Before AI

In a busy factory in the North India region of SFL, plant managers were struggling with a different kind of a problem. It was not about power-cuts. It was not about a machine-breakdown. Not even manpower issues. But something that would always catch them off-guard. There would be days that would come unannounced when the plant would miss a big batch of soft foam or delay a crucial memory-gel order. Due to poor timing and inability to plan ahead, the plant system would fail to crunch the exact number of rolls required for such orders. The inventory team would find itself fighting errors, and delays that would cost a lot- and not just in terms of material or products but also in precious time and retail-impact.

But that was then. Six months back. When the production and inventory teams sat down to brainstorm on this challenge, they also invited the IT team to the table. One suggestion saw quick and excited approval. What if the gaps could be filled through AI? With the real-time eyes and precision-backed ears of AI, the plant would be assured of having the right material, at the right place, and at the right time- always. This would solve a big and deep issue- the problem that even with all the Data in current systems, the plant was unable to utilise it in real-time and failed to translate it into production-level context – that very minute. AI could jump in here. It would equip the plant with visibility, decision-ability and quick reflexes on all the data related to material, supplies, demand and pipelines.

After AI

The table agreed. And they pressed this AI button for dynamic replenishment. What followed was something unprecedented and compelling for business metrics too. The plant acquired:

• The ability to understand customer demand at a granular level of requirements
• The sensibility to contextualise and categorise the demand in various dimensions
• The ultra-fast speed of calculating materials and accessories as per consumption-charts
• The elegant and flawless balance between always-on availability and cost-control
• Surgical inventory minimisation – enriched with optimisation and future-readiness
• Optimal space utilisation with intelligence and Jenga-game-like strategic skills
• The capability to empower and integrate suppliers, plant staff, inventory teams, production resources and everyone else in the chain
• The confidence and never-amiss precision of maintaining the requisite inputs and finished goods
• The dexterity to forecast for short-term adjustments through adaptive moving average
• The acuity to forecast for long-term needs through simple moving average
• Deep and sleek automation of invoicing, procurement and order processing areas
• The super-powers to smell, see, hear and feel all the tiny, but crucial, signs that are hidden inside plant and customer data

Today, the plant almost feels like having super-powers to manage space, needs, materials, resources and deadlines with the help of this AI engine. Not one miscalculation. Not one missed deadline. Not one customer who is not happy and not ordering again. Everything pristine. Accurate. Poised. Subtly working. Sharply. Softly. Intelligently.

SFL factory is busy as ever. Not chasing problems or data black-outs. But chasing new opportunities and a big head-start in material planning.