The global supply chain as we know it has been exposed to its weaknesses in the recent past – whether because of a never-before/seen global pandemic, surging e-commerce demand or dynamic labor and social fabric, the challenges we are currently facing seem insurmountable and overwhelming.
This combination of factors has multiplied the stress on lean and bare-bones supply chains, creating a dangerous bullwhip effect, leaving impatient customers waiting in lines for products, retailers struggling to retain customer attention and service level goals, and suppliers dealing with insufficient capacity, with only a key question in common: how will it be from now on? But to understand where we are going, we must first question ourselves: how did we get here?
The COVID-19 pandemic was unquestionably a catalyst event: the number of factories that stopped production altogether during the first few months of 2020, along with national lockdowns and stay at home government policies brought the world commercial landscape to a halt (like a bright and endearing sidenote, these lockdowns and stoppages also reflected themselves on the environment and pollutant emissions across highly industrialized areas).
This meant that, at a rather sudden pace, customers were locked within their homes, with little to none cultural and recreational events at their disposal and without social gatherings where to spend “play money” – as a whole, the world consumer behavior turned its focus to e-commerce and online retailers.
Many retailers and shops were not ready for this and lagged in adapting their business and supply chain models to this new reality.
Parallel to this, COVID-19 cases rose at alarming speeds, meaning that suppliers and industries frequently found themselves with capacity limitations due to worker shortage, thus struggling to meet demand. And then, just as quickly as they were imposed, thanks to herculean scientific, medical and logistical efforts, lockdowns and travel restrictions were lifted, almost worldwide. Swiftly the economy gained traction and sped up again: customers were eager to taste life back as they used to, and soon there was another boost in demand.
However, behemoths such as worldwide supply chains and logistical plans do not change overnight, and significant delays started to appear on the supplier’s end of these supply chains. Traffic in ports increased sharply in 2021, following a counter-cycle decay in 2020, coupled with an exponential increase in container prices (fivefold increase per 40ft container comparing with January 2020).
This sensitive and explosive combination means that, plain and simple, moving products around has become increasingly expensive, and the margin for supply chain and inventory management errors has fallen to near zero.
Companies must be agile and flexible to surf this wave: consumers want more, more quickly, and without paying extra, and have become increasingly unforgivable when it comes to out-of-stock situations. Past managerial policies are unlikely to answer to these challenging times.
As such, new policies are needed, and analytical methods have an important role in supporting their definition.
But how can companies take the leap, while making informed and analytics-based decisions? What are the options?
The “last mile” of the supply chain is often the most challenging: highly granular and dense dendrite-like chains mean that, often, distribution centers become increasingly large and “far away” from demand focus points.
The decision on whether to open more distribution centers, where to open them, how to size them, to effectively balance demand throughout a network of smaller DC’s are decisions on top of the mind of supply chain managers
Analytical approaches can boost supply chain performance and robustness.
Digital twin models are powerful tools to understand how thousands of different supply chain configurations answer to fluctuations and peaks on demand, allowing to find pain points and oversized areas on the overall network.
The production strategies that once worked may not be the most adjusted to these new times – the decision on whether to Make-to-Stock (MTS) or Make-to-Order (MTO) can be an important lever to become more efficient.
Furthermore, such strategy does not need to be taken at company or category level, as the definition of the product strategy for key products can have a significant impact on both operational and commercial KPIs. These decisions, once taken relying on the historic know-how and empiric feeling, must be supported by high-performing and flexible analytical models, which allow for greater insights on what each decision holds for the future.
Traditional forecasting modules are being questioned for the lack of ability to perform under today’s uncertain events.
Advanced forecasting modules which can quickly adapt to the dynamic market conditions and correlate demand profiles with exogenous sources of information are vital sources of information nowadays. They allow for greater confidence and readiness for the future – companies must look ahead with the trust that their models truly paint a realistic picture of what the future holds, and such knowledge beforehand is key to modulating an anticipated response.
Imbalances and disconnection between the supply and demand in a chain typically reflect themselves on surging inventory levels while having poor customer service – novel, analytical and scientific based inventory management policies are of utmost importance if companies want to maintain controlled stocks levels while performing at the desired service level.
The distribution routes that worked in the past are very unlikely to excel nowadays – operational constraints change daily, and the definition of these new routes and the logistical planning required to perform at the highest levels of flexibility and service level rely on key analytical models and novel routing algorithms.
These are some examples in which analytical models can boost decision-making in supply chains, yet the number of challenges on the supply chain that lay ahead is enormous and their complexity is increasing. In any case, decision-making should be guided by insightful analytical models, which allow to explore different perspectives and understand the ability to face up different scenarios, rather than gut feelings or historical perspectives that might be outdated or misfitted to current challenges and improvement opportunities.