When are public holidays coming up? How will this affect sales? <\/li><\/ul>\n\n\n\nTo address questions like these, organisations need a system in place that can rapidly analyse terabytes of data to forecast demand in the coming weeks or months. These analyses can inform merchandising operations to ensure the right products are available at the right time to meet customers\u2019 needs. <\/p>\n\n\n\n
Jitendra highlighted a concrete example during the Forum: \u201cStockers are tasked with keeping shelves full of the right products. Stockers need to know: Which product will be popular in the next week? In the case of a grocery store, should they display healthy foods or snack foods? Using Big Data, stockers can take a picture of the shelf, and a mobile app will show them the right answers using a powerful AI based model & API. For example, shelve more sweets and chips in the weeks leading to Christmas. That\u2019s where AI is coming into picture. It\u2019s informing the forecast for each product.\u201d<\/p>\n\n\n\n
Surprise \u2013 this isn\u2019t just for online shopping! <\/h2>\n\n\n\n The above example considers a physical store with physical shelves, because Big Data isn\u2019t limited to online commerce. Big Data has enormous applications for traditional brick-and-mortar stores, as well.<\/p>\n\n\n\n
Jitendra explains, \u201cIn retail we have a habit of thinking that bricks and mortar is going away, and everything is going online. However, the one hurdle is the time that it takes for consumers to get their deliveries. Less than 1% of the world population is getting same-day delivery of items. For the foreseeable future, bricks and mortar isn\u2019t going away. Online is penetrating, but we cannot completely lose focus on physical stores.\u201d <\/p>\n\n\n\n
How do large enterprises, like national grocery store chains, take advantage of these data processes for product forecasting? There\u2019s no single silver bullet. Most multi-national organisations are working with a hybrid cloud solution. It\u2019s not uncommon to see 10 different tools for end-to-end business processes. ERP, CRM, supply chain, data warehousing \u2013 they all have to fit together like Lego blocks to work in harmony.<\/p>\n\n\n\n
Odaseva\u2019s tools provide one component in this landscape, enabling organisations to extract data from Salesforce, get it into a data warehouse, and feed it to an AI system to conduct data modelling on this data. That is a powerful capability which adds a new Big Data aspect onto systems that are already in place without having to alter the parts that are already working well. In this way, we can drive this intelligent sales forecasting without starting from scratch.<\/p>\n\n\n\n
Supply chain \u2013 taming unpredictability <\/h2>\n\n\n\n While sales is about forecasting consumer demand, supply chain is about forecasting the movement and prices of goods. Following the explosion in demand as pandemic public health measures have loosened and the subsequent global supply chain crisis, accurate forecasting has never been more important. Using Big Data, we can apply proactive analysis to our supply chain in an unpredictable business landscape. <\/p>\n\n\n\n
For example: Let\u2019s say I\u2019m a global device manufacturer. I have limited space in my warehouse. Which of my products will move and sell fast? The most important structure I should have in place to answer that question is an intelligent workflow. <\/p>\n\n\n\n
Outside factors will affect the manufacturer\u2019s capability to produce goods, such as the price of raw materials. Over the last 12 months, prices have moved in unpredictable ways, spiking extremely high and dropping very low. If an intelligent workflow that can tell us to buy raw material in the next six months because the price might go up, we are way ahead of the game and, hopefully, a step ahead of our competitors. Big Data can help us predict what\u2019s seemingly unpredictable in both demand and supply. <\/p>\n\n\n\n
How it all fits together \u2013 ROI from one intelligent workflow<\/h2>\n\n\n\n At the end of the Forum, Jitendra highlighted an anonymous customer example: \u201cFor one customer, we implemented Salesforce alongside an intelligent engine. We asked the system: \u2018How much time would it take a frontline employee to stock something in a store?\u2019 The solution showed that each frontline employee would save three hours per week, after the intelligent workflow was implemented. Doing the math, that means that each employee could save 156 hours annually. With 25,000 employees accessing the system, we were able to save the customer 3.9 million man-hours. Even using minimum wage of $20\/hour in our model \u2013- that equates to $78 million in savings per year.\u201d<\/p>\n\n\n\n
The above workflow is based upon a suite of applications of which Salesforce and Odaseva are two components of many. <\/p>\n\n\n\n
If you\u2019d like to find out more, request a demo<\/a> today.<\/p>\n","protected":false},"excerpt":{"rendered":"By Remy Claret, CMO of Odaseva with contributions from Jitendra Zaa, Salesforce CTA, MVP and Associate Partner, IBM There\u2019s little doubt that the currency of the modern world \u2013 the so-called \u201cfifth fuel\u201d \u2013 is data. Data is being captured everywhere, at all times, so that the volumes of global data are constantly multiplying. The question","protected":false},"author":7,"featured_media":9926,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[18],"tags":[],"acf":[],"yoast_head":"\nHow Re-using your Backup Data can Transform the Consumer Goods Industry - Odaseva<\/title>\n \n \n \n \n \n \n \n \n \n \n \n \n\t \n\t \n\t \n \n \n \n \n \n \n \n \n\t \n\t \n\t \n