SaaS Optimisation vs Traditional Approaches

SaaS Optimisation solutions have become a necessity in modern business. These solutions don’t just result in lower costs, better products, and ease of use, but better customer satisfaction, employee satisfaction, and streamlined business processes.

Gartner predicts the SaaS market to be $85.1 billion globally by 2019, up from $72.2 billion in 2018. While much of this growth will be the continued adoption of infrastructure such as CRM and ERP, point solutions will continue to emerge to fill gaps.

What are point solutions and why should they matter?

Unlike ERP systems that provide end-to-end solutions for problems that you might not have, point solutions are targeted to specific pain points within your business. This might be: The best way to route your vehicles, the best way to manage your employees, or, the best way to build your facilities (location and size).

Point solutions also give you the capabilities of integrating with larger ERP systems.

A unique problem requires a unique solution.

The Key Benefits of SaaS

The benefits to a SaaS solution over a traditional approach can be evident by looking at these 5 key areas:

  • Reduced time to benefit. SaaS development reduces the feedback cycle to the point where anyone, anywhere in the world can test software on a web browser.
  • Lower costs. You don’t require expensive IT infrastructure and big IT departments, these worries are outsourced to the company offering the solutions.
  • Scalability and integration. SaaS solutions can be scaled up and down based on your requirements. This means you can drive more powoer in peak times or seasonality.
  • Faster releases (upgrades) as it reduces the feedback loop and allows for much easier troubleshooting.
  • Easy to use and perform proof of concepts

These benefits have allowed Biarri to develop world class tools for operational and strategic business requirements.

SaaS vs Traditional Software at Biarri

Beyond the above generic benefits, there are a number of specific benefits that we see at Biarri. We believe that the SaaS approach is far superior to ensuring the client gets a solution into a production environment sooner rather than later, it also makes the job of support much more efficient as well. Biarri’s specific benefits it sees are outlined in the below table.

SaaSTraditional Software
DevelopmentEasy to get constant feedback using the Mitigation by Iteration approach. More iterations and feedback from clients can significantly reduce development timeframes.The client either needs to wait for the final version to be installed in their testing environment. If iterations are used they are usually far less frequent (and more difficult to manage) than a SaaS approach.
User Acceptance Testing (UAT)All the client needs to do is use a testing environment URL. No UAT specific hardware is required by the client.Often clients need to setup different computers and/or databases for the UAT environment.
DeliveryClient usually only needs to type the production URL into a web browser. Sometimes their firewall needs to be configured to allow access to our servers.Software has to be installed on each machine which requires more resources from IT departments.
Hardware Clients only need access to an internet connected device. It does not need to be powerful. It could be a desktop or mobile device.Each user needs to have computing power, and memory, required to run potentially resource hungry optimisations.
Software CostOur optimisations sometimes use expensive third party tools (like Gurobi) which are on our servers. This cost gets distributed to all our clients in the form of licensing fees.Each user would need to have the expensive 3rd party software installed on their computer which has the potential to massively increase the final price if there are many users.
SupportWe can easily replicate issues because your data and logs are on our server. This often results in issues being replicated, addressed, and a fix delivered all in the same day.Often it can be difficult to even determine if an issue is caused by the software, or the complex IT environment that it may be running in. Complex IT environments may have internal firewall issues, database issues, Citrix issues, authentication issues, timeout issues, etc. If the problem is with the software it is sometimes required to export the client database and transfer it to the vendor (via ftp, or if it’s too large via snail mail). Sometimes replicating the issue can be difficult because the client’s and vendor’s environment are different (e.g. different database versions, different patch versions or operating system versions). This process can often take weeks even for relatively simple issues.

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Making it Accessible: Applying the Optimisation Toolkit

At Biarri Optimisation one of our core priorities is to uncover interesting new mathematical problems in industry, build optimisation engines to solve them, and use those products to incubate new Biarri entities.  It is a constant source of amazement at how many disparate contexts that we can apply the tools of our trade: in particular, the tool of Mixed Integer Programming, which is one of the pillars of our discipline of Operations Research. Tools like this live in our optimisation toolkit.

Operations Research (OR) has its origins in World War II, where mathematics and analysis was used to determine what convoy size was most effective for avoiding German U-boat detection, the best paint colour to maximise camouflage for aircraft, and to identify the best trigger depth for aerial-delivered depth charges to maximise U-boat kills.  You can read more about these fascinating applications and more in the excellent book Blackett’s War.

The problems we solve

We tend to think of the problems that we try to solve in the following way.  First, we identify the decision variables that are involved: what elements of an operation do planners and users have the power to change?  Second, we establish the objective: what are planners looking to minimise or maximise?  This might be cost, or some combination of minimising cost with maximising benefits or safety (for example).  Lastly we pinpoint the constraints: what are the real-world limits on resources (people, equipment, budget) that have to be taken into account?

An example is a daily workforce planning problem faced by warehouse and distribution managers in many different industries.  Here there is a “profile” of required work across the day, of various types (e.g. forklift work, truck loading/unloading, racking replenishment, etc), and a set of personnel who have different skills and availabilities to do this work (permanent, casuals, with various possible start times).  How to cover all the work while keeping the total shift cost as low as possible?

The objective in this example is cost; the decision variables are the allowed shifts that can be operated; and the constraint is that all the work must be done by the appropriately skilled personnel.  The extra complexities we might encounter here come about where people can do multiple task types (e.g. they are qualified for both forklifts and manual truck loading), and there may be a changeover time incurred between tasks; they will also require meal and rest breaks, or incur overtime; there is fatigue to take into account (productivity typically starts dropping later in a shift); and equipment and space/congestion constraints.  You can even apply a productivity multiplier if different people are more or less productive at different tasks. Add to this the fact that some of the work can be done well ahead of time (pre-picking for truck loads, for example), which allows the work profile to be “smoothed” over, and you have quite a complex planning task indeed!

When we formulate this as a Mixed Integer Problem (MIP), we aim to solve it for the overall minimum cost.  This overall minimum cost is known as the global optimum, and it may be that, if you look at any one part of the solution in isolation, it might seem “sub-optimal”.  In our workforce planning example, for instance, a person might be allocated a very short shift. But this will make sense in the overall sense of the problem: it might be that there is a small piece of work left over that cannot “fit” into all the other shifts.

Feasible solutions

Part of the art of applying this type of mathematical approach lies in making sure that there is always an implementable solution.  For example, what if there are not enough people to cover the work? We do not want the solver to tell the user that the problem is simply “infeasible” – that there is no plan.  Instead, we build in extra decision variables which model the unallocated work over time, and we give these variables a high artificial cost in our objective (so the solver still tries to cover as much work as possible); now we can also report to the planner how much work is uncovered.

This technique is an example of a more general method which distinguishes between hard and soft constraints.  Hard constraints must always be met; there is no wiggle room.  By contrast, soft constraints (as in the “uncovered work” example) allow you to break a constraint, but incur some penalty for doing so.  In the workforce example, overtime can also be thought of as a soft constraint (once you exceed the regular shift limit, you incur extra cost).

Soft constraints can also be used as a way to “explore” the solutions that are near the optimal solution – there are some trade-offs you might be willing to make in order to save an extra chunk of cost.  You sometimes have to be quite careful when there are several types of soft constraint or extra penalties: for example, allowing very large penalties can obscure the smaller components of the objective. When you have multiple competing objectives, it can also sometimes be hard to explain why the result looks like it does.

Trusting the results

Of course, we must also exercise caution when trusting results of a mathematical process.  Our solvers will make arbitrary decisions when we do not provide guidance – a common instance is where there is a “shallow” cost function, which is where there are several similar solutions with the same or nearly the same cost.  These might be identical from an optimisation point of view: in our workforce context, an example is where several people with the same skills and availability might be able to do the same task: if it makes no difference to the total cost, who should we choose?  Often there is a real-world analog to this problem of “breaking ties”: here, for example, we might choose the employee based on seniority; or to ensure a longer term attractive roster; or a host of other factors.

Biarri has always strived to make optimisation “accessible” – part of which means making our optimisation results implementable in the real world – and in these examples I hope you have seen some of the nuances and complexities that underlie this promise as well as the tools that comprise our optimisation toolkit.

By Andrew Grenfell

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BAM2018 Wrap Up

Are you our new Front End Developer?

We’re hiring, so if you’re a front end developer, then you’re in the right place.

We’re always looking for motivated and friendly front end developers but we have a number of wonderful front end projects that need some love and attention right now. The projects are multifaceted and exciting and you’ll be working with some of Australia’s greatest brands.

Not only that but we’re also a genuinely great place to work, having a 4.4 star rating on Glassdoor. At Biarri, you’ll get to develop your skills further and work with new technologies in a supportive and helpful team environment as a part of your day to day work. We value diversity and new perspectives, so everyone is welcome at Biarri.

If you’re a front end developer, then check out what we’re looking for here and get in touch.

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