloed data, AI and machine learning models can factor in far more complexities than the human brain can manage. For instance, this could be balancing availability with inventory levels and working capital. Or, reducing costs, growing profits and increasing efficiency without impacting customer service. These are just two examples, but by underpinning business decisions with the intelligence of AI, organizations can achieve and exceed their sustainability goals even as the world around us changes.

2. …and achieving them in the most effective way

Two businesses may have the same sustainability goals, but the best way of achieving these goals will be different for each of these businesses. 

Since every organization is underpinned by unique data, the optimal way to achieve strategic goals will be unique, too. For example, a 5% reduction in wastage might be best tackled through a reduction in waste using dynamic inventory levels and intelligent replenishment, or a reduction in returns by better understanding each customer. A reduction in carbon emissions may be best achieved with logistics optimization, or may be best achieved by optimizing production scheduling to minimize production time. AI gives you the power to deploy bespoke solutions at speed.

3. Being dynamic and adaptable as the world changes

If there’s one thing 2020 has taught us (other than the speed of our home WiFi), it’s that some things just can’t be predicted, and the world is constantly changing. This makes working towards a sustainable future even more challenging.

It’s hard to keep making an impact as the world around us changes and our immediate priorities shift, and the best way to have this impact will constantly change as customers, processes and the environment around us changes. Utilizing AI allows you to be dynamic, respond rapidly to change and ensure you’re always achieving business targets in the most optimal way.  If you have a flexible technology which helps you to react rapidly and is accurately underpinned by your unique data, it no longer matters if we can’t ‘see into the future’.

AI is already having a profound impact across many different sectors, by implementing solutions which are faster, more efficient and more intelligent. Every business is different, as are their sustainability targets. But technology can make sure that these targets are achieved and exceeded, helping to drive truly sustainable growth.

Want to find out more about how Peak’s unique approach to enterprise AI can help you achieve your sustainability targets? Get in touch!

For more key reading on AI for sustainability, look here ?