- July 7, 2020
- Posted by: Saddle Point
- Category: Planning and Optimization, Supply Chain Digitization
Jonny was not little anymore. He was the owner of Fastrade, a short term trading company that mainly dealt in perishable goods like fruits and vegetables. In a short span of 10 years, he had grown the company to a USD 100 million topline. Although his net margins were wafer thin at 0.5% to 2%, his cash to cash cycles were extremely short, spanning from a few hours to a few days.
Jonny was getting restless. He was raring to expand. He wanted to start an F&B manufacturing company that manufactured juices from exotic fruits and vegetables. Soon, Jonny had his top of the range manufacturing facility up and running. By using his earlier network, he had quickly set up the distribution channel.
However, Jonny was not happy. Something that he thought will be smooth, started giving him sleepless nights. Most of his distributors started complaining either about not getting supplies on time or of getting supplies that were not moving. There used to be lot of cross depot movements and the dispatch team was always busy putting out fires.
Jonny started getting jittery when his working capital debt started mounting. He was completely at a loss in understanding why his manufacturing company was in such a bad shape when with lesser experience and knowledge, he could quickly build a USD 100 million company.
You see, what Jonny overlooked is the fundamental change in his cash to cash cycles. Now he had to consider his procurement lead time of procuring fruits and vegetables from far flung places, his manufacturing leadtime, his distribution lead time and the shelf time. From a few days in Fastrade, the lead time had now increased to 5-6 months.
Increased lead times threw up a very fundamental challenge of estimating the demand that is 6 months out. F&B products by their nature show lot of geographical variations and seasonal variations and not estimating the future demand properly was creating havoc in the supply chain which finally spilled over to the company.
Fortunately the head of sales pointed this out to Jonny and suggested using a good cloud based forecasting solution to tame this monster. Jonny, who always relied on pure instinct for decision making found it increasingly tough to leave the fate of his company to the drab and boring world of statistical algorithm.
However, good sense prevailed and soon the company implemented a cloud based demand planning solution that used Time Series, Machine Learning and MLR algorithms to accurately forecast the demand. Its hierarchical planning, consensus planning, NPI and promotions planning features also helped in further improving the forecasting accuracy.
Soon, things were back in control. The OTIF increased drastically, stock-outs and inventory write-offs vanished and logistics executives could sleep peacefully. Jonny was back in his Golf course discussing with his buddies about his next big adventure.