Before the pandemic, many companies prioritized efficiency and lean supply chain models, focusing on minimizing inventory and ensuring products were available just in time to meet customer demand. This approach aimed to reduce costs and maximize operational efficiency, with limited reserves for unforeseen disruptions.
Operations focused on reducing waste and increasing efficiency, striving to deliver maximum value to customers while using resources most effectively. Operations managers continually analyzed tasks, processes and personnel to eliminate non-essential activities and ensure seamless communication at every step of the workflow. Digital transformation played a key role in this optimization, centralizing data and increasing visibility, allowing senior managers to have greater control over operations.
However, the upheaval caused by the COVID-19 pandemic and the broader international instability of recent years have changed the rules of the game. These carefully optimized systems, which had gone through iterations of minimal changes to function as efficiently as possible, were suddenly hit by a wave of unpredictable problems.
Lockdowns around the world meant that certain materials or components were in short supply, quarantine times caused transportation delays when crossing borders, and the change in people's daily lives caused shifts in the demand curve. Additionally, the rise of remote working meant that internal processes were forced to adapt and new methods of communication and collaboration became necessary.
Optimization was no longer the priority as the gains from this approach became negligible in the face of significant potential losses. Instead, the focus was on operational resilience. The organizations that emerged victorious were those with the ability to resist, adapt and recover from disruptive events.
In real terms, this means flexible logistics routes that are able to adapt to geopolitical situations, agile management of multiple sources and the implementation of a tactical buffer plan. Those organizations that successfully implemented these structural changes were able to avoid customer order cancellations and keep revenue stable.
The disruption caused by the pandemic taught us an important message: when you spend too much time solving predictable challenges, you run out of time and resources for unpredictable ones.
Responsible for the Global Operations, Business Technology and Quality teams at Alcatel-Lucent Enterprise.
A trade-off: efficiency or resilience
Since the pandemic, we have seen some degree of normalization across industries. Most organizations no longer face large-scale disruptions on a daily basis. However, the impact of these extreme phenomena has not been forgotten. As operations managers once again look for small wins in efficient supply chains, the prospect of chaos caused by unpredictable and uncontrollable events looms large in their minds. The question is: how much time should be allocated to daily challenges and how much should be allocated to predicting the big picture? Limited resources mean there is often a balance between efficiency and resilience, both of which are necessary for an organization to be successful.
The solution
Automating responses to predictable challenges is the solution to this problem. Technological advances are allowing organizations to automate more complex tasks than ever before. Tasks that previously had to be performed manually by employees can now be performed by machines. This not only reduces human error, but allows staff to focus on more complex and satisfying tasks and leave the mundane to AI or machine learning models.
In the context of balancing efficiency and resilience, automation can be used to solve predictable challenges, maximizing the efficiency of operations by reducing waste and increasing short-term flexibility. AI can handle processes such as sales and operations planning (S&OP), coordinating different areas of the business to meet customer demand with the right level of supply. Reporting measures such as demand forecast accuracy (DFA), which measures how well a forecast matches actual demand, can also be automated, reducing the burden on team members.
AI chatbots can be used to communicate with key stakeholders and improve an organization's internal and external information flow. These models can examine and analyze massive amounts of data, collecting comprehensive information that can be used in decision making. For example, a chatbot could instantly provide inventory availability and stock plans to partners and customers, as well as the sales team, reducing the need for manual research and back-and-forth email interactions.
Automation assumes regular, uninterrupted processes, which means it is not equipped to deal with irregular events such as a pandemic and its effects. Material shortages, longer transport times and unstable demand have an inevitable impact on entire ecosystems. That's where the human element comes into play.
Technologies such as artificial intelligence and machine learning mean that staff can spend their time predicting unpredictable challenges and use these predictions to create accurate solutions. The number of possible scenarios means that complex models along with human judgment and creativity are needed if an organization is able to devise reactive strategies in advance, allowing it to overcome obstacles on a geopolitical scale. This may mean developing relationships with alternative suppliers, building a stockpile of key components, or working with customers to suggest a staggered delivery schedule in times of upheaval.
Moving forward
Instability is a reality and the modern world will always present us with challenges that seem impossible to predict. The balance between efficiency and resilience is not something we are going to see end anytime soon. Organizations need to innovate and adapt, using automation to meet the daily challenges of optimization, leaving employees free to dedicate their time and resources to predicting the unpredictable. That way, when the next obstacle arises, we'll be prepared.
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