Just in case more stocks are required to cater to customer demands – For long, this dictum propelled enterprises’ efforts to meet customer needs anytime and every time. Not anymore. Just in time (JIT) now shares that close affinity with supply chain, starting from availability of raw materials for production to shipping of products to customers.
JIT has risen into a smart stratagem to reduce waste and increase efficiency. With JIT, flexibility becomes the antidote for addressing cost volatility. For this to happen, organizations must stay on toes to read demand signals, predict what amount of stocks and raw materials would be just enough to meet customer requirements at any point in time.
What underpins JIT is the need to know several things in advance. It calls for predicting customer demand say for a specific period of time; it is about predicting the optimal level of inventories that need to be maintained for production, predict hiccoughs in the manufacturing line among other things that can impact the supply chain process. The predictions then have to be aptly complemented by prescriptions to guide organizations take the right action at the right time or in real-time to be precise. That leads to encouraging results encompassing ‘No wasted time, materials or efforts’ in the process.
What invigorates organizations is the data available across supply chain. Data flowing from internal and external sources, prevalence of machine learning, advanced analytics gives an inkling as to how organizations can get closer to making predictions across supply chain. Through supply chain analytics, bolstered by advanced analytics and machine learning models, organizations now embrace a reliable approach to unlock capital, drive revenue growth, enhance cash flow and nurture customer smiles.
Where supply meets demand
As we retrace the steps from the day when a product has to be shipped to the day when the product has to be produced to the time when the raw materials need to be acquired, what becomes palpable is that organizations ought to make use of nothing less than accurate predictions to make the JIT concept come true. Then there is only a short lead time to fulfill customer orders.
With ML-algorithms driven demand forecasting, organizations acquire the ability to establish just in time synchronization between demand and supply. Building on top of the datasets acquired from the internal ERP, such as the historical data, ML models leverage other data including external data, weather forecasts, market intelligence and social media for recommending optimal forecasts.
Making the prediction engine drive JIT supply chain efficiency leads the way for scenarios where organizations can make sure that right resources are made available just in time at every step of the supply chain as right products reach customers at the right time to accelerate revenue growth. Giving the near-clockwork-precision twist to the supply chain, organizations ensure that there is no loss of customer sales or there is no overstocking of products at the warehouse.
Where optimum inventory breeds reduced maintenance costs, every time
Excess inventory translates into excessive capital investment. With JIT promulgating the need to maintain optimum stock levels at any given time, machine learning as well as optimization techniques get to work by making effective use of data including purchase orders, production orders and the supplier deliveries to provide stocking recommendations across components, purchase parts as well as finished goods. This in turn helps organizations unlock capital by undoing excessive inventory stocking and in the process set the tone for enhancing cash flow.
Where intelligent fulfillment logistics nurtures customer smiles
The right products and SKUs are out of the production line to meet customer requirements for a specified time window. The perfect finishing touch would come by way of seamless and intelligent fulfillment logistics.
But that intelligent fulfillment logistics becomes a tough ask considering the many factors that can leave their marks on the fulfillment process. Advanced machine learning models come to the rescue of organizations that want to create intelligent fulfillment logistics, made possible by leveraging several unknown and known variables, such as the shipping destination, origin, resources including rail and trucks, service commitments, weather and traffic among other things and processing shipments right on time. That would in turn ensure that the product reaches the customer just in time, nurture customer smiles.
The JIT tightrope walk means that ‘optimum’ balance along the supply chain doesn’t veer off towards overstocking or a missed opportunity. For that to happen, organizations seek to answer so many ‘what lays ahead’ questions along the supply chain and know ‘what to do about those situations’ to ensure JIT drives the supply chain from start to finish.